TW201944429A - Cognitive screens, monitor and cognitive treatments targeting immune-mediated and neuro-degenerative disorders - Google Patents

Abstract

Systems and methods for generating a personalized cognitive treatment recommendation for an individual. The system includes one or more processors; and a memory to store processor-executable instructions. Upon execution of the instructions, the one or more processors receive parameters for at least one cognitive treatment tool; receive physiological data indicative of a condition of the individual, and/or clinical data associated with the individual; and generate the personalized cognitive treatment recommendation based on the physiological data and/or the clinical data. The recommendation includes a specification of (i) at least one first cognitive treatment tool, (ii) at least one second cognitive treatment tool different from the at least one first cognitive treatment tool, or (iii) both (i) and (ii). Optionally, the one or more processors receive performance data indicative of the individual's performance of at least one task associated with the at least one cognitive treatment tool of the recommendation.

Description

Cognitive screening, monitoring, and cognitive processing for immune regulation and neurodegenerative disorders

The present invention relates to the personalization of cognitive processing, such as for immune regulation and neurodegenerative disorders.

There are an increasing number of problems and unmet needs related to baseline assessments, short-term and long-term monitoring, and cognitive processing of individuals with a condition such as an immunomodulatory or neurodegenerative disorder. As the use of new drug treatments addressing the potentially inflammatory and neurodegenerative aspects of these diseases has increased, the problems associated with cognitive impairment of life have become increasingly important. The problems associated with cognitive impairment in life have led to the emergence of orthopedic techniques, which are mainly used in clinical settings and are scarce relative to the number of patients needed.

Provided are devices, systems, and methods for personalizing cognitive processing.

In one aspect, embodiments relate to a system for generating personalized cognitive processing suggestions for one of a group of individuals. The system includes: one or more processors; and a memory for storing processor-executable instructions and communicatively coupled with the one or more processors. After the one or more processors execute the processor-executable instructions, the one or more processors are immediately configured to: a) receive parameters for at least one cognitive processing tool; b) receive instructions for the individual At least one of the physiological data of one condition or the clinical data associated with the individual; and c) generating the personalized cognitive processing proposal based on at least one of the physiological data or the clinical data. The proposal contains a provision for one of: (i) at least one first cognitive processing tool; (ii) at least one second cognitive processing tool different from the at least one first cognitive processing tool; or (iii) (i ) And (ii).

Any aspect of any embodiment may include one or more of the following features. The clinical data can be obtained from at least one patient registration form.

The one or more processors may be further configured to receive performance data indicative of individual performance of at least one task associated with the proposed at least one cognitive processing tool.

The personalized cognitive processing proposal may be further based on the received performance data.

Steps b) and c) are repeated after the individual executes the personalized cognitive processing recommendation, wherein the data received during the repetition of step b) is included in at least one associated with the at least one cognitive processing tool of the recommendation Information collected after the performance of an individual task.

The one or more processors may be further configured to monitor the individual based on at least one of the physiological data, the clinical data, or information indicating that the individual interacts with one of the at least one cognitive monitoring tool. A condition of the condition, wherein the data received during the repetition of step b) includes information indicative of the condition of the condition based on the monitoring.

Generating the personalized cognitive processing recommendation may include using a prediction model that is trained using a plurality of training data sets, each training data set corresponding to one of the previously classified individuals in the plurality of individuals, and each training data The set includes information indicating at least one indicator of the cognitive ability of the classified individual and information indicating a diagnosis of a state or progress of the condition of the classified individual.

The prediction model may include a linear / logistic regression, principal component analysis, a generalized linear mixed model, a random decision forest, a support vector machine, and / or an artificial neural network.

The condition may include multiple sclerosis and / or lupus.

This condition can include dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, autism spectrum disorder, 16p11.2 repetition, and attention deficit hyperactivity disorder , Sensory processing disorder (SPD), mild cognitive impairment, Alzheimer's disease, schizophrenia, depression and / or anxiety.

The one or more processors may be further configured to generate an output that indicates (i) the likelihood of an episode of the condition for the individual, (ii) a period of progression of the condition, or (iii) a combination thereof.

The one or more processors may be further configured to monitor the individual based on an analysis of at least one of the physiological data, the clinical data, or data instructing the individual to interact with one of the at least one cognitive processing tool. One of the conditions.

The at least one cognitive processing tool may include an interference processing tool, a spatial navigation tool, and / or an emotion processing tool.

The proposal may include an interference processing tool, and the one or more processors may be further configured to generate a user interface. Via the user interface, a first instance of a task may appear to have an interference at the user interface, so it is required that one of the first instances of the task from the individual exists in the presence of the interference First response. Through the user interface, the first instance of the task can be presented, and a second response from the individual to the first instance of the task is required from the individual without the interference. At least one of the first instance of the task and the interference may include a computerized element. The first response from the entity to the first instance of the task and the response to the interference from the entity can be measured substantially simultaneously. Information indicating the first response and the second response may be received. The data indicating the first response and the second response may be analyzed to calculate at least one performance metric that includes at least one quantified indicator of the cognitive ability of the individual.

The one or more processors may be configured to present the task as a continuous visual motion tracking task, and the first instance of the task may be a first time interval of the continuous visual motion task. The one or more processors may be configured to present the interference as a target discrimination interference via the user interface.

The proposal may include a space navigation tool, and the one or more processors may be further configured to generate a user interface. Through the user interface, a first task requiring navigation through a prescribed route of an environment can be presented. Via the user interface, a first indicator may be presented, the first indicator being configured to navigate from a starting point in the environment to a target ending point with or without input from the individual. The prescribed route. The user interface may be configured to display to the individual a command to perform a second task, which requires the individual to: (i) navigate one or more of the prescribed routes in reverse, or (ii) at least Navigate again at least a part of the prescribed route. Via the user interface, a second indicator may be presented, the second indicator being configured to navigate in the environment in response to the individual's physical movements to control one of the following: (i) the A relative direction of one of the second indicators, or (ii) a moving speed of one of the second indicators, or (iii) (i) and (ii) both to perform the second task. When performing the second task, measurement data can be obtained by measuring data indicating the physical actions that the individual uses to control the second indicator. The measurement data may be analyzed to generate a performance metric regarding the performance of the second task, the performance metric providing an indication of the cognitive ability of the individual.

Generating the performance metric may include considering a total time spent successfully completing the second task, a number of incorrect turns made by the second indicator, and an incorrect direction of movement made by the second indicator A number and / or a degree of deviation of the route navigated by the user from the prescribed route in the second task.

The suggestion may include an emotion processing tool, and the one or more processors may be further configured to generate a user interface. Via the user interface, a first instance of a task with an interference is presented at the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response and a response from the individual to one of the at least one triggering element. At least one of the first instance of the task and the interference may include the at least one triggering element. The first response from the individual to the first instance of the task and the response from the individual to the at least one priming element can be measured substantially simultaneously, thereby providing the individual with emotional processing capacity under emotional load A measure. Receiving information indicating the first response and the entity's response to the at least one triggering element. The analysis indicates the first response and the data of the response of the individual to the at least one triggering element to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual under emotional load.

The system may include an actuating component, and the one or more processors may be further configured to control the actuating component to generate an auditory stimulus, a tactile stimulus, or a vibrational stimulus, and the triggering element may include the auditory stimulus. At least one of the tactile stimulus or the vibrational stimulus.

The system may include one or more sensor components, wherein the one or more processors are configured to control the one or more sensor components to measure data indicative of individual performance of the task.

The one or more sensor components may include a gyroscope, an accelerometer, a motion sensor, a position sensor, a pressure sensor, an optical sensor, a video camera, and an auditory sense. And / or a vibration sensor.

The system may be at least one of a virtual reality system, an augmented reality system, or a mixed reality system.

In another aspect, the embodiments are directed to a computer-implemented method for generating personalized cognitive processing suggestions for one of a subject. The method includes using one or more processors to execute instructions stored in one or more memory storage devices, the instructions including computer-executable instructions for performing multiple operations. The operations include: receiving parameters for at least one cognitive processing tool; receiving at least one of physiological data indicative of a condition of the individual or clinical data associated with the individual; and based on the physiological data or the clinical data At least one of them generates the personalized cognitive processing proposal. The proposal contains a provision for one of: (i) at least one first cognitive processing tool; (ii) at least one second cognitive processing tool different from the at least one first cognitive processing tool; or (iii) (i ) And (ii).

One or more of the following features may be included. The clinical data can be obtained from at least one patient registration form.

The operations may further include: receiving performance data indicative of individual performance of at least one task associated with the proposed at least one cognitive processing tool.

The personalized cognitive processing proposal may be further based on the received performance data.

The operations may further include repeating steps b) and c) after the individual executes the personalized cognitive processing recommendation, wherein the information received during the repetition of step b) is included in the at least one of the recommendations Cognitive processing data collected after the performance of individuals associated with at least one task.

The operations may further include monitoring a state of the condition of the individual based on at least one of the physiological data, the clinical data, or data indicating that the individual interacts with one of the at least one cognitive monitoring tool; Wherein the data received during the repetition of step b) includes data indicative of the state of the condition based on the monitoring.

Generating the personalized cognitive processing recommendation may include using a prediction model that is trained using a plurality of training data sets, each training data set corresponding to one of the previously classified individuals in the plurality of individuals, and each training data The set includes information indicating at least one indicator of the cognitive ability of the classified individual and information indicating a diagnosis of a state or progress of the condition of the classified individual.

The prediction model may include a linear / logistic regression, principal component analysis, a generalized linear mixed model, a random decision forest, a support vector machine, and / or an artificial neural network.

The condition may include multiple sclerosis and / or lupus.

This condition can include dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, autism spectrum disorder, 16p11.2 repetition, and attention deficit hyperactivity disorder , Sensory processing disorder (SPD), mild cognitive impairment, Alzheimer's disease, schizophrenia, depression and / or anxiety.

The operations may further include generating an output that indicates (i) the likelihood of an episode of the condition for the individual, (ii) a period of progression of the condition, and / or (iii) a combination thereof.

The operations may further include monitoring a state of the condition of the individual based on an analysis of at least one of the physiological data, the clinical data, or data instructing the individual to interact with one of the at least one cognitive processing tool. .

The at least one cognitive processing tool may include at least one of an interference processing tool, a spatial navigation tool, or an emotion processing tool.

The proposal may include an interference processing tool, and the one or more processors may be further configured to generate a user interface. Via the user interface, a first instance of a task may appear to have an interference at the user interface, so it is required that one of the first instances of the task from the individual exists in the presence of the interference First response. Through the user interface, the first instance of the task can be presented, and a second response from the individual to the first instance of the task is required from the individual without the interference. At least one of the first instance of the task and the interference may include a computerized element. The first response from the entity to the first instance of the task and the response to the interference from the entity can be measured substantially simultaneously. Information indicating the first response and the second response may be received. The data indicating the first response and the second response may be analyzed to calculate at least one performance metric that includes at least one quantified indicator of the cognitive ability of the individual.

The task may be presented as a continuous visual motion tracking task, and the first instance of the task may be a first time interval of the continuous visual motion task.

This interference can be presented as a target discrimination interference.

The proposal may include a space navigation tool, and the one or more processors may be further configured to generate a user interface. Through the user interface, a first task requiring navigation through a prescribed route of an environment can be presented. Via the user interface, a first indicator may be presented, the first indicator being configured to navigate from a starting point in the environment to a target ending point with or without input from the individual. The prescribed route. The user interface may be configured to display to the individual a command to perform a second task, which requires the individual to: (i) navigate one or more of the prescribed routes in reverse, or (ii) at least Navigate again at least a part of the prescribed route. Via the user interface, a second indicator may be presented, the second indicator being configured to navigate in the environment in response to the individual's physical movements to control one of the following: (i) the A relative direction of one of the second indicators, or (ii) a moving speed of one of the second indicators, or (iii) (i) and (ii) both to perform the second task. When performing the second task, measurement data can be obtained by measuring data indicating the physical actions that the individual uses to control the second indicator. The measurement data may be analyzed to generate a performance metric regarding the performance of the second task, the performance metric providing an indication of the cognitive ability of the individual.

Generating the performance metric may include considering a total time spent successfully completing the second task, a number of incorrect turns made by the second indicator, and an incorrect direction of movement made by the second indicator A number and / or a degree of deviation of the route navigated by the user from the prescribed route in the second task.

The suggestion may include an emotion processing tool, and the one or more processors may be further configured to generate a user interface. Via the user interface, a first instance of a task with an interference is presented at the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response and a response from the individual to one of the at least one triggering element. At least one of the first instance of the task and the interference may include the at least one triggering element. The first response from the individual to the first instance of the task and the response from the individual to the at least one priming element can be measured substantially simultaneously, thereby providing the individual with emotional processing capacity under emotional load A measure. Receiving information indicating the first response and the entity's response to the at least one triggering element. The analysis indicates the first response and the data of the response of the individual to the at least one triggering element to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual under emotional load.

The operations may further include: controlling the concertina to generate an auditory stimulus, a tactile stimulus, or a vibrational stimulus, and wherein the triggering element includes at least one of the auditory stimulus, the tactile stimulus, or the vibrational stimulus.

The operations may further include controlling one or more sensor components to measure data indicative of the performance of the individual for the task.

The one or more sensor components may include a gyroscope, an accelerometer, a motion sensor, a position sensor, a pressure sensor, an optical sensor, a video camera, and an auditory sense. And / or a vibration sensor.

Cross-reference to related applications

This application claims that the application filed on March 4, 2018 was titled `` COGNITIVE SCREENS, MONITOR AND COGNITIVE TREATMENTS TARGETING IMMUNE-MEDIATED AND NEURO-DEGENERATIVE DISORDERS) "priority and rights in US Provisional Application No. 62 / 638,299, the entire disclosure of this US provisional application (including drawings) is incorporated herein.

It should be understood that all combinations of the concepts discussed in more detail below (assuming these concepts are not contradictory) are included as part of the inventive subject matter disclosed herein. It should also be understood that terminology explicitly used herein that may also appear in any disclosure incorporated by reference should be given one of the meanings most consistent with the particular concept disclosed herein.

The following is a more detailed description of various concepts related to inventive methods, devices, and systems, and embodiments of inventive methods, devices, and systems, which include a cognitive platform and / or platform products, The cognitive platform and / or platform product is configured to couple with one or more other types of measurement components, and is used to analyze at least one interaction with the user of the cognitive platform and / or at least one or more other types of components Data collected by a measurement. As a non-limiting example, cognitive platforms and / or platform products may be configured for cognitive training and / or for clinical purposes. Exemplary systems, methods, and devices are suitable for monitoring and / or cognitive processing of an individual with an immunomodulatory or neurodegenerative disorder.

In an exemplary embodiment, the cognitive platform may be integrated with one or more physiological or monitoring components and / or cognitive test components.

In another exemplary embodiment, the cognitive platform may be independent of one or more physiological or monitoring components and / or cognitive test components and configured to interact with one or more physiological or monitoring components and / or cognitive test components coupling.

In any of the examples herein, the cognitive platform and systems including the cognitive platform may be configured to present computerized tasks and platform interactions that inform cognitive assessment (including screening and / or monitoring), and / or deliver cognitive processing.

In any of the examples in this article, the platform product in this article can be formed as one of the AKILI® platform products of Akili Interactive Labs (Boston, Massachusetts), based on the AKILI® platform product, or integrated with the AKILI® platform product, the AKILI® platform products are configured to present computerized tasks and platform interactions that inform cognitive assessments (including screening and / or monitoring), or deliver cognitive processing.

It should be understood that the various concepts introduced above and discussed in more detail below can be implemented in any of a number of ways, as the concepts disclosed are not limited to any particular implementation. Examples of specific implementations and applications are provided primarily for illustrative purposes. Exemplary methods, devices, and systems including cognitive platforms or platform products can be used by an individual, a clinician, a resident physician, and / or other practitioners or health practitioners to provide an assessment and / or screening of one of the individuals , Monitoring and processing information.

The present invention relates to an increasing number of issues and unmet needs related to baseline assessment, short-term and long-term monitoring, and cognitive processing of individuals with immunomodulatory or neurodegenerative disorders. There are relatively specific diagnostic methods and population sub-classification techniques for multiple classes of disorders (such as, but not limited to, one of progressive multiple sclerosis (primary or secondary) relative to relapsing-remitting multiple sclerosis). Cognitive insufficiency is considered to be a symbiotic disorder of immunomodulation or neurodegenerative disorders, and there is no sub-classification based on the cognitive profile of patients with the same immunomodulation or neurodegenerative disorders, resulting in cognitive impairment due to different reasons The risk of inadequate corrective treatment for some patients. As an example, two patients with multiple sclerosis who have been diagnosed with the same form of disease (relapsing-remitting multiple sclerosis) and who have received similar or comparable anti-inflammatory treatment may suffer completely different forms of cognitive impairment. It is because underlying brain disorders (e.g., lesions, minor lesions, and other microstructural or functional changes) can have different natures and can affect different locations or networks of the central nervous system (grey or white matter).

For example, individuals diagnosed with multiple sclerosis may have different types of cognitive profiles depending on the location, nature, and size of the lesion in the central nervous system. Because of these heteromorphic disease patterns, cognitive processes of the same type and / or sequence that are effective when dealing with cognitive dysfunction identified in the cognitive profile of a first individual with an immunomodulatory or neurodegenerative disorder are useful for diagnosis as A second individual with one of the same disorders can be ineffective.

In a non-limiting example, methods, devices, and systems that include a cognitive platform or platform product can be used to determine a personalized cognitive processing scheme for an individual with a condition, such as, but not limited to, an immunomodulatory or neurodegenerative disorder, And / or as a tool to assist in monitoring the progress of the individual when the individual interacts with the cognitive platform according to a personalized cognitive processing scheme. Example tools can be constructed and trained using one or more training data sets obtained from an individual with a known condition, such as, but not limited to, an immunomodulatory or neurodegenerative disorder, including but not limited to utilizing and supporting The vector machine's deep learning-related classifier tool finds patterns associated with information provided by clinicians via a patient registration form, such as magnetic resonance imaging written or oral reports integrated using natural language processing.

As used herein, the term "includes" means including but not limited to, and the term "including" means including but not limited to. The term "based on" means based at least in part.

Exemplary platform products and cognitive platforms based on the principles described herein are applicable to many different types of conditions, including immune regulation and neurodegenerative disorders such as, but not limited to, multiple sclerosis and lupus.

Example systems based on the principles described herein can be applied to many other types of conditions, including neuropsychological conditions such as, but not limited to, dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease or other neurodegenerative conditions, autism spectrum disorder (ASD), 16p11.2 repetition, and / or an executive dysfunction (such as, but not limited to, attention deficit hyperactivity disorder (ADHD), sensory Handling disorders (SPD), mild cognitive impairment (MCI), Alzheimer's disease, multiple sclerosis, schizophrenia, depression or anxiety).

The present invention is directed to computer-implemented devices formed as example cognitive platforms or platform products that are configured to implement software and / or other processor-executable instructions for implementing example closures. Loop system purpose. In one example, the closed loop system can be configured to adapt the digital cognitive processing recommendations of each individual patient to personalize the cognitive processing scheme based on the actual nature of the cognitive impairment associated with each individual patient. During the implementation of the treatment, the cognitive treatment plan can also be based on information provided by the individual, a clinician, a resident physician, and / or other practitioners or health practitioners, as well as individual perceptions caused by the course of the disease or by the current physiological state And / or adjust for feeling inadequate. Such physiological states may include fatigue / sleepiness / alertness, or other information obtained through assessments made by the device or self-reported by the patient. Adjustments can also be made based on device control / interaction methods, where non-limiting examples of such inputs include relative to a user interface or image capture device such as, but not limited to, a touch screen or other pressure sensitive screen, Or a camera), the user interface or image capture device includes any form of graphical user interface configured to record a user interaction, a pointing device 320 ( e.g., (A mouse), a camera or other image recording device, a microphone or other sound recording device, an accelerometer, a gyroscope, or a sensor for one of tactile, vibration, or auditory signals.

In one example, the system is configured to implement a set of algorithms and associated methods based on a first set of mathematical algorithms (trained monitoring component 52) and is implemented on a computing device such as, but not limited to, a digital intelligence Device) to produce multiple metrics related to cognitive and physiological performance, such as, but not limited to, response time or targeting ability. This monitoring period distinguishes the specific needs of an individual patient based on its distinct pathophysiological context associated with the cognitive impairment of the individual patient.

Figure 1 shows a non-limiting example of a closed loop system. The non-limiting exemplary system of FIG. 1 includes a closed loop system 10 and a closed loop system 50. The closed-loop system 10 is implemented via an interface 12, which is used by individuals in several closed-loop iterations 16 to interact with a cognitive processing engine 14. The closed-loop system 50 generates individual-targeted individuals via one of the monitoring components 52 for assessing and / or monitoring one of the states of the individual and at least one closed-loop iteration 56 between the monitoring component 52 and the processing generating component 54 based on the data. One of the personalized cognitive processing suggestions is implemented by a processing generating component 54. In one example, there are multiple iterations between the interface 12 and the cognitive processing engine 14 to continuously adjust one of the difficulty levels of the tasks presented at the interface 12.

The closed loop system 10 may be any one or more of the cognitive tools described herein. The closed loop system 10 is configured such that the interface 12 presents one or more tasks generated by the cognitive processing engine 14 to the individual and / or presents one or more questions or informational materials to the individual. In a non-limiting example, cognitive processing engine 14 may be configured to present tasks to individuals at interface 12 to perform and interfere with processing and / or spatial navigation and / or emotional processing and / or any other type of applicable cognitive tool ( Contains one or more tasks associated with the cognitive tools described in this article). The interface is also configured to measure data that indicates the interaction of one or more entities while performing one or more tasks, and / or collect other data that indicates the performance and / or status of the individual. The cognitive processing engine 14 is also configured to analyze data measured and / or collected at the interface 12 to generate an indication of an individual's cognitive ability. In one example, data is collected and analyzed to assess the status of one of the individuals. In another example, the cognitive processing engine 14 is also configured to adjust the level of difficulty of at least one of the tasks presented at the interface 12, and the analysis of the measurements and / or collected data at the interface 12 can be used for Provides an indication of a change in one's cognitive ability. In any of the exemplary implementations of the closed-loop system 10, the cognitive processing engine 14 may also be configured to analyze responses to one or more questions presented to the individual at the interface 12 and measured and / or collected Data to generate indicators of individual cognitive ability.

As shown in FIG. 1, the closed-loop system 10 is configured to implement a number of closed-loop iterations 16 to adapt the interface 12 based on the use of the cognitive processing engine 14 to measure and / or collect data on the interface 12 The level presents one level of difficulty for one or more tasks. In either instance, the adaptation can be achieved using a step approach.

In one example, the interface 12 may be configured as a graphical user interface presented at a display location to present one or more tasks. In another example, the interface 12 may be configured to present one or more tasks based on auditory, vibration, and / or tactile signals. For example, the interface 12 may include at least one actuator, haptic unit, or vibration unit, or be used to present one or more tasks of a cognitive tool and to measure and / or otherwise collect indications that an individual is interacting with an or Other similar components of physical or other movement data when multiple tasks interact. In another example, the interface 12 may be configured to measure metrics and / or otherwise collect instructions that indicate an individual's physical or other actions when interacting with one or more tasks using at least one camera or other image capture device. data.

The closed loop system 50 is configured to receive data 20 output from the closed loop system 10. In the non-limiting example of FIG. 1, the monitoring component 52 is configured to receive data 20 output from the closed-loop system 10. The monitoring component 52 is configured to evaluate and / or monitor a state of an individual based at least in part on an analysis of one of the data 20. The closed loop system 50 may also take as input the data collected from the questions presented to the individual, which are information about the individual's condition, the individual's mood and emotional state, mobility, and a self-care provider (HCP) Or other clinical information about the individual obtained by the practitioner 58 (which may include associated nData), from one or more physiological measurement equipment and laboratories (such as, but not limited to, magnetic resonance imaging (MRI), heart rate monitors, thermometers Etc.) of nData. As a non-limiting example of nData from a physiological measurement performed using an MRI is the type, location, and distribution of one of the lesions in several regions of the brain of an individual with multiple sclerosis. For example, nData may contain data indicating a region of the brain of an individual with a lesion, such as, but not limited to, the prefrontal cortex, hippocampal network, amygdala, caudate nucleus region of the brain, or the brain Inside the olfactory cortical area. In an example, the monitoring component 52 is configured to receive its input data and analyze the input data to construct one of the entities based on the indication of the cognitive capabilities received from the closed loop system 10 and other nData received for the entity. Overview. Clinical information may include clinical measures of an individual's symptoms and nData indicating the individual's physical condition, clinician diagnosis based on the clinical measures and nData, and other data.

In one example, the monitoring component 52 may also receive clinical data from one or more patient registration forms 60. A patient registration form 60 may receive de-identified data from a hospital or other health network. The patient registration form can be used to provide information indicating physiological measurements of multiple individuals and their disease conditions, including trend data showing the level of biomarkers for individuals and their disease conditions, such as, but not limited to, inadequate levels, disease progression data And MRI scans, measures of patient mobility, visual acuity, and cognitive ability. The patient registration form 60 contains information from multiple patients with markings and progress that can be related to some of the information entered into the monitoring module 52.

The monitoring component 52 may be configured as a predictive model based on training computing techniques and machine learning tools using a previously classified training input data set collected from an individual on the progress or degree of progress of a known condition of interest. As used herein, the term "predictive model" encompasses models trained and developed based on models that provide continuous output values and / or models based on discrete labels. In any of the examples herein, the prediction model includes a classifier model. Non-limiting examples of computing techniques and machine learning tools may include, but are not limited to, linear / logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, or artificial neural networks.

The example closed loop system 50 may be configured to apply the predictive model of the monitoring component 52 to the output 20 from the closed loop system 10 using trained computing techniques and machine learning tools, and / or instruct the individual to perform a cognitive monitoring Received data in response to one or more other tasks of the tool, and / or data from one or more physiological measures, and / or clinical data to generate an output 55. In an example, the predictive model may be configured to generate an output 55, such as, but not limited to, an individual profile, including an indication of the severity of one of the conditions, an advanced phase of the condition, an assessment of the individual's cognitive health, A measure of an individual's performance while performing at least one task of a cognitive tool, a state or a change in an individual's motor function or cognitive condition, an instruction to the individual to follow a type of drug, biological, pharmaceutical preparation, or other treatment protocol and / Or dosage information. A cognitive monitoring tool may be any cognitive tool that provides information indicating the state of an individual's cognitive ability at a given point in time, without applying a treatment or enhancement of the individual's cognition. As a non-limiting example, a cognitive monitoring tool may be a cognitive tool that is configured to present one or more tasks to be performed by an individual with little or no difficulty. In one example, the cognitive monitoring tool may be configured to present one or more tasks involving interference processing to an individual with little or no adjustment to the difficulty level. In any of the examples herein, a cognitive processing tool can be configured to act as a cognitive monitoring tool to evaluate a person's cognitive capabilities in the context of application processing.

An example system, method, and device according to one of the principles herein may be configured to be based on a plurality of individuals based on interactions between the plurality of individuals and one or more tasks presented by the cognitive processing engine using the closed loop system 10 The measured data 20 and the training monitoring component 52. In this example, the training data set contains previously categorized data measured from the individual regarding one of the indicators of cognitive ability and the physical condition. For example, the monitoring component 52 may be trained using a plurality of training data sets, where each training data set is associated with a previously classified individual from one of the body groups. Each of the training data sets includes data indicating one or more parameters that indicate the performance of the classified individual at the task presented using one or more cognitive tools, the performance based on the classified individual Interaction with one of the example devices, systems, or computing devices described herein. The example monitoring component 52 may also provide information indicating the performance of the classified individual at a cognitive test and / or a behavioral test and / or indicate a cognitive condition, a disease, or a disorder (including an executive function) of the classified individual. Disturbance) is considered as an input or a diagnosis of progression is considered.

In one example, the monitoring component 52 may include a cognitive monitoring tool that monitors a cognitive condition of an individual. As a non-limiting example, the cognitive tool may be configured to present the individual with an interference processing task (with little or no adaptation to the difficulty level of the task) such that the cognitive tool provides an assessment of the individual's cognitive ability without providing processing.

In any of the examples herein, the example trained monitoring component 52 can be used as one of the quantifiable assessments of a person's cognitive ability and / or individual disease or condition state (e.g., extent and location of brain lesions). Smart agent. That is, once a monitoring component 52 is trained, the output of the monitoring component 52 can be used to provide an indication of the cognitive abilities of multiple individuals without using a physiological metric or another cognitive or behavioral assessment test. In one example, the trained monitoring component 52 may be used as a smart agent to provide an indication of the likelihood of a condition or the progression of a condition for an individual. In one example, the trained monitoring component 52 can be used as a smart agent for subsequent measures of an individual's condition. For example, as shown in Figure 1, performance metrics and / or changes in disease or condition may be communicated to one of the interested individuals, the HCP or other practitioner (with the consent of the individual).

In one example, the trained monitoring component 52 may be used as a biomarker for certain clinical conditions of an individual based on a measure of data from the individual's interaction with one or more cognitive tools.

The closed loop system 50 also includes a personalized processing component 54 to receive output 55 from the monitoring component 52 and clinical data from one or more patient registration forms 60 to generate a personalized cognitive processing recommendation (PCTR) for one of the individuals and PCTR (shown at element 62 in FIG. 1) is transmitted to the closed loop system 10. The patient registration form contains images from patients who have previously performed scans to measure their nData, which includes scans of brain lesions, and other measures of the individual's clinical and physical conditions. Using the data received from the self-monitoring module 52 and other data, such as, but not limited to, data from one or more patient registration forms 60, the personalization processing module 54 generates a PCTR. In a non-limiting example, the PCTR specifies an output of one of the individual's treatment schemes, which includes a recommendation for the percentage of time and the treatment level (A _i ) that the individual should perform on one or more cognitive tools or other engines. PCTR can provide the use of only a single cognitive tool (A _i = A ₁ ), two types of cognitive tools (A _i = A ₁ , A ₂ ), and three types of cognitive tools (A _i = A ₁ , A ₂ , A ₃ ) or one of the more cognitive tools suggested. For example, the PCTR may specify A ₁ %-a first cognitive tool, A ₂ %-a second cognitive tool, A ₃ %-a third cognitive tool, and the like. In the case of a value A _i = 0, the PCTR will indicate that the cognitive tool is not used as part of the cognitive tool processing scheme for the individual. As shown in FIG. 1, the closed-loop system 50 may be configured to generate an iterative process (closed-loop iteration 56) between the monitoring component 52 and the personalized processing component 54 such that the generated by the personalized processing component 54 The PCTR output for an individual is also transmitted to the monitoring component 52 to further train and improve the monitoring component 52 for improving the monitoring of the condition and progress of the individual and other individuals of interest.

Based on the transmitted PCTR (as shown at element 62 in Figure 1), the closed loop system 10 may be configured to generate a PCTR. For example, based on the provisions of each PCTR cognitive tools of different types of A _i, the cognitive processing engine 14 may cause the closed-loop system 10 exhibits a particular cognitive one or more tasks associated with the tool and at the interface 12 , The presentation is performed within a specified duration and at a specified level of intensity or an appropriate amount of difficulty for one or more of the tasks presented.

As a non-limiting example, the PCTR may include provisions regarding the duration of a scheme using a cognitive tool, the intensity of a processing session (including the adjustment of difficulty levels), and the type of cognitive tool to be used in the process.

The personalization processing component 54 may use training input data sets previously collected from individuals that are categorized on the progress or degree of progression of a known medical condition of interest and known measurements and scores from one or more cognitive tools. Technology and machine learning tools are configured as a predictive model. Non-limiting examples of computing techniques and machine learning tools may include, but are not limited to, linear / logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, or artificial neural networks.

The example closed loop system 50 may be configured to apply the prediction model of the personalized processing component 54 to the output data 55 using trained computing techniques and machine learning tools, and / or data from one or more physiological metrics, And / or clinical data to generate output data 62. In an example, the predictive model may be configured to generate output data 62, such as, but not limited to, PCTR, data indicating the onset of one of the conditions, one of the progression stages of the condition, an assessment of the cognitive health of the individual, the individual Information on a state or a change in motor function or cognitive condition that instructs an individual to follow a type and / or dose of a drug, biological, pharmaceutical preparation, or other treatment regimen.

An example system, method, and device according to one of the principles herein may be configured to train a personalized processing component 54 based on an output 55 of de-identified data from the monitoring of a plurality of individuals. In this example, the training data set includes monitoring a previously classified data output generated from the individual regarding one of the indicators of cognitive ability and the physiological condition. For example, the personalization processing component 54 may be trained using a plurality of training data sets, where each training data set is associated with a previously classified individual from one of the body groups. Each of the training data sets includes data indicating one or more parameters indicating the monitored condition of the classified individual. The example personalization processing component 54 may also provide information indicating the performance of the classified individual at a cognitive test and / or an behavioral test and / or indicate a cognitive condition, a disease, or a disorder (including a (Executive dysfunction) is considered as input, as is the likelihood of a seizure or a diagnosis of progression.

In any of the examples herein, the example trained personalized processing component 54 can be used as a quantifiable assessment of a person's cognitive ability and / or individual disease or condition state (e.g., extent and location of brain lesions) One of the smart agents. That is, once a personalized processing component 54 is trained, the output of the personalized processing component 54 can be used to provide an indication of the type of processing scheme without using a physiological measure or another cognitive or behavioral assessment test. Protocols can provide treatment of cognitive conditions in one individual. In one example, the trained personalization processing component 54 can be used as a smart agent to provide an indication of the possibility of a change in the progression of a condition. In one example, the PCTR for one individual may be communicated to an HCP or other medical practitioner (with individual consent) for one of the concerned individuals.

Personalized processing component 54 training based on machine learning or other computing techniques may use scores that indicate an individual's cognitive abilities (made using a cognitive tool and other aspects (e.g., information from other patients or HCP)), and Information from patients who do not interact with cognitive tools and their disease progression can be used to help train the personalized processing component 54 to generate a personalized treatment (PCTR). Based on data from a plurality of individuals (eg, from thousands of other patients), the monitoring component 52 and / or the personalization processing component 54 may be trained as disease markers. For example, the monitoring component 52 and / or the personalization processing component 54 may be trained to predict a disease level of an individual based on the score of the individual using a cognitive tool, based on the individual's profile, duration of the program, intensity, potential The type of area affected by a disease condition (eg, multiple sclerosis lesions) provides a prediction of one's cognitive state and potential efficacy.

In one example, the monitoring component 52 may be trained to analyze a body's feelings and athletic capabilities, and monitor the extent to which these capabilities are affected by the course of an individual's disease condition. The output data 55 from the monitoring component 52 may include performance metrics transmitted to an HCP or other practitioner as an alert to the condition of the individual. As an example, the output from the monitoring component 52 may be transmitted as the subject being monitored may not benefit from a prescribed drug plan and / or a cognitive tool plan or an individual being monitored based on the use of the prescribed drug plan And / or a warning that the cognitive tool solution being developed may be improving satisfactorily or at a faster rate than expected.

The performance metric from the monitoring component 52 can be used as an agent marker and / or biomarker for an individual's condition to trigger a schedule for a visit to the HCP or a medical practitioner, or to cause a reassessment or change to a drug prescribed to the patient Program. For example, if the monitoring component 52 provides an output 55 indicative of a change in an individual's motor function or cognitive condition, the HCP or practitioner can determine whether to increase a dose of a medication to take care of a condition that is worsening or improving .

As a non-limiting example, the type of tool specified in the PCTR output from the personalized processing component 54 and the proportion / percentage of interactions suggested for a particular cognitive tool may depend on the brain of a body determined to be affected by a lesion Areas and / or types of cognitive abilities identified as affected individuals. For example, the PCTR may specify a percentage amount (including the duration of interaction) of an individual with a cognitive tool performing interference processing tasks, wherein the percentage amount is determined based on the following: the lesion may be positively affecting the prefrontal cortex, or Whether nData located in or near the prefrontal cortex, and / or cData or nData, indicate that cognitive abilities (such as, but not limited to, working memory) and / or executive functions are affected. As another example, the PCTR may specify a percentage amount (including the duration of interaction) of an individual and a cognitive tool that performs a task involving spatial navigation and / or episodic memory, where the percentage amount is determined based on the following: Can positively affect the brain's extended hippocampal network, caudate nucleus, or entorhinal cortex, or located in or near the brain's extended hippocampal network, caudate nucleus, or entorhinal cortex, Whether nData of the caudate nucleus or entorhinal cortex area and / or cData or nData indicate cognitive ability (such as but not limited to working memory, spatial memory), motion control and / or executive function are affected. As another example, the PCTR may specify a percentage amount (including the duration of interaction) of an individual with a cognitive tool that performs a task involving emotional processing, where the percentage amount is determined based on the following: the lesion can positively affect amygdala dependence Whether the nData of the amygdala-dependent network, or the amygdala-dependent network, and / or cData or nData indicate cognitive abilities (such as, but not limited to, working memory), mood, and depression are affected .

In one example, the output data 62 from the processing personalization component 54 may be transmitted to an HCP or other medical practitioner as an alert to the condition of the individual. As an example, the output data 62 (including PCTR) from the processing personalization component 54 may be transmitted as an individual being monitored that may not have benefited from a drug program and / or a cognitive tool program or being monitored. A warning that an individual may be improving satisfactorily or at a faster rate than expected based on the use of the prescribed drug regimen and / or the prescribed cognitive tool regimen.

In another example, PCTR may specify different combinations of several types of cognitive tools in a treatment regimen based on new lesions in different regions of the brain. The goals of PCTR can be defined as a desired level of cognitive ability and treatment plan for individuals, including expected persistence and compliance with the use of cognitive tools. For example, cognitive tools that perform emotional processing tasks can be used to provide an increased work session to an individual with major depression to resolve depression and possibly increase compliance with the processing of cognitive tools that address other cognitive conditions .

In some examples, PCTR may include prescribed exposures and durations of cognitive tools, such as psychological education (textbooks on disease processes and response skills), mindfulness (including meditation, focus on breathing, relaxation, etc.) or cognitive behavioral therapy. As an example, the mindfulness portion of the PCTR program can be used as part of a program to reduce anxiety or depression to help improve adherence and compliance with other cognitive tools. In addition, mindfulness exercises can help reduce fatigue and thereby reduce the amount of time it takes an individual to use other cognitive tools in the PCTR program.

The monitoring component 52 (monitoring) enables the assessment of cognition in a way that is comparable across subjects based on the task being performed, and enables the evolution of personalized treatment agreements and certain clinical cognition assessments of cognitive impairment or disease activity and / or Biomarkers (e.g., predictions of an exacerbation event or progression of disease severity or possibly one of the treatments) are associated. Here, a health care provider can measure cognitive function and pathophysiology during a health care visit. The input of the monitoring component 52 may be a performance metric generated by a patient when performing a mission, as explained below. Another input for the monitoring component 52 may include information about mental health obtained by the patient's clinician or other health care provider, such as using performance-based instruments and clinical interviews and / or using the gold standard (where applicable) Cognition and depressive symptoms obtained from pathophysiology, such as brain lesion load based on MRI or immune status based on tissue sampling techniques. In some instances, performance-based instruments can be verified. One non-limiting example input from a clinician or other health care provider about systemic lupus erythematosus (SLE) may include SLE disease activity index (SLEDAI) physiological markers and SLE neuropsychiatric symptoms (SLE-NP) checklist . Another input of the monitoring component 52 may be health care provider pharmaceutical, biological or other treatment information (eg, disease modification therapy options and protocols, glucocorticoids, etc.). One of the outputs of the monitoring component 52 may be a personalized cognitive profile based on performance on a personalized processing agreement and / or health care provider assessment (verified performance-based instrumentation and clinical interviews). Another output of the monitoring component 52 may be a personalized sensory motor profile that includes perceptual motor capabilities (e.g., visual and auditory acuity / sensitivity, and dexterity). In addition to information related to the drug, organism, or any other treatment protocol followed by the patient, another output of the monitoring module 52 may be related to the profile of pathophysiological information (i.e., clinical data) from the health care provider Evolution of performance on personalized mission agreements, such as the gold standard (where applicable), such as brain disease burden based on magnetic resonance imaging or immune status based on tissue sampling techniques. Another output of the monitoring component 52 may go to a health care provider in the form of performance metrics and / or alerts (if certain pre-established performance thresholds are reached) and prompt a clinical reassessment (for example, with neurodegeneration Related to an impending recurrence or change in the disease period).

The output of the monitoring component 52 can be used by another set of algorithms and associated methods based on a second set of mathematical algorithms (trained to process the personalized component 54) to determine and adapt therapeutic interventions to provide a personalized recommendation, from adjustments The nature of cognitive intervention for the actual cognitive profile of each patient. This recommendation is used as a Personalized Cognitive Processing Recommendation (PCTR).

In particular, the processing personalization component 54 (processing personalization) may include several inputs, including the output of the monitoring component 52, such as clinical information from a health care provider, patient self-reporting results, such as fatigue, perception Experience of insufficient cognitive, emotional / emotional state. Yet another input may be a physiological metric (e.g.,% eye opening, sound, activity recorder tag) obtained by a computing device and a graphical user interface and / or other I / O devices for receiving input from a user Etc.), I / O devices such as a keyboard or any suitable multi-touch interface 318, a pointing device 320 ( for example, a mouse), a camera or other image recording device, a microphone or other sound recording device, an acceleration A gyroscope, a sensor for one of the tactile, vibratory, or auditory signals, and / or at least one actuator.

Yet another input for processing the personalized component 54 may be a personalized cognitive profile associated with the patient's measured cognitive impairment, including, but not limited to, processing speed and attention to alert and orientation, and executive control. Yet another input for processing the personalized component 54 may be a personalized sensorimotor profile that includes one of perceptual motor capabilities (eg, visual and auditory acuity / sensitivity, and dexterity). Finally, another input can come from the patient registration form, including neuropsychological, cognitive, pathophysiological information, treatment options, and disease progression information.

The PCTR generated by the processing personalization component 54 may include one or more of the following:
Depending on the condition of one of the prefrontal cortex, A of cognitive control and attention₁ % Interference processing; Depending on the condition of one of the expanded hippocampal networks, A₂ % Space navigation and episodic memory composition; Depending on the condition of one of the amygdala-dependent networks, A₃ % Emotion assessment and regulation; As needed, A₄ % Other engines, such as working memory and other executive functions, such as cognitive flexibility; and A₅ % Psychological education, mindfulness, cognitive behavior training, and other

For example, a patient with relapsing-remitting multiple sclerosis that exhibits a disease load that is primarily manifested in the prefrontal cortex and one of the lower levels of the extended hippocampal network (but not elsewhere) is acceptable to include 60% PCTR is one of the interference processing and 40% space navigation and episodic memory.

Personalized processing algorithms used in the training of processing the personalized component 54 may be based on, for example, physiology (MRI-brain lesion localization), cognitive performance profile, and supplementation for perceptual or sensorimotor capabilities and emotional states, respectively Performance-based quiz or questionnaire. The processing personalized component 54 can also take advantage of clinical symptoms, including one of the core mechanism assignments and processing adaptability (including duration) based on the level of each individual participating in digital processing, that is, the individual's continuous effort, beyond simple tasks time.

The closed loop 1 iteration 56 includes periodic iterations that provide output from the monitoring component 52 and other factors described above as input to the processing personalization component 54. As used herein, "periodic iterations" can be repeated at regular time intervals or at irregular time intervals. For example, the closed-loop system 50 may be configured to start at regular time intervals or irregularly (e.g., based on input of clinical data from an HCP or practitioner), or based on the monitoring component 52 for individual disease Status or performance measure starts with an indication of a threshold change in one of the monitored parameters) Execution of input data from the monitoring component 52 to the personalization processing component 54 or PCTR output from the personalization processing component 54 to the monitoring component 52 Repeatedly in exchange. One of the benefits of implementing this closed loop 1 is to quickly adjust personalized cognitive processing recommendations based on incoming updates to cognitive, pathophysiology, or other factors, otherwise these factors will only lead to clinical standard treatments. One of the revision processes occurs every few months (if not every year) or at even longer periods (if they really happen).

PCTR is provided to a digital processing engine to process the patient. PCTR is translated into a set of tasks and challenges for functions of interest, including cognition, perception, and sensorimotor capabilities, through interaction with the device and / or in combination with programmed physical activity.

The PCTR can be determined manually by a health care provider. Data sets obtained from individuals with a known condition (such as an immunomodulatory or neurodegenerative disorder) (such as from a patient registry 60) (e.g., written or oral reports of magnetic resonance imaging using natural language processing integration, Pharmaceutical, biological, or other treatment protocols; disease progression) can be used to train (as a non-limiting example) machine learning tools associated with deep learning to personalize components 54 to discover disease profiles that can be associated with neuropsychological performance And progress patterns, and used to optimize the effectiveness of cognitive processing. Through the training of this data set, the patient's performance and progress and other factors affecting cognitive and / or cognitive processing participation and / or effectiveness of cognitive processing are informed through feedback, and the processing personalization component 54 can provide a PCTR completely automatically.

In one example, the PCTR may also include one of the suggestions for using sensory stimuli in combination with one or more of the cognitive tools. Sensory stimuli according to a specific frequency band may promote cognitive processing and / or help reduce inflammation, for example, during use of the acting device or at other times, such as during sleep. As a non-limiting example, for suitable sensory stimuli using a gamma frequency pattern (approximately 40 Hz), using a uniform motion component, the computing device can be configured to present auditory stimuli or initiate other auditory-based interactions with the user, and / Or present vibrational stimuli or initiate other vibration-based interactions with the user, and / or present tactile stimuli or initiate other tactile-based interactions with the user, and / or present visual stimuli or initiate Other vision-based interactions. This sensory stimulus may be by a graphical user interface and / or other I / O devices used to deliver input to a user (e.g., a sensor for one of tactile, vibratory, or auditory signals, and / or at least one Moving components).

Device control can be adapted based on a personalized sensory motion profile, for example, instead of touching a target with a finger, a patient can use a nod motion or a voice command.

The PCTR may be transmitted (at transmission point 62 in Figure 1) for use by a digital processing engine such as the cognitive processing engine 14 to combine each distinct cognitive therapy algorithm (including interference processing, spatial navigation, emotion / emotion, Integrate cognitive and physical training with whole-body movement supplemented by sensory / motor discrimination (e.g., visual or auditory), fine-motion control training, cognitive-behavioral therapy, mindfulness, psychological education, or other), and suggest an appropriate algorithm within the process And stimulus (type and duration) (stepped profile, percentage (%) of each cognitive process administered to the patient during the process) to form a personalized cognitive process program for each patient.

The closed loop 2 iteration 16 provides continuous or continuous iteration of one of the performance thresholds achieved on any task being performed based on real-time performance in order to keep the user at the desired level of difficulty for maximum cognitive benefit. For example, the closed-loop system 10 may be configured to perform continuous or continuous iterations of the exchange of input data, measurement data, or output data between the interface 12 and the cognitive processing engine 14 and / or perform an interface 12 Based on the analysis performed by the cognitive processing engine 14 to adjust the difficulty level of one or more of the tasks or experiments presented, the analysis performed by the cognitive processing engine 14 is based on the input or measurement of the interface 12 Test data.

A performance metric generated by the device based on the patient's performance on the proposed task may be provided as an input to the monitoring component 52. For example, one of the emotional load, spatial navigation ability, or memory neutrality interferes with the processing index.

The novel adaptive processing closed loop allows PCTR to be adjusted at periodic intervals to personalize the processing of the cognitive profile of the evolving patient undergoing cognitive processing.

In some embodiments, the health care provider collects the appropriate input from the processing personalization component 54 and the processing personalization component 54 calculates a PCTR based on information received from the HCP on the interpretation of various possible processing inputs and knowledge. In other embodiments, machine learning techniques may be used to use the data received from the HCP to train the processing personalization component 54 to generate a PCTR.

Example methods, devices, and systems are configured to measure data indicating a user's performance at one or more tasks to provide a user performance metric that can ultimately be used as a disease organism mark. Example tasks may include an interference processing task, and / or a spatial navigation and memory task, and / or an emotional / emotional task. Example performance metrics can be used to derive an assessment of a user's cognitive ability, and / or measure a user's response to a cognitive process, and / or provide a user's condition (including physiological conditions and / or cognition) Condition) or other quantitative identifiers. A non-limiting example cognitive platform or platform product based on the principles herein may be configured to be based on data collected from an individual's interaction with the cognitive platform and / or platform product and / or based on analysis of that data (and related (Combined operations) and calculated metrics relative to a condition (such as, but not limited to, an immunomodulatory or neurodegenerative disorder), a level of protein expression that may be clinically significant in the condition, and / or the administration of a drug, biological or other A pharmaceutical agent uses cognitive platforms and / or the potential efficacy of platform products to classify an individual. Yet other non-limiting example cognitive platforms or platform products based on the principles herein may be configured to be based on data collected from an individual's interaction with the cognitive platform and / or platform products and / or based on analysis of that data ( And associated operations) and the measures of operations are used to classify a body relative to the likelihood and / or progression of an immune-modulating or neurodegenerative condition. The immunomodulatory condition may be, but is not limited to, multiple sclerosis or lupus.

Any performance indicator based on the principles herein and / or the classification of an individual relative to the onset probability and / or progression of a neurodegenerative condition may be transmitted as a signal to a medical device, health care computing system or other Device, and / or transmitted to a medical practitioner, a health practitioner, a physical therapist, a behavioral therapist, a sports medicine physician, a pharmacist, or other physician to allow the development of a treatment for an individual, or to modify a Existing processes include determining that one of the dosages of a pharmaceutical, biological, or other pharmaceutical preparation used in an individual is changed or determining one of the optimal types or combinations of pharmaceutical, biological, or other pharmaceutical preparations used in an individual.

In any of the examples herein, the platform product or cognitive platform can be configured as any combination of a medical device platform, a monitoring device platform, a screening device platform, a processing device platform, or other device platforms.

The present invention is also directed to an example system including a platform product and a cognitive platform that are configured for coupling with one or more physiological or monitoring components and / or cognitive testing components. In some examples, these systems include platform products and cognitive platforms integrated with one or more other physiological or monitoring components and / or cognitive testing components. In other examples, the system includes a separate housing from one or more physiological or monitoring components and / or cognitive test components and is configured to communicate with one or more physiological or monitoring components and / or cognitive test components to receive instructions Platform products and cognitive platforms that use such measured data from one or more components.

As used herein, the term "cData" refers to data collected from a user's interaction with one of a computer-implemented device formed as a platform product or a cognitive platform.

As used herein, the term "nData" refers to other types of data that can be collected in accordance with the principles herein. Any component used to provide nData is referred to herein as an nData component.

In any of the examples herein, cData and / or nData can be collected on the fly.

In a non-limiting example, nData may be collected from measurements performed using one or more physiological or monitoring components and / or cognitive testing components. In any of the examples herein, one or more physiological components are configured to perform a physiological measurement. Physiological measurement provides quantitative measurement data of physiological parameters and / or data that can be used to visualize physiological structure and / or function.

As a non-limiting example, nData can be used to determine protein types and / or protein configurations (which can provide information on a body's tissue or fluid (including blood) and / or from an individual's collected tissue or fluid (including blood). A measure of protein formation ( e.g., whether the protein is forming an aggregate) is a measure of collection. In some examples, measurements may be made on tissue and / or fluid extracted in situ or from the brain of an individual. An expression group may be defined based on a threshold expression level of one of clinically significant proteins in a neurodegenerative condition, wherein a measured value defined above one of the expression levels above a predetermined threshold value defines a first expression A second expression group is defined by a measured value of one expression level that is lower than a predetermined threshold. In other examples, nData may be neuropsychological or other clinical instrument data.

It should be understood that references to "drugs" herein include a drug, a biological and / or other pharmaceutical preparation.

In a non-limiting example, the physiological instrument may be based on MRI, and nData may be measurement data indicating cortical thickness, changes in brain functional activity, or other metrics.

In other non-limiting examples, nData may include any data that can be used to characterize the status of a subject, such as, but not limited to, age, gender, or other similar data.

In any of the examples herein, data (including cData and nData) was collected with the consent of the individual.

In any of the examples herein, one or more physiological components may include any component that measures physical properties of the body and nervous system, including EEG activity, heart rate, blood flow, and oxygenation levels, to provide nData. This may include camera-based heart rate detection, measurement of skin electrical response, blood pressure measurement, electroencephalogram, electrocardiogram, magnetic resonance imaging, near-infrared spectroscopy, sound mode, activity recorder, and / or pupil dilation measures to Provide nData.

Other examples used to provide nData for physiological measurements include, but are not limited to, body temperature measurements, use of an electrocardiogram (ECG) to measure heart or other heart-related functions, use of an electroencephalogram (EEG) to measure brain activity Measurement, event-related potential (ERP) measurement, magnetic resonance imaging (MRI) measurement, functional magnetic resonance imaging (fMRI) measurement, blood pressure measurement, potential measurement on a part of the skin Measurement, measurement of skin electrical response (GSR), measurement of magnetoencephalography (MEG), measurement of eye tracking devices or other optical detection devices including a processing unit that is programmed to determine the degree of pupil dilation, Measurement of functional near-infrared spectroscopy (fNIRS), and / or measurement of a PET scanner. An EEG-fMRI or MEG-fMRI measurement allows simultaneous acquisition of electrophysiology (EEG / MEG) nData and hemodynamics (fMRI) nData.

fMRI can also be used to provide measurement data (nData) indicative of neuronal activation based on the difference in magnetic properties of oxygenated blood supply and hypoxic blood supply to the brain. fMRI can provide an indirect measure of neuron activity by measuring regional changes in blood supply based on a positive correlation between neuron activity and brain metabolism.

A PET scanner can be used to perform functional imaging by detecting gamma rays indirectly released by a positron releasing radionuclides (a tracer) to observe the body's metabolic processes and other physiological measures. The tracer can be introduced into the user's body using a bioactive molecule. The body's metabolic process and other physiological indicators can be derived from the scan, including 2D and 3D images reconstructed from the computer based on the tracer concentration nData from the scan. nData may include measures of tracer concentration and / or PET images (such as two-dimensional or three-dimensional images).

In any of the examples herein, the task may be a space navigation task according to one of the principles in this article. In this example, a computing device is configured to present a high-level top view of a landscape including one or more internal routes and obstacles. In this example, portions of the route are configured to include paths and pathways that allow a character stand-in or other guideable element to traverse. The navigation task requires a body to establish a path from a starting point ("A") to at least one target position ("B") around a strategically located obstacle. The computing device may be configured to present instructions to the individual on a navigation route. A computing device may also be configured to provide an entity with an input device or other type of control element that allows an individual to traverse a route, including specifying and / or controlling one or more of the following: movement speed, orientation, speed, Choice of navigation strategy, waiting or delay period or other periods of inactivity before continuing or changing direction along one of the directions of a route, time interval for completing a route, and / or seeing a bird's eye view or elevated view The frequency or number of times (including as a map) includes the value of any of these parameters over time.

The computing device may be configured to collect data indicative of performance metrics quantified by the navigation strategy used to bring the individual from the starting point ("A") to one or more target points ("B"). For example, the computing device may be configured to collect individual directions, movement speeds, character avatars, or other guideable elements that indicate the direction of travel from the starting point ("A") along the dashed or dotted line And other metrics. In various examples, a performance metric that can be measured using a computing device may include data indicating the following: movement speed, orientation, speed, choice of navigation strategy, before continuing or changing direction along a given direction of a route Waiting or delay periods or other periods of inactivity, the time interval to complete a route, and / or the frequency or number of times a bird's-eye view or high-level view (including as a map) of a landscape is viewed, including any of these parameters This value changes over time. As another non-limiting example, the performance metric may include a metric that optimizes the degree of the path navigated by individuals traveling through the route, such as by determining the shortest path or near the shortest path through the route.

In another example herein, a task may involve one or more activities requiring a user to participate. Any one or more of the tasks may be implemented by a computer as computerized stimulation or interaction (explained in more detail below). For a targeting task, the cognitive platform may require time-specific responses and / or location-specific responses from a user. For a navigation task, the cognitive platform may require position-specific responses and / or motion-specific responses from the user. For a memory task, the cognitive platform may require stimulus-specific responses, location-specific responses, and / or time-specific responses from the user. For a facial expression recognition or object recognition task, the cognitive platform may require a time-specific response and / or a position-specific response from the user. For integrated cognitive and whole body motor tasks, cognitive platforms may require stimulus-specific responses, location-specific responses, and / or time-specific responses combined with specific body movements / dances. Multitasking tasks can include any combination of two or more tasks. In a non-limiting example, a user's response to a task (such as, but not limited to, a targeting task, and / or a navigation task, and / or a facial expression recognition or object recognition task) may be recorded using an input device of a cognitive platform . Non-limiting examples of such input devices may include touch, toggle, or other relative to a user interface or an image capture device such as, but not limited to, a touch screen or other pressure-sensitive screen, or a camera. Gestures, the user interface or image capture device includes any form of graphical user interface configured to record a user interaction. In other non-limiting examples, user responses to tasks (such as, but not limited to, targeting tasks, and / or navigation tasks, and / or facial expression recognition or object recognition tasks) recorded using the cognitive platform may include causing Contains user actions that change the position, orientation, or movement of a computing device on a cognitive platform. Such changes in the position, orientation, or movement of a computing device may be recorded using an input device (such as, but not limited to, a sensor) disposed in or otherwise coupled to a computing device. Non-limiting examples of sensors include a motion sensor, a position sensor, and / or an image capture device (such as, but not limited to, a camera).

In one example implementation involving a multi-tasking task, the computer device is configured (such as using at least one specially programmed processing unit) to cause the cognitive platform to operate over a short period of time (including instant and / or substantial Simultaneously) present a user with two or more than two different types of tasks, such as, but not limited to, targeting tasks, and / or navigation tasks, and / or facial expression recognition or object recognition tasks. The computer device is also configured (e.g., using at least one specially programmed processing unit) to collect, within a short period of time (including real-time and / or substantially simultaneously), instructions indicating the user's response to the multi-tasking task Type of information. In these examples, an individual may be presented with two or more different types of tasks within a short period of time (including instantaneously and / or substantially simultaneously), and the computing device may be configured to perform the task within a short period of time. Receive (including in real time and / or substantially simultaneously) data that indicates a user response with respect to two or more different types of tasks.

In some examples, the short period of time may be any time interval at a resolution of at most about 1.0 milliseconds or greater. These time intervals may be, but are not limited to, the duration of any partition with a periodicity of about 2.0 milliseconds or greater, up to any reasonable end time. Such time intervals may be, but are not limited to, about 3.0 milliseconds, about 5.0 milliseconds, about 10 milliseconds, about 25 milliseconds, about 40 milliseconds, about 50 milliseconds, about 60 milliseconds, about 70 milliseconds, about 100 milliseconds or more. In other examples, the short period of time may be, but is not limited to, a fraction of a second, about a second, between about 1.0 second and about 2.0 seconds, or up to about 2.0 seconds, or more.

In some examples, the platform product or cognitive platform may be configured to collect data indicating a response time of a user response relative to the presentation time of the task. For example, the computing device may be configured to cause the platform product or the cognitive platform to provide a user with a smaller or larger response time window as a way of adjusting the difficulty level to provide one of the responses to the task.

As used herein, the term "computerized stimulus or interaction" or "CSI" refers to a computerized element that is presented to a user to facilitate the user's interaction or other interaction (whether active or passive) with a stimulus. As a non-limiting example, the computing device may be configured to present auditory stimuli or initiate other auditory-based interactions with the user, and / or present vibratory stimuli or initiate other vibration-based interactions with the user, and / Or present tactile stimuli or initiate other tactile-based interactions with the user, and / or present visual stimuli or initiate other visual-based interactions with the user.

Any task in accordance with the principles herein may be presented to a user via a computing device, actuation component, or other device for implementing one or more stimuli or other interactive elements. For example, a task may be presented to a user by presenting a graphical user interface to present computerized stimulation or interaction (CSI) or other interactive elements. In other examples, the task may be presented to a user using a concerted motion component as an auditory, tactile or vibratory computerized element (including CSI). The description of the use of one or more CSIs (and analysis of data from one or more CSIs) in the various examples in this document also includes the use of tasks that include one or more CSIs in their examples (and Analysis of data from tasks).

In one example where the computing device is configured to present visual CSI, the CSI may be rendered using at least one graphical user interface to be presented to a user. In some examples, at least one graphical user interface is configured to measure a response when a user interacts with a CSI computerized element that is rendered using at least one graphical user interface. In a non-limiting example, the graphical user interface may be configured such that the CSI computerized element is active and may require at least one response from a user, such that the graphical user interface is configured to measure instructions for use Information on the type or degree of interaction between the user and the platform product. In another example, the graphical user interface may be configured such that the CSI computerized elements are passive and presented to the user using at least one graphical user interface, but may not require a response from one of the users. In this example, at least one graphical user interface may be configured to exclude a recorded response of one of the user interactions, applying a weighting factor to the data indicating the response ( e.g., weighting the response to a lower or higher Value), or use platform products to measure the data indicating the user ’s response as a metric of one of the user ’s misleading responses ( for example, sending a notification or other feedback to the user of misleading guidance).

In one example, the cognitive platform and / or platform product may be configured as a system, method or device implemented by a processor including at least one processing unit. In one example, at least one processing unit may be programmed to render at least one graphical user interface to present computerized stimulation or interaction (CSI) or other interactive elements to a user for interaction. In other examples, the at least one processing unit may be programmed to cause the consistent motion components of the platform product to produce auditory, tactile, or vibratory computerized elements (including CSI) to generate stimuli or other interactions with the user. The at least one processing unit may be programmed to cause a component of the program product to receive data (such as, but not limited to, cData) indicating at least one user response based on user interaction with CSI or other interactive elements, including the use of input devices provided Response. In one instance where at least one graphical user interface is rendered to present a computerized stimulation or interaction (CSI) or other interactive element to the user, at least one processing unit may be programmed to cause the graphical user interface to receive instructions at least Data from a user response. At least one processing unit may also be programmed to: analyze cData to provide a measure of an individual's cognitive condition, and / or analyze differences in individual performance based on determining differences between user responses (including differences based on cData). , And / or adjust the level of difficulty of auditory, tactile or vibratory computerized elements (including CSI), CSI or other interactive elements based on an analysis of cData (including measures of the performance of the individuals identified in the analysis), and / or Provide performance from platform products that can indicate an individual's performance, and / or cognitive assessment, and / or response to cognitive processing, and / or one of the outputs of the evaluated cognitive metric or other feedback. In a non-limiting example, at least one processing unit may also be programmed to be based on cData collected from an individual's interaction with a cognitive platform and / or platform product and / or based on analysis (and associated operations) of that cData. The computational measure is relative to an immunomodulatory or neurodegenerative condition, a protein expression level that may be clinically significant in the condition, and / or the use of cognitive platforms and / or platform products when administering a drug, biological or other pharmaceutical preparation to an individual Potential for classifying a body. In a non-limiting example, at least one processing unit may also be programmed to have an onset probability and / or progression relative to an immunomodulatory or neurodegenerative condition based on interactions between the individual and the cognitive platform and / or platform product. Collecting cData and / or classifying a body based on metrics calculated based on the analysis (and associated operations) of that cData. The neurodegenerative condition may be, but is not limited to, lupus or multiple sclerosis.

In one example, at least one processing unit may be programmed to render at least one graphical user interface to present CSI or other interactive elements, and / or cause the consistent motion components of the platform product to produce audible, tactile, or vibratory computerized elements ( Include CSI), or cause connected devices (such as goggles, headphones, haptics, or other) to generate passive stimuli to the user, such as via a graphical user interface and / or independent of the graphical user interface during cognitive processing The interaction is presented in a pattern of frequency band synchronization intended for the brain and / or inflammation / modulation for reducing the immune response.

An example system, method, and device according to the principles herein includes using a platform product (including the use of an app) of a cognitive platform that is configured to perform at least one emotional / emotional element (EAE) to Add emotional processing to tasks in a multi-tasking game (MTG) or a single-tasking game (STG) as an explicit component. In one example, EAE is used in tasks that are configured to assess or improve emotion-related cognition, and is collected as a measure of user interactions with EAE manifested in platform products The data (including cData) is used to determine the cognitive assessment measure or improvement of the cognitive measure after a process that is configured to interact with the graphical user interface of the platform product, or as the auditory and tactile sense of the platform product Or vibrating elements to interact. EAE can be configured to collect data to measure the impact of emotions on non-emotional cognition, such as by causing a graphical user interface to present to the user a spatial task to perform under an emotional load, and / or to collect Information that measures the impact of non-emotional cognition on emotions, such as by controlling the characteristics of emotions by causing graphical user interface performance to use measures of executive function. In an exemplary embodiment, the graphical user interface may be configured to perform tasks for performing the following operations under the cognitive load caused by MTG: identifying emotions indicated by CSI (based on measurement data); enabling His identification is maintained in working memory; and the mood is compared with the mood measure indicated by subsequent CSI.

In other examples, the platform product may be configured as a system, method or device implemented by a processor including a display component, an input device, and at least one processing unit. At least one processing unit may be programmed to render at least one graphical user interface for display at a display component to present computerized stimulation or interaction (CSI) or other interactive elements to a user for interaction. In other examples, the at least one processing unit may be programmed to cause the consistent motion components of the platform product to produce auditory, tactile, or vibratory computerized elements (including CSI) to generate stimuli or other interactions with the user.

Non-limiting examples of an input device include a touch screen or other pressure-sensitive or touch-sensitive surface, a motion sensor, a position sensor, a pressure sensor, a joystick, fitness equipment, and / or an image Capture device (such as, but not limited to, a camera).

In any example, the input device is configured to include at least one component configured to receive input data indicative of a body movement of an individual, wherein the data provides the body movement of the individual when interacting with a cognitive platform and / or platform product One metric, for example, to perform one or more tasks and / or tasks with interference.

Analysis of an individual's performance may include using a computing device to calculate percentage accuracy, number of hits, and / or number of misses during a session or from a previously completed session. Other identifiers that can be used to compute performance metrics are the amount of time it takes an individual to respond after presenting a task (eg, as a target-seeking stimulus). Other identifiers may include, but are not limited to, response time, response changes, number of correct hits, missed errors, false alarms, learning rates, spatial deviations, subjective assessments, and / or performance thresholds.

In a non-limiting example, the user's performance can be further analyzed to compare the impact of two different types of tasks on the user's performance, where these tasks exhibit different types of interference ( e.g., a distraction or an obstacle ). The computing device is configured to present different types of interference as CSI or other interactive elements that distract a user's attention from a main task. For a distraction, the computing device is configured to instruct the individual to provide a primary response to one of the main tasks and not provide a response ( ie, ignore distraction). For an obstacle, the computing device is configured to instruct the individual to provide a response as a secondary task, and the computing device is configured to follow the user's response to the main task (where at least one input device is used to collect the response). Obtain information indicating the user's secondary response to the obstruction within a short period of time, including at substantially the same time. The computing device is configured to calculate one of the user's performance at the main task without an interference, the performance when the interference is a distraction, and the performance when the interference is an obstacle Or more. A user's performance metric can be calculated based on these metrics. For example, the user's performance can be calculated as one cost (performance change) of each type of interference ( eg, distraction cost and obstacle / multi-tasking cost). The user's performance level on the task can be analyzed and reported as feedback, including reporting to the cognitive platform as a feedback for adjusting the difficulty level of the task, and / or reporting to individuals who care about the user's status or progress .

In a non-limiting example, the computing device may also be configured to analyze, store, and / or output the response time of the user's response and / or any statistical measure of an individual's performance ( e.g., in the last few sessions The percentage of correct or incorrect responses to a task type (including non-target and / or target stimuli, a specific type of task, etc.) within a specified duration or specifically.

In a non-limiting example, the computerized element includes at least one task that is presented at a graphical user interface as a visual task or as an auditory, tactile, or vibration task. For cData and / or nData collection purposes, each task may present an interactive mechanism designed to elicit a response from one of the users after the user is stimulated.

In a non-limiting example, the computerized element includes at least one platform interaction (game) element of a platform rendered on a graphical user interface, or at least one platform interaction as an aural, tactile, or vibration element of a program product ( Game) elements. Each platform interaction (game) element of the platform product may include an interaction mechanism (including in the form of a mechanism similar to a video game), or may or may not be a visual (or appearance) feature for the purpose of cData and / or nData collection.

As used herein, the term "game" encompasses user interaction (including other user experiences) with one of the aspects of a platform product.

In a non-limiting example, the computerized element includes at least one element to indicate positive feedback to a user. Each element may include an acoustic signal and / or a visual signal transmitted to the user indicating success at a task or other platform interaction element, that is , the user response at the platform product has exceeded one task or platform interaction ( Threshold success measure on one of the game elements.

In a non-limiting example, the computerized element includes at least one element to indicate negative feedback to a user. Each element may include an audible signal and / or a visual signal transmitted to the user indicating failure at a task or platform interaction (game) element, that is , the user at the platform product responds that a task or platform is not met Threshold success measure on an interactive element.

In a non-limiting example, the computerized element includes at least one element for message transfer, that is , communication with one of the users other than positive feedback or negative feedback.

In a non-limiting example, the computerized element includes at least one element for indicating a reward. A rewarding computer element may be a computer-generated feature delivered to a user to increase user satisfaction with CSI and thus increase active user interaction (and therefore the joy of the user experience).

In a non-limiting example, the cognitive platform may be configured to render multi-tasking interactive elements. In some instances, the multi-tasking interactive element is called a multi-tasking game (MTG). The multi-tasking interaction element includes an interaction mechanism configured to allow a user to participate in multiple time-overlapping tasks ( ie , multiple tasks that may require a substantially simultaneous response from a user).

In a non-limiting example, the cognitive platform may be configured to render a single task interactive element. In some instances, a single task interactive element is called a single task game (STG). A single task interaction element includes an interaction mechanism configured to engage a user in a single task at a given time interval.

According to the principles in this article, the term "cognitive" or "cognitive" refers to a mental action or process that acquires knowledge and understanding through thinking, experience, and feelings. This includes, but is not limited to, psychological concepts / areas, such as executive function, memory, perception, attention, emotion / emotion, motion control, and interference processing. An example computer-implemented device based on one of the principles herein may be configured to collect data indicating interactions with users of a platform product, and compute metrics that quantify user performance. The quantifiers of user performance can be used to provide a measure of cognition (for cognitive evaluation) or to provide a measure of the status or progress of a cognitive process.

According to the principles in this article, the term "treatment" refers to any manipulation of CSI in a platform product (including in the form of an APP), which manipulation results in a measurable improvement in a user's ability , Such as, but not limited to, improvements related to cognition, a user's mood, emotional state, and / or level of participation or attention to the cognitive platform. The degree or level of improvement can be quantified based on user performance metrics as described herein. In one example, the term "treatment" may also refer to a therapy.

According to the principles in this article, the term "working phase" refers to a discrete time period with a clear beginning and end. During this discrete time period, a user interacts with a platform product to receive a platform product (including an APP). Form).

According to the principles in this article, the term "evaluation" refers to at least one working stage where a user interacts with the CSI or other features or elements of a platform product. The data collected from one or more evaluations performed by a user using a platform product (including in the form of an APP) can be used to derive cognitive measures or other quantifiers or other aspects of a user's capabilities.

According to the principles in this article, the term "emotional load" refers to the cognitive load specifically associated with processing emotional information or regulating emotions.

According to the principles in this article, the term "cognitive load" refers to the amount of psychological resources that a user may need to complete a task. This term can also be used to refer to the challenge or difficulty level of a task or game.

In one example, the platform product includes a computing device configured to present a cognitive platform to a user based on interference processing. In an example system, method, and device that implements interference processing, at least one processing unit is programmed to render at least one first graphical user interface or cause a concerted component to generate an audible, tactile, or vibration signal as a The first task presents the first CSI, which requires a first type of response from a user. Example systems, methods, and devices are also configured to cause at least one processing unit to render at least one second graphical user interface or cause the actuation component to generate an audible, tactile, or vibration signal as one of the first tasks. A second CSI is presented by an interference, and a response from the user to one of the first tasks is required in the presence of the first interference. In a non-limiting example, the second type of response may include a first type of response to the first task and a secondary response to the first interference. In another non-limiting example, the second type of response may not include the first type of response, and is completely different from the first type of response. At least one processing unit is also programmed to receive data (such as, but not limited to, cData) indicating a first type of response and a second type of response based on interaction with the user of the platform product, such as, but not limited to, by rendering at least A graphical user interface to receive the data. The platform product may also be configured to receive nData (including data from physiological or physiological) indicating measurements made before, during, and / or after the user interacts with the cognitive platform. NData for measurement of monitoring components and / or cognitive testing components). At least one processing unit may also be programmed to: analyze cData and / or nData to provide a measure of an individual's condition (including physical and / or cognitive conditions), and / or based on the first type of response and The differences between the two types of responses (including differences based on cData) and the differences in associated nData are used to analyze differences in individual performance. At least one processing unit may also be programmed to adjust the data based on an analysis of cData and / or nData (including measures of the individual's performance and / or condition (including physiological and / or cognitive conditions) determined in the analysis). A task and / or first level of difficulty, and / or a platform product that provides an indication of an individual's performance, and / or cognitive assessment, and / or a response to cognitive processing, and / or one of the assessed measures of cognitive Output or other feedback. In a non-limiting example, at least one processing unit may also be programmed to be based on nData and cData collected from an individual's interaction with the cognitive platform and / or platform products and / or based on an analysis of their cData and nData (and related (Combined calculations) and calculated metrics relative to an immunomodulatory or neurodegenerative condition, a level of protein expression that may be clinically significant in the condition, and / or use cognitive platforms when administering a drug, biological or other pharmaceutical preparation to an individual, and / Or the potential effectiveness of a platform product to classify an entity. In a non-limiting example, at least one processing unit may also be programmed to be based on nData and cData collected from an individual's interaction with the cognitive platform and / or platform products and / or based on an analysis of their cData and nData (and related (Joint operation) and the metric of the operation classifies an individual relative to the likelihood and / or progression of an immune-modulating or neurodegenerative condition. Immunomodulatory or neurodegenerative conditions can be, but are not limited to, lupus and multiple sclerosis.

In an example, the feedback from the difference in the performance of the individual based on the difference between the measurement of the first type of response and the second type of response to determine the user and the nData can be used as an indicator entity in the cognitive platform Enter one of the immediate performances during one or more sessions. The feedback information can be used as an input to one of the computing components of the computing device in the same working phase being performed and / or in a subsequent working phase to determine the first task of the cognitive platform to interact with the user and / Or a degree of adjustment made at a difficulty level of the first disturbance.

As a non-limiting example, the interference processing-based cognitive platform may be a cognitive platform based on one of the Project: EVO ™ platforms of Akili Interactive Labs (Boston, Mass.).

In an example system, method, and device based on interference processing based on the principles in this article, as one of the components of interference processing, the graphical user interface is configured to identify one of the identification characteristics of the user-targeting task. The display in the platform serves as a feature of emotion, a shape, a color, and / or a position of an interference element in the interference process.

An example system, method, and device according to one of the principles herein includes a configuration to set a CSI level in an APP session based on measured nData (including indicators of neuropsychological disorders) indicative of physiological and / or cognitive conditions. A baseline measurement of attributes to increase the accuracy and processing efficiency of a cognitive platform and / or platform product (including the use of an APP). CSI can be used to calibrate an nData component to the individual user dynamics of nData.

An example system, method, and device according to one of the principles herein includes configuring to use nData to detect states of focus / non-focus, alertness, alertness, and / or fatigue to optimize delivery of CSI related to processing or evaluation A cognitive platform and / or platform product (including the use of an APP).

An example system, method, and device in accordance with one of the principles herein includes configurations configured to detect and focus on the processing and evaluation of specific CSIs related to the use of nData and CSI cData through subtle or apparent manipulation of CSI. A cognitive platform and / or platform product (including using an APP).

An example system, method, and device according to one of the principles herein includes configuration to generate cData using analysis of CSI patterns and nData of cData within or across multiple evaluation or processing sessions. And nData user profiles (including profiles that are ideal, best, or expected user responses) and a cognitive platform that manipulates CSI across multiple sessions or within multiple sessions to guide users to replicate these profiles and / or Platform products (including using an APP).

An example system, method, and device according to one of the principles herein includes nData configured to monitor indicators of parameters related to user participation and to optimize the cognitive load generated by CSI for optimal participation A cognitive platform and / or platform product (including the use of an APP) that aligns time in a state to maximize neural plasticity and the transfer of benefits from processing. As used herein, the term "neural plasticity" refers to the targeted reorganization of the central nervous system.

An example system, method, and device according to one of the principles herein includes nData configured to monitor anger and / or frustration to facilitate continuous user interaction with cognitive platforms by providing alternative CSI (also known as ("Playing a game") or a cognitive platform and / or platform product (including the use of an APP) that promotes CSI.

An example system, method, and device according to one of the principles herein includes a configuration to change the CSI dynamics within a plurality of assessment or processing sessions or across a plurality of assessment or processing sessions to make users aware or otherwise A cognitive platform and / or platform product (including the use of an APP) for nData optimization related to physiological or cognitive patterns.

An example system, method, and device according to one of the principles in this document includes a system configured to adjust CSI or CSI cognitive load in the event that a task automation nData signal is detected or task learning-related physiological measurements show signs of attenuation. A cognitive platform and / or platform product (including using an APP).

An example system, method, and device in accordance with one of the principles herein includes a method configured to combine signals from CSI cData and nData to optimize individualized processing to promote cognitive abilities and improvements in indicators of cognition. Cognitive platforms and / or platform products (including using an APP).

An example system, method, and device according to the principles herein includes a cognitive platform and / or platform product (including using an APP) configured to use a profile of nData to confirm / verify / identify a user's identity ).

An example system, method, and device in accordance with one of the principles in this article includes a configuration to use nData to detect positive emotional responses to CSI in order to catalog individual user preferences to customize CSI in order to optimize fun and facilitate response. A cognitive platform and / or platform product (including the use of an app) that evaluates or deals with ongoing participation in the work phase.

An exemplary system, method, and device according to one of the principles herein includes a user profile configured to produce cognitive improvement (such as, but not limited to, and classified as or known to exhibit improved working memory, attention, processing Speed and / or perceptual detection / identification of the user profile associated with the user) and delivery of one of the processes adapted to CSI to optimize the profile of a new user as confirmed by the profile from nData Platform and / or platform products (including using an APP).

An example system, method, and device according to the principles herein includes a cognitive platform and / or platform product (including a cognitive platform configured to provide a user with one or more profiles selected for cognitive improvement) Use an APP).

An example system, method, and device in accordance with principles described herein includes nData configured to monitor auditory and visual physiological measurements to detect evaluations or processes that can interfere with a user's use of a cognitive platform or program product A cognitive platform and / or platform product (including the use of an APP) from external environmental sources.

An example system, method, and device according to one of the principles herein includes nData configured to monitor the audio and visual measurements of devices from the environment to detect evaluations that can interfere with a user's use of a cognitive platform or program product A cognitive platform and / or platform product (including the use of an app) that handles interference from external environmental sources and adjusts the platform or product accordingly or notifies users to change environmental conditions or postpone to a more appropriate time.

An example system, method, and device according to one of the principles herein includes configurations to use cData and / or nData (including metrics from analytical data) as a determining factor or to make decisions about a user (including using a medical device) (A patient) is a decision that is likely to respond to a treatment (such as, but not limited to, a cognitive treatment and / or treatment using one of a biological, a drug, or other pharmaceutical preparation) or it is not possible to respond to the treatment A cognitive platform and / or platform product (including using an APP). For example, the system, method, and device can be configured to select whether a user (including a patient using a medical device) should be treated based on specific physiological or cognitive measures, such specific physiological or cognitive measures Tests can be used as a feature that has been validated to predict efficacy in a given individual or groups of individuals in a population ( eg, individuals classified into a given group of immunomodulatory or neurodegenerative diseases). This example system, method, and device configured to perform the analysis (and associated operations) described herein may be used as a biomarker to perform monitoring and / or screening. As a non-limiting example, exemplary systems, methods, and devices are configured to provide a cognitive process for a given individual or certain individuals in a population ( e.g., classification into a given group based on the state of an immunomodulatory disease). Individual) is a quantitative measure of the degree of efficacy (including the degree of efficacy of a biological, pharmaceutical or other pharmaceutical preparation in combination). In some examples, the individual or individuals in the population may be classified as having a particular neurodegenerative condition.

A non-limiting example classifier model can be trained to use trained cData and corresponding nData and generate an individual's immune regulation or neurodegeneration based on metrics collected from a user's interaction with at least one example cognitive platform and / or platform product Prediction of the status of sexually transmitted diseases. Training nData may include information indicating the status and age of immunomodulatory diseases corresponding to each user of cData collected for a given user (such as, but not limited to, from any of the example cognitive platforms and / or herein) User score for at least one interaction with the platform product). In some examples, nData may include data indicating the gender of the user. In other examples, the nData collected may indicate compliance or effectiveness with regard to cognitive processing adjustments. For example, cData may be collected based on a limited user interaction (e.g., about a few minutes) with any of the example cognitive platforms and / or platform products herein. The length of time for limited user interaction may be, for example , about 5 minutes, about 7 minutes, about 10 minutes, about 15 minutes, about 20 minutes, or about 30 minutes. Example cognitive platforms and / or platform products may be configured to perform an evaluation session (such as, but not limited to, performing an evaluation using a Project: EVO ™ platform).

Non-limiting example systems, methods, and devices based on principles herein also provide a cognitive platform and / or platform product configured to implement an example classifier model that is configured to be based on Measured data (including cData) from interactions with multiple users of example cognitive platforms and / or platform products to identify individuals with a positive state of immunomodulatory disease and a negative state of immunomodulatory disease with high accuracy . For example, the example classifier model can be configured to be based on baseline performance data from several user performances before using the example cognitive platform and / or platform products to perform a first assessment and use the example cognitive platform and And / or three (three) subsequent evaluations of the performance of the user's performance by the platform product to compare the measured data (including cData) to identify individuals with a positive state of immunomodulatory disease with an accuracy of about 83% Individuals with a negative state of immunomodulatory disease were identified with approximately a 79% accuracy.

A non-limiting example classifier model according to the principles herein can be trained to generate individuals using training cData and corresponding nData based on metrics collected from a user's interaction with an example cognitive platform and / or platform product The predicted amount of the state of the immunomodulatory disease. The training nData may contain data indicating the status and age of the immunomodulatory disease of each user. In some examples, nData may include data indicating the gender of the user. The corresponding cData is collected for a given user (such as, but not limited to, user scores from at least one interaction with any of the example cognitive platforms and / or platform products herein). For example, cData may be collected based on a plurality of interactive work sessions (eg, two or more interactive work sessions) performed by a user using one of the cognitive platforms and / or platform products herein. The length of each interactive session can be, for example , about 5 minutes, about 7 minutes, about 10 minutes, about 15 minutes, about 20 minutes, or about 30 minutes. Example cognitive platforms and / or platform products may be configured to perform multiple evaluation work phases (such as, but not limited to, performing an evaluation using a Project: EVO ™ platform).

As set forth above, example systems, methods, and devices according to the principles herein may be implemented using at least one processing unit of a programmed computing device to provide a cognitive platform and / or platform product. FIG. 2 illustrates an example device 200 that can be used to implement a cognitive platform and / or platform product that includes the classifier model described above, according to one of the principles herein. The example device 200 includes at least one memory 202 and at least one processing unit 204. At least one processing unit 204 is communicatively coupled to at least one memory 202.

Example memory 202 may include, but is not limited to, hardware memory, non-transitory tangible media, storage disks, optical disks, flash drives, computing device memory, random access memory (such as but not limited to DRAM, SRAM, EDO RAM), any other type of memory, or a combination thereof. The example processing unit 204 may include, but is not limited to, a microchip, a processor, a microprocessor, a special-purpose processor, a special-application integrated circuit, a microcontroller, a programmable gate array, any other Suitable for processors, or a combination thereof.

At least one memory 202 is configured to store processor-executable instructions 206 and a computing component 208. In a non-limiting example, the computing component 208 may be used to analyze cData and / or platform products received from a cognitive platform and / or platform product coupled to one or more physiological or monitoring components and / or cognitive test components as set forth herein. Or nData. As shown in FIG. 2, the memory 202 may also be used to store data 210 such as, but not limited to, nData 212 (including the results of operations from an example classifier model application, from the use of one or more physiological or monitoring components, and / Or measurement data for measurements performed by cognitive test components) and / or data (cData) indicating a body's response to one or more tasks, the response containing tasks performed on one of the graphical user interface locations of device 200 And / or using a response from a task generated by an audible, tactile, or vibration signal coupled to or integrated with the device 200. The material 210 may be received by being self-coupled to or integrated with the device 200 or one or more physiological or monitoring components and / or cognitive test components.

In a non-limiting example, the at least one processing unit 204 executes processor-executable instructions 206 stored in the memory 202 to analyze at least one of the physiological or monitoring processes using at least the computing component 208 CData and / or nData received by the cognitive platform and / or platform product coupled to the component and / or cognitive test component. The at least one processing unit 204 may also be configured to execute processor-executable instructions 206 stored in the memory 202 to apply the example classifier model to cDdata and nData to generate an indicator based on an immune regulation or neurodegenerative condition The result of an operation to classify an individual with the possibility of onset and / or the likelihood of onset and / or progression of a neurodegenerative condition (including an executive dysfunction). At least one processing unit 204 also executes processor-executable instructions 206 to control a transmission unit to transmit instructions to a cognitive platform coupled with one or more physiological or monitoring components and / or cognitive test components as described herein and / Or the analysis value of cData and / or nData received by the platform product, and / or the control memory 202 to store the value indicating the analysis of cData and / or nData.

In another non-limiting example, at least one processing unit 204 executes processor-executable instructions 206 stored in memory 202 to apply signal detection metrics in at least a computer-implemented adaptive response deadline program.

FIG. 3 is a block diagram of an exemplary computing device 310 that can be used as a computing component in accordance with the principles herein. In any of the examples herein, the computing device 310 may be configured to receive user input as a console to implement a computing component, including applying a signal detection metric in a computer-implemented adaptive response deadline program. For clarity, FIG. 3 also re-refers to various elements of the example system of FIG. 2 and provides more details of these elements. The computing device 310 may include one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing the examples. Non-transitory computer-readable media may include, but is not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more storage disks, one or more optical disks, one or more Flash drives) and the like. For example, the memory 202 included in the computing device 310 may store computer-readable and computer-executable instructions or software for performing the operations disclosed herein. For example, the memory 202 may store one of the configurations configured to perform various disclosed operations (e.g., analyze cognitive platform and / or platform product measurement data and response data, apply an example classifier model, or perform an operation) Software application 340. The computing device 310 also includes a configurable and / or programmable processor 204 and an associated core 314, and optionally one or more additional configurable and / or programmable processing devices, such as the processor 312 ' And associated cores 314 '(for example, in the case of a computing device with multiple processors / cores) for executing computer-readable and computer-executable instructions or software stored in memory 202, and for Other programs in the control system hardware. The processor 204 and the processor 312 'may each be a single-core processor or a multi-core (314 and 314') processor.

Virtualization can be used in the computing device 310, so that infrastructure and resources in the console can be shared dynamically. A virtual machine 324 may be provided to handle a program running on multiple processors such that the program appears to use only one computing resource rather than multiple computing resources. Multiple virtual machines can also be used with one processor.

The memory 202 may include a computing device memory or a random access memory, such as but not limited to DRAM, SRAM, EDO RAM, and the like. The memory 202 may include a non-volatile memory, such as but not limited to a hard disk drive or a flash memory. The memory 202 may also include other types of memory or a combination thereof.

In a non-limiting example, the memory 202 and the at least one processing unit 204 may be components of a peripheral device such as, but not limited to, a server key (including an adapter) or other peripheral hardware. An example peripheral device may be programmed to communicate with or otherwise couple to a main computing device to provide the functionality of an example cognitive platform and / or any of the platform products, an example application The classifier model and implements any of the example analyses (including associated operations) described herein. In some examples, the peripheral device may be programmed to communicate directly with or otherwise couple to the main computing device (such as, but not limited to, via a USB or HDMI input), or via a cable (including a coaxial cable) ), Copper wire (including but not limited to PSTN, ISDN and DSL), optical fiber or other connectors or adapters for indirect communication. In another example, a peripheral device may be programmed to communicate wirelessly with a main computing device (such as, but not limited to, Wi-Fi or Bluetooth®). An exemplary main computing device may be a smartphone (such as, but not limited to, an iPhone®, a BlackBerry®, or an Android ™ -based smartphone), a television, a workstation, a desktop computer, a laptop computer, A tablet, a keyboardless slate, an e-reader, a digital assistant, or other electronic reader or handheld, portable or wearable computing device, or any other equivalent Device, an Xbox®, a Wii® or other equivalent computing device.

A user may interact with the computing device 310 through a visual display unit 328, such as a computer monitor. The visual display unit may display one or more user interfaces 330 that may be provided according to example systems and methods. The computing device 310 may include other I / O devices for receiving input from a user, for example, a keyboard or any suitable multi-touch interface 318, a pointing device 320 ( e.g., a mouse), a A camera or other image recording device, a microphone or other sound recording device, an accelerometer, a gyroscope, a sensor for tactile, vibration or auditory signals, and / or at least one actuator. The keyboard 318 and the pointing device 320 may be coupled to the visual display unit 328. The computing device 310 may include other suitable I / O peripherals.

The computing device 310 may also include one or more storage devices 334 (including a single-core processor or a multi-core processor 336) for storing data and computer-readable instructions and / or software for performing the operations disclosed herein, Such as a hard drive, CD-ROM, or other computer-readable media. The example storage device 334 (including a single-core processor or a multi-core processor 336) may also store one or more databases for storing any suitable information required to implement the example systems and methods. These databases may be manually or automatically updated at any suitable time to add, delete, and / or update one or more items in the database.

The computing device 310 may include a network interface 322 configured to interface with one or more networks via one or more network devices 332, for example, via a variety of connections to a local area network (LAN) , Metropolitan Area Network (MAN), Wide Area Network (WAN), or Internet access, these connections include, but are not limited to, standard phone lines, LAN or WAN links (for example, 802.11, T1, T3, 56kb, X.25), broadband connection (for example, ISDN, frame relay, ATM), wireless connection, controller area network (CAN) or any or all of the above combinations. The network interface 322 may include a built-in network adapter, a network interface card, a PCMCIA network card, a card bus network adapter, a wireless network adapter, a USB network adapter, and a modem. Or suitable for interfacing the computing device 310 to any type of network capable of communication and any other device that performs the operations set forth herein. In addition, the computing device 310 may be any computing device, such as a smartphone (such as, but not limited to, an iPhone®, a BlackBerry®, or an Android ™ -based smartphone), a television, a workstation, a desktop computer, a Server, a laptop, a tablet, a keyboardless tablet, an e-reader, a digital assistant, or other e-reader or handheld, portable, or wearable computing device , Or any other equivalent device, an Xbox®, a Wii®, or other equivalent form of operation that is capable of communicating and has or can be coupled to sufficient processor power and memory capacity to perform the operations described herein Telecommunications equipment. One or more network devices 332 can communicate using different types of protocols, such as but not limited to WAP (Wireless Application Protocol), TCP / IP (Transmission Control Protocol / Internet Protocol), NetBEUI (NetBIOS Extended User Interface) Or IPX / SPX (Internet packet exchange / sequential packet exchange).

The computing device 310 can run any operating system 326, such as any of the versions of the Microsoft® Windows® operating system, iOS® operating system, Android ™ operating system, different versions of Unix and Linux operating systems, for Macintosh computers Any version of MacOS®, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, or any other operating system capable of running on a console and performing the operations described in this article. In some examples, the operating system 326 may operate in a native mode or a simulated mode. In one example, the operating system 326 may run on one or more cloud machine instances.

In any of the examples herein, adjustments to the type of task and / or CSI can be made on the fly.
Examples of cognitive tools
interference

Figures 4A through 6D show non-limiting, exemplary user interfaces that can be implemented using the example systems, methods, and devices herein, which interfaces are used to perform tasks and / or interferences for user interaction (any Either or both have computer-implemented time-varying elements). The non-limiting example user interfaces of FIGS. 4A to 6D may also be used for one or more of them: to show individuals instructions for performing tasks and / or interruptions; to interact with time-varying elements implemented by computers; to collect and indicate individuals Information on responses to tasks and / or disturbances and time-varying elements of computer implementation; display progress metrics; and provide analytical metrics.

4A-4D show non-limiting example user interfaces implemented using the example systems, methods, and devices herein. As shown in FIG. 4A to FIG. 4B, an example stylized processing unit may be used to render the display feature 500 and the measurement feature 502 to a user interface (including a graphical user interface), and the display feature 500 is used to display to an individual Instructions for performing tasks and / or disturbances. The metric feature 502 is used to display status indicators from progress measures and / or data collected from interactions with individuals (including responses to tasks / interferences). Apply analytics to provide analytical metrics. The result. In any of the example systems, methods, and devices herein, a predictive model can be used to provide an analytical metric provided as a response output. In any of the example systems, methods, and devices herein, data collected from user interactions can be used as input to train a predictive model. As shown in FIGS. 4A-4B, an example stylized processing unit can also be used to control a body's needs (such as, but not limited to, navigating a path or other environment in a visual motion task, and / or In the target identification task, an object), a character avatar, or other processor-guided guide 504 is rendered to a user interface (including a graphical user interface). As shown in FIG. 4B, when the user interface draws (using dashes) the type of movement of the character avatar or the guide 504 rendered by other processors to perform navigation tasks, the display feature 500 can be used to indicate to the individual Expected to perform one of the navigation tasks. In one example, the navigation task may include an individual needing to manipulate a milestone object 510 that a character doubles through or avoids in order to determine a score. As shown in FIG. 4C, when the user interface depicts the types of objects 506 and 508 that can be rendered to the user interface, the display feature 500 can be used to indicate to the individual the content of a target identification task that is expected to be performed, one of which type The object 506 is designated as a target, and another type of object 508 that can be rendered to the user interface is designated as a non-target, for example, designated as a non-target by being crossed out in this example. As shown in FIG. 4D, the user interface draws (using dashes) the type of movement of the guide 504 that is performed by a character avatar or other processor that needs to perform navigation tasks, and the user interface presentation is designated as a target When the object type of the object 506 and the object type designated as a non-target object 508, the display feature 500 can be used to indicate to the individual a navigation task (as a main task) and a target identification (as a secondary task) expected to be performed ( also That is, one interference) the content of both.

Analyze measured data that indicates an individual's response to a single-task task that appears as a target-seeking task to provide perception (detection and identification), motor function (detection and identification), impulsive / inhibitory control And quantitative insights into the cognitive domain of visual working memory. Measured data indicating an individual's response to a single-task task that appears as a navigation task can be analyzed to provide quantitative insights into the cognitive domain of visual motion tracking and motor function. In a multi-tasking task, the measured data indicating the response of an individual to a main task (acting as a navigation task) in the presence of an interference (acting as a targeting task) can be analyzed to provide a response Quantitative insight into the cognitive domain of distractive attention and interference management.

5A to 5T show one non-limiting example of tasks and interference dynamics that can be realized at the user interface according to the principles herein. In this example, the main task is a visual motion navigation task, and the interference is target identification (as a secondary task). As shown in FIGS. 5D, 5I to 5K, and 5O to 5Q, the individual needs to perform navigation tasks by controlling the movement of the character avatar 602 along a path that coincides with the milestone object 604. 5A-5T show a non-limiting exemplary embodiment in which a device or computing device (or other sensing device) is expected to be actuated in response to a navigation task to cause a character avatar 602 to coincide with a milestone object 604, The score is based on an individual's success in traversing a path (eg, a hit) with the landmark object 604. In another example, an individual is expected to actuate a device or computing device (or other sensing device) to cause the character avatar 602 to miss the milestone object 604, where the score is based on the individual's success in avoiding the milestone object 604. 5A to 5C show the dynamics of a target object 606 (a star with a pattern of a first type). 5E to 5H show the dynamics of a non-target object 608 (a star with a second type of pattern). Figures 5I to 5T show the dynamics of other parts of the navigation task, in which the individual is expected to guide the character avatar 602 through the path of the milestone object 604 in the absence of a distraction (one of the instances of the secondary task).

In the examples of FIGS. 5A-5T, the processing units of the exemplary systems, methods, and devices are configured to receive data indicative of the physical movements of the individual such that the character avatar 602 navigates the path. For example, an individual may need to perform a physical action to "manipulate" a character avatar, for example , by changing a rotational orientation or otherwise moving a computing device. This action can cause a gyroscope, or accelerometer, or other motion or position sensor device to detect movement, thereby providing measurement data indicating the success of the individual in performing navigation tasks.

In the examples of FIGS. 5A to 5C and 5E to 5H, the processing units of the example systems, methods, and devices are configured to receive data indicating physical actions used by an individual to perform target identification tasks. For example, prior to a test or other work session, in response to the display of a target object 606, the individual may be instructed to touch or make other physical instructions, and in response to the display of a non-target object 608, the individual may be instructed not to Tap to make physical instructions. In FIGS. 5A to 5C and 5E to 5H, in an interference processing multitasking implementation, the target identification task acts as an interference to one of the main navigation tasks (that is, one of the executions of the secondary task). As set forth above, the example systems, methods, and devices may cause the processing unit to exhibit a display feature to display instructions to an individual regarding expected performance. As also set forth above, the processing units of the exemplary systems, methods, and devices can be configured to (i) collect data that indicates the extent and type of an entity's response to interference (whether or not the interference includes a target or a non- (Objective) while receiving data indicating the degree and type of the entity's response to the main task, or (ii) collecting data indicating the degree and type of the entity's response to the task, substantially simultaneously ( i.e., (At substantially the same time) to selectively receive data indicative of the magnitude and type of response of the individual to a disturbance that includes a target stimulus ( i.e. , an obstacle), and to collect information indicating the individual's response to the task The degree and type of measurement data are substantially simultaneously ( i.e. , at substantially the same time) selectively not collecting the extent and type of the individual's response to an interference that includes a non-targeted stimulus ( i.e., a distraction) Measurement.

6A-6D show other non-limiting examples of tasks and interference dynamics that can be realized at the user interface in accordance with the principles herein. In this example, the main task is a visual motion navigation task, and the interference is target identification (as one of the secondary task execution instances). Similar to FIG. 5A to FIG. 5T, the individual needs to perform navigation tasks by controlling the movement of the character avatar 702 along a path. The individual is required to provide a response to one of the tasks in the presence or absence of an interference 704 (acting to identify a target).

In a non-limiting example, each different stimulus presented as a computer-implemented time-varying element can be used to adapt the adaptability of the difficulty of a task and / or interference.

In another non-limiting example, the example systems, methods, and devices herein can be configured to perform one or more tasks and / or interference at a level of difficulty at a fixed time interval or other set schedule. Adaptation, such as, but not limited to, frequencies per second, at 10-second intervals, every 30 seconds, or at 1 / second, 2 / second, or greater (such as, but not limited to, 30 times / second).

In one non-limiting example of a visual motion task (a type of navigation task), one of the navigation speed, the shape of the route (turn frequency change, turn radius change), and the number and / or size of obstacles may be changed or Many to modify the difficulty of a navigation game level, where the level of difficulty increases with speed and / or obstacles (including multiple types of milestone objects (for example, some milestone objects are used to avoid, or some milestone objects It is used to increase the number and / or size of)).

In a non-limiting example, as a feedback, the difficulty level of a task and / or interference at a subsequent level can be changed immediately, for example, the difficulty of a subsequent level can be increased or decreased in relation to the information indicating the performance of the task small.

In one example, the response recorded for the targeting task may be, but is not limited to, a user interface or image collection device (including a touch screen or other pressure-sensitive screen) used to interact with a user interface. , Or a camera). In another example, the response recorded for the targeting task may be, but is not limited to, a user action that causes a change in the position, orientation, or movement of a computing device that includes a cognitive platform. The computing device is recorded in or otherwise coupled to a sensor of the computing device, such as but not limited to a motion sensor or a position sensor.

In this and any other examples herein, cData and / or nData may be collected on the fly.

In this and any other examples herein, adjustments to the type of task and / or CSI can be made on the fly.
navigation

7A to 7D show non-limiting examples of computerized presentation of a route (path) for a navigation task.

FIG. 7A shows a non-limiting example of a computerized presentation of a route that can be used to present a navigation task, which includes a route learning task, or a relatively directional task, or a wayfinding, according to the principles herein. Tasks or any combination thereof. In this example, the computing device is configured to present a high-level top view of an environment 710 including one or more internal routes 712 and obstacles 714. In this example, a portion of the route 712 is configured to include paths and pathways that allow a user indicator, such as but not limited to a character stand-in or other navigable element 716, to traverse. In this example, the environment appears as a city street type structure, however, other exemplary environments are included in the present invention. According to the description in the present invention, the Cartesian axis (x-axis, y-axis, and z-axis) directions in the environment are only used as a guide, and are not intended to limit the environment. The example environment also includes a number of strategically placed shaped objects 718 (such as a ring, a ball, a cone, etc.) that a user is responsible for positioning. In this example, the user is presented with a perspective view of a fully localized landscape and obstacles, so that the user needs a strategy for crossing the route without resorting to the entire route or a bird's eye view of a significant part of the route Make a choice or decision. The navigation task requires a body to develop a path from a starting point to at least one of the shaped objects 718 around a strategically located obstacle 714. The example environment may include one or more portals 719 that remain in the same location or in different locations relative to the environment 710. The computing device may be configured to present the instruction to the individual to instruct the positioning of the shaped object 718 during a test phase, and optionally allow the user in an exploration phase (including a guided route phase or a free exploration phase) Familiarize yourself with the location and type of obstacles 714 and shaped objects 718 in environment 710. The computing device may also be configured to provide an entity with an input device or other type of control element (including a joystick, joystick, buttons, or other controls described above) that allows an individual to pass through route 712, including provisions and / Or control one or more of the following: movement speed, orientation, speed, choice of navigation strategy, waiting or delay periods or other periods of inactivity before continuing to travel or change direction along one of a route, The time interval for completing a route, and / or the frequency or number of times a bird's-eye view or elevated view (including as a map) of a landscape is viewed, when a previously learned route is regenerated (e.g., at one or more test stages) (Medium) Accuracy is a measure of a user's use of spatial memory rather than visual cues to orient a user's pointer (such as, but not limited to, the origin of a predetermined, predetermined navigation route) in space Including a measure of accuracy when pointing the user pointer back to that particular location) and / or one of the strategies used in exploring and learning a novel environment measure. In any of the examples herein, a metric may include a value of any of these parameters over time. As a non-limiting example, a performance metric may include a measure of how well an individual traversing a route navigates, such as determining the shortest or nearest shortest route through a route, the time to complete a task, or a route Other scoring mechanisms associated with a learning task, or a relatively directional task, or a pathfinding task, or any combination thereof (as set forth herein).

In an exemplary embodiment, the walls of the environment can be configured with different colors (indicated as a color 1, color 2, color 3, and color 4) to provide a user with a visual cue for navigating through the environment 710 . For example, each color can be a different color, two or more colors can be the same color, or all colors can be the same color. A first specific color can be used to indicate a wall that intersects the x-axis of the environment (for example, color 3 is the same as color 4), and a second different specific color can be used to indicate a wall that intersects the y-axis of the environment (for example, color 3 (Same as Color 4).

The computing device may be configured to collect data indicative of performance metrics that bring individuals to one or more targets from a starting point ("A") or entrance 719 when performing a route learning task, a wayfinding task, or a combined task Quantification of the navigation strategy (including path, speed, and number of turns and glances) used by location, landmark, shaped object, or end point ("B"). For example, the computing device may be configured to collect individual decisions, movement speeds, user indicators (such as but It is not limited to the orientation of character avatars or other navigable elements 716) and other metrics (as explained above). This information may be collected during one or more quiz stages. This data may also be collected during the exploration phase to provide a baseline or other comparative metric for computing the scores described herein. In various examples, a performance metric that can be measured using a computing device may include data indicating the following: movement speed, orientation, speed, choice of navigation strategy, before continuing or changing direction along a given direction of a route Waiting or delay periods or other periods of inactivity, the time interval to complete a route, and / or the frequency or number of times a bird's-eye view or high-level view (including as a map) of a landscape is viewed, including any of these parameters This value changes over time. As another non-limiting example, a performance metric may include a measure of how well an individual traveling through a route has navigated, such as determining the shortest or nearest path through a route, time to complete a task, or Other scoring mechanisms associated with a route learning task, or a relatively directional task, or a pathfinding task, or any combination thereof (as set forth herein).

As shown in the example of FIG. 7A, the route 712 may include instructing an individual to locate one or more targets (such as a shaped object 718, a landmark, or other desired location) as the route 712 is crossed. In this example, the performance metric may include a score based on a specific type of one of the targeted targets, and / or the total number of targeted targets and / or the time it takes to locate the targets. In a non-limiting example, an individual navigation route 712 may be instructed such that multiple targets are located in a prescribed sequence. In this example, the performance metric may include a score based on the number of targets located in the sequence and / or the time it takes to complete the sequence.

FIG. 7B shows a non-limiting example of another computerized rendering of a computing device that can be implemented to present an environment 720 of a navigation task according to the principles herein. In this example landscape 720, a portion of the route 722 is defined by an obstacle 724 and is configured to allow a user indicator (such as, but not limited to, a character stand-in or other navigable element 726) to pass from an origin 729 To a prescribed goal. As explained above, the origin 729 may be in the same or different position relative to the environment between the two quiz stages. As shown in FIG. 7B, the obstacle 724 may have a different cross-sectional shape, such as a substantially square cross-section of one of the obstacles O ₁ compared to a longitudinal cross-section of one of the obstacles O ₂ . In this example, the user is presented with a perspective view of a fully localized landscape and one of the obstacles, so that a body needs a strategy for crossing the route without resorting to the entire route or a bird's eye view of a significant part of the route Make a choice or decision. The computing device may be configured to collect individual decisions indicating continued travel along dashed or dotted lines (such as, but not limited to, a user's forward or original return movement during the test phase of a route learning task), And / or speed of movement, and / or orientation of user indicators (such as, but not limited to, character avatars or other navigable elements 726), such as, but not limited to, a user may require during the quiz phase of a course learning task Origin point (or other indication) and other metrics. In this example, a performance metric measured using a computing device relative to a localized landscape may include data indicating one or more of the following: movement speed, orientation, speed, choice of navigation strategy, in Wait or delay periods or other periods of inactivity before proceeding or changing directions along one of the routes, time interval between completing a route, and / or seeing a bird's eye view or elevated view of a landscape (including as a map) Frequency or number of times, a measure of accuracy in regenerating a previously learned route (e.g., in one or more quiz stages), a user using spatial memory rather than visual cues relative to one of the spaces A measure of the accuracy of a specific location (such as, but not limited to, the origin of a predetermined, predetermined navigation route) when orienting a user pointer (including directing the user pointer back to that specific location), and / or exploring And learning one of the strategies used in a novel environment. In any of the examples herein, a metric may include a value of any of these parameters over time. As another non-limiting example, the performance metric may include a metric that optimizes the degree of the path navigated by the individual traveling through the route, such as, but not limited to, determining the shortest path or near the shortest path through the route.

The example environment 720 includes a plurality of target shaped objects S _i ( i = 1, 2, 3, 4) that instruct the individual to locate when crossing the route 722 from the origin 729. In this example, the performance metric may include a score based on the success of locating a specific target object, the number of targets located (including from multiple test stages), and / or the time it takes to locate the target. In a non-limiting example, an individual navigation route 722 may be instructed such that multiple targets are located in a prescribed sequence. In this example, the performance metric may include a score based on the number of targets located in the sequence and / or the time it takes to complete the sequence.

In an exemplary pathfinding task, a computing device may be configured to present a body with a broader bird's-eye view (such as, but not limited to, those shown in FIGS. 7A-7B) of at least one instance of a working phase. View) to the ability to change to a more localized perspective, such as, but not limited to, the perspectives shown below in FIGS. 9A-9U.

As a non-limiting exemplary embodiment of a wayfinding task, a bird's eye view such as that shown in FIG. 7A or FIG. 7B can be presented to an individual to obtain an overview of the route, but then an individual may need to read from below One of the more localized perspective navigation routes shown in Figs. 9A to 9U. In this example, an entity may need to rely on non-allocentric navigation capabilities to self-localize the perspective view based on the spatial memory formed by the individual from a broader bird's-eye view of Figure 7A or 7B (similar to The more localized perspectives shown in Figures 9A-9U below) make choices and decisions to navigate the route.

FIG. 7C shows one non-limiting example of the types of dimensional constraints that can be imposed on the environment's pathways, obstacles, and sizes. As shown in FIG. 7C, the width of the obstacle (α ₁ ) is approximately or approximately equal to the width of the path (α ₂ ). In a non-limiting example, α ₁ is about twice as large as α ₂ . The width (α ₁ ) is also smaller than the length of the environment wall (α ₃ ), so that no part of the environment will appear as inaccessible due to an obstacle. In a non-limiting example, α ₁ is about a quarter or a fifth of α ₃ . Although example appropriate values are given for the relative dimensions (width and length) of the pathway, obstacle, and environmental wall, these values are not intended to be limiting, but only α ₃ > α ₂ > α _{1 is required} .

FIG. 7D shows a non-limiting example of a computerized environment, where the path 740 from point A to point B includes at least one turn 742 with a discrete angle (represented by angle θ ₁ ). In one non-limiting example of a task involving path integration, such as, but not limited to dead-reckoning, a user needs to navigate from a starting point A to a target end point (C ) And use a pointer from point C to "point" or otherwise indicate origin A. In one example, the system is controllable to allow the user to indicate any angle around point C in the range of 0 ° to at least about 180 °. In another example, the system is controllable to allow the user to indicate any angle around point C over the entire range from 0 ° to 360 °. One measure of the success of the performance of the task is a measure of the angle Δ (Δα) between the relative orientation of the origin (dashed arrow 744) and the actual relative orientation of the origin (dashed arrow 746).

As shown in FIG. 7D, one of the navigation paths (included in the examples of any of Figures 8A-9U below) of any of the example environments described herein may be included as part of a curve or substantially non-linear .

8A-9U show various perspective views of portions of a computerized rendering of an environment during various non-limiting example navigation tasks in accordance with the principles herein. In these examples, the computing device is configured to present a different perspective view of a selected portion of an environment in which an individual needs to navigate, but from a user pointer (such as, but not limited to, a character stand-in or other navigable element) Angle of view. The example perspective view illustrates navigation through an example environment and is not a limitation on the scope of the invention. Example images depict the type of perspective sequence that a user may encounter as the user navigates through the environment.

8A-8C show different perspective views of an exemplary inlet 800 (shown here as a illuminated opening) when a user activates a control of the computing device to enter the environment through the inlet. 8A to 8C also show examples of the type of head-up display (HUD) 802 that the computing device can use to display to a user when the user navigates the environment. In this example, the computing device prompts the user with the display of the command "Ready to explore" (such as HUD 802).

9A to 9U show non-limiting examples of a series of perspective views of an environment when the computing device allows a user to conduct an exploration to become slightly familiar with the environment. In the example of FIG. 9A, as the user explores the environment, portions of the exemplary route 902 are defined by obstacles 904 and a wall 906, and are configured to allow the user indicators such as, but not limited to, a character stand-in or other Guided element) through. An example of a target shaped object 908 (in this example, a ball) that can instruct the user to locate during one or more quiz sessions is also shown. Figures 9B and 9C show examples of perspective views rendered when a user actuates a control of a computing device to turn and move around in the environment. 9D-9U show perspective views of the environment when a user moves forward, backwards, and turns around an obstacle in the environment. Figures 9D to 9U also show non-limiting examples of the amount of time the computing device renders to the user to indicate that it is an exploration phase and allows the user to explore (whether it is a guided route or a free exploration) HUD 910 displays and instructs the user HUD 912, one of the time it takes to navigate during the exploration phase. 9D-9U show other non-limiting exemplary shaped objects positioned around the environment, including a cone 914, a cube 916, and a ring 918.

In a non-limiting example, a subject may be presented with a perspective view such as that shown in Figures 9A-9U, where verbal or visual instructions are used to indicate that the individual has been placed in a previously experienced virtual environment (through the exploration phase ) At an unknown location, and instructs the individual to perform a navigation task from this unknown location. As an example of such a navigation task, an entity may need to use computing device controls to look around, determine its current position with its maximum power, and point to a previously navigated (and presumed known) position within the environment. The performance measure for this task will include the accuracy of the targeted response and the time required to generate the response. As another example of such a navigation task, an entity may need to move its character avatar from an unknown location to a hypothetical known location within the environment. The performance metric for this task may include the time required to reach the target location and the path used to reach the target location and one or more optimal paths (e.g., the optimal path determined using mathematical or algorithmic operations or modeling methods) )difference between.

As shown in FIGS. 9A to 9U, the relative sizes of the pathway, obstacles, and environmental walls are configured such that α ₃ > α ₂ > α ₁ (as explained in connection with FIG. 7C), and obstructive perspective views are presented to them. A user observes the content of an adjacent passageway until the user is within a certain distance of a crossing passage or a turn. As a non-limiting example, the dimensions α ₃ : α ₂ : α ₁ may be related to a 10: 2: 1 ratio.
Emotional processing

As set forth herein, the example systems, methods, and devices herein may be implemented to adapt tasks and / or interference between user sessions (or even user experiments) (at least one of which includes a triggering element) Based on the science of brain plasticity to enhance one's cognitive skills under emotional load. Adaptability is a useful design element for any effective plasticity tool. In the example systems, methods, and devices, the processing unit is configured to control tasks and / or parameters of the disturbance (such as, but not limited to, the timing, positioning, and nature of the stimulus) such that the physical movements of the individual can be recorded during the interaction. As explained above, during interaction with a computing device to perform single-tasking tasks and multi-tasking tasks, an individual's physical movements are affected by their neural activity. Scientific presentation of interference processing (based on results from physiological and behavioral measurements), adaptive patterns can be based on neuroplasticity in response to training from multiple work phases (or experiments) to produce changes in the brain of a body, thereby enhancing Individual cognitive skills. Example systems, methods, and devices are configured to perform tasks and / or disturbances using at least one triggering element, where an individual performs disturbance processing under an emotional load. As supported by the public research results described above, the impact on a person's performance of tasks under emotional load can unearth novel aspects of cognitive training to enhance individuals' cognitive abilities.

Figures 10A to 13P show non-limiting examples of systems and methods and devices herein that can be used to perform tasks and / or interference (either or both of which have triggering elements) for user interaction. Example user interface. The non-limiting example user interface of FIGS. 10A to 13P may also be used for one or more of the following: displaying instructions to an individual to perform tasks and / or interruptions; interacting with triggering elements; collecting instructions to the individual to Information on tasks and / or interference and triggering element responses; display progress metrics; and provide analytical metrics.

10A to 10B show examples of initiating elements (target or non-target) that are realizable to an exemplary user interface according to the principles herein. FIG. 10A shows an example of a triggering element that appears as different types of facial expressions, which include facial expressions with positive valence (happy) and facial expressions with negative valence (anger) . For example, the triggering element may be represented as a face having a happy expression 1005, a neutral expression 1006, or an angry expression 1007. FIG. 10A also shows the adjustment of the facial expression that triggers the element, thereby showing the facial expression of varying degrees from the extremely happy face 1005 (the highest level) to the neutral face 1006 as the level of happiness gradually decreases, and also shows as The degree of anger gradually decreases from the extremely angry face 1007 (the highest degree) to the neutral face 1006 to different degrees of facial expressions, each of which can potentially trigger a different level of emotional response. FIG. 10B shows an example user interface with one of the triggering elements that manifest as different types of facial expressions (happy 1010, neutral 1014, anger 1016). FIG. 10B also shows an example display feature 1018 for displaying to an individual instructions for performing tasks and / or disturbances and for interacting with the triggering element. In the non-limiting example of FIG. 10B, the display feature 1018 may be used to indicate to an individual the content expected to perform a target identification task using one of the types of response required to trigger the element (in this example, the happy face 1012 is performed Identification and targeting).

11A to 11D show examples of object features (target or non-target) that can be implemented as time-varying characteristics to an exemplary user interface according to the principles herein. FIG. 11A shows a modification in which the time-varying characteristics of one of the objects 1100 rendered to the user interface is a dynamic change of one of the position and / or speed of the object 1100 relative to the environment rendered in the graphical user interface. An example. FIG. 11B shows the modification of the time-varying characteristics of one aspect of the object 1102 rendered to the user interface, which is the size and / or direction of the trajectory / movement and / or the object 1102 rendered relative to the graphical user interface. An example of dynamic changes in the orientation of the environment. FIG. 11C shows that one of the changes to the time-varying characteristics of one of the objects 1104 rendered to the user interface is one of the dynamic changes of the shape or other type of the object 1104 relative to the environment rendered in the graphical user interface. Instance. In this non-limiting example, the time-varying properties of the object 1104 are achieved using deformations from an object of a first type (a star-shaped object) to an object of a second type (a circular object). In another non-limiting example, the time-varying property of the object 1104 is achieved by rendering a mixed shape into an appropriate combination of an object of a first type and an object of a second type. FIG. 11C shows that the modification of the time-varying characteristics of one of the objects 1104 rendered to the user interface is a dynamic change of the shape or one of other types of the objects 1104 rendered in the graphical user interface (herein non-limiting In one example, from a star object to a circular object). FIG. 11D shows that the modification of the time-varying characteristics of one of the objects 1106 rendered to the user interface is a dynamic change of one of the patterns or colors or visual characteristics of the object 1106 relative to the environment rendered in the graphical user interface. (In this non-limiting example, from a star-shaped object with a first pattern to a circular object with a second pattern). In another non-limiting example, the time-varying characteristics of an object may be a rate of change drawn on the object or relative to one of the facial expressions of the object. In any of the examples herein, the aforementioned time-varying characteristic may be applied to an object containing a triggering element to modify an emotional load of an individual interacting with a device (eg, a computing device or a cognitive platform).

12A to 12T show one non-limiting example of tasks and interference dynamics that can be realized at the user interface in accordance with the principles herein. In this example, the task is a visual motion navigation task, and the interference is target identification (as a secondary task). The priming element is a rendered face with different facial expressions, and the priming element is part of the interference. The example system is programmed to instruct the individual to perform visual motor tasks and target identification (where a specific facial expression is identified as a response to a priming element). As shown in FIGS. 12A to 12T, an individual needs to perform navigation tasks by controlling the movement of the character avatar 1202 along a path that coincides with the milestone object 1204. FIGS. 12A to 12T show a non-limiting exemplary embodiment in which a device or computing device (or other sensing device) is expected to be actuated in response to a navigation task to cause a character avatar 1202 to coincide with a milestone object 1204, The score is based on the individual's success in traversing the milestone object 804 (eg, hit). In another example, an individual is expected to actuate a device or computing device (or other sensing device) to cause the character avatar 1202 to miss the milestone object 1204, where the score is based on the individual's success in avoiding the milestone object 1204. 12A to 12T also show the dynamics of a non-target object 1206 with a first type of triggering element (a neutral facial expression), where the time-varying characteristic is the motion trajectory of the object. 12A to 12T also show the dynamics of a target object 1208 having a second type of triggering element (a happy facial expression), where the time-varying characteristic is the motion trajectory of the object. 12A to 12T also show the dynamics of another non-target object 1210 with a third type of triggering element (an angry facial expression), where the time-varying characteristic is the motion trajectory of the object.

In the examples of FIGS. 12A to 12T, the processing units of the example systems, methods, and devices are configured to receive data indicative of an individual's physical movements to cause the character avatar 1202 to navigate a path. For example, an individual may need to perform a physical action to "manipulate" a character avatar, for example , by changing a rotational orientation or otherwise moving a computing device. This action can cause a gyroscope, or accelerometer, or other motion or position sensor device to detect movement, thereby providing measurement data indicating the success of the individual in performing navigation tasks.

In the examples of FIGS. 12A to 12T, the processing units of the exemplary systems, methods, and devices are configured to receive data indicative of an individual's physical actions to perform target identification and identify a prescribed triggering element (i.e., a prescribed face expression). For example, prior to an experiment or other work session, responding to the display of a target object with a prescribed triggering element 1208 may instruct the individual to tap or make other physical instructions, and respond to a non-target object 1206 or 1210 The display (based on the type of trigger element) may indicate that the individual does not tap to make a physical indication. In FIGS. 12A to 12C and 12E to 12H, in an interference processing multitasking implementation, target discrimination acts as an interference to one of the main navigation tasks (ie, a secondary task). As set forth above, example systems, methods, and apparatus may cause a processing unit to render a display feature (e.g., display feature 500 in FIGS. 4A-4D) to display instructions to an individual on an expected performance (i.e., which The triggering element will respond and how to perform target identification and navigation tasks). As also set forth above, the processing units of the exemplary systems, methods, and devices may be configured to (i) receive substantially simultaneous instructions when collecting data (for a specified triggering element) that indicates the measurement of an individual's response to the triggering element. Information on the measurement of the extent and type of the entity's response to the main task, or (i) when collecting data indicating the measurement of the extent and type of the entity's response to the task, select substantially simultaneously ( that is , at substantially the same time) Sexually receive data that indicates an entity's response to a prescribed triggering element that is a target stimulus ( i.e. , an obstruction), and collect data that indicates the extent and type of an entity's response to a task substantially simultaneously ( I.e. , at substantially the same time) selectively does not collect an individual's response to an unspecified triggering element that is a non-targeted stimulus ( i.e. , a distraction).

In FIGS. 12A to 12T, a character 1212 containing the word "Good" is rendered near the character avatar 1202 to signal the individual: analysis of data indicating the individual's response to navigation tasks and target discrimination interference including triggering elements Indicates satisfactory performance. The figures show an example of a change in the type of reward presented to an individual as another indication of satisfactory performance, including at least one modification of the role avatar 1202 to symbolize motivation, such as but not limited to ring 1214 or other activities Element, and / or a jet booster element 1216 that exhibits a star shape (and a reward graphic, such as, but not limited to, a "star zone" graphic). Many other types of reward elements can be used, and the rate and type of reward elements displayed can be changed and adjusted as a time-varying element.

13A to 13P show one non-limiting example of tasks and interference dynamics that can be realized at the user interface according to the principles herein. In this example, the task is a visual motion navigation task, and the interference is target identification (as a secondary task). The priming element is a rendered face with different facial expressions, and the priming element is part of the interference. FIG. 13A shows an example display feature 1300 that can be rendered to instruct an individual to perform visual motor tasks and target identification (where a specific facial expression is identified as a response to a priming element). As shown in FIGS. 13A to 13P, the individual needs to perform navigation tasks by controlling the movement of the character avatar 1302 along a path that avoids the milestone object 1304 (ie, does not coincide with the milestone object 1304). 13A to 13P show one non-limiting exemplary embodiment in which a device or computing device (or other sensing device) is expected to be actuated in response to a navigation task in order for a character avatar 1302 to avoid a milestone object 1304, The score is based on the individual's success in not traversing the path (e.g., miss) of the landmark object 1304. 13A to 13P also show the dynamics of a non-target object 1306 with a first type of triggering element (a happy facial expression), where the time-varying characteristic is the motion trajectory of the object. 13A to 13P also show the dynamics of a target object 1308 having a second type of initiating element (an angry facial expression), where the time-varying characteristic is the motion trajectory of the object. 13A to 13P also show the dynamics of another non-target object 1310 with a third type of initiating element (an angry facial expression), where the time-varying characteristic is the motion trajectory of the object.

In the examples of FIGS. 13A-13P, the processing units of the exemplary systems, methods, and devices are configured to receive data indicative of the physical movements of the individual to cause the character substitute 1302 to navigate the path. For example, an individual may need to perform a physical action to "manipulate" a character avatar, for example , by changing a rotational orientation or otherwise moving a computing device. This action can cause a gyroscope, or accelerometer, or other motion or position sensor device to detect movement, thereby providing measurement data indicating the success of the individual in performing navigation tasks.

In the examples of FIGS. 13A to 13P, the processing units of the exemplary systems, methods, and devices are configured to receive data indicative of an individual's physical movements to perform target identification and identify a prescribed triggering element (i.e., a prescribed face expression). For example, before an experiment or other work phase, in response to the display of a target object having a prescribed triggering element 1308, the display feature 1300 may be used to instruct the individual to tap or make other physical instructions, and respond to a non- The display of the target object 1306 or 1310 (based on the type of triggering element) may use the display feature 1300 to instruct the individual not to touch to make a physical indication. In FIGS. 13A to 13P, in an interference processing multitasking implementation, target discrimination acts as an interference to one of the main navigation tasks (ie, a secondary task). As set forth above, example systems, methods, and apparatus may cause a processing unit to render a display feature (e.g., display feature 500 in FIGS. 4A-4D) to display instructions to an individual on an expected performance (i.e., which The triggering element will respond and how to perform target identification and navigation tasks). As also set forth above, the processing units of the exemplary systems, methods, and devices may be configured to (i) receive substantially simultaneous instructions when collecting data (for a specified triggering element) that indicates the measurement of an individual's response to the triggering element. Information on the measurement of the extent and type of the entity's response to the main task, or (i) when collecting data indicating the measurement of the extent and type of the entity's response to the task, select substantially simultaneously ( that is, at substantially the same time) Sexually receive data that indicates an entity's response to a prescribed triggering element that is a target stimulus ( i.e. , an obstruction), and collect data that indicates the extent and type of an entity's response to a task substantially simultaneously ( I.e. , at substantially the same time) selectively does not collect an individual's response to an unspecified triggering element that is a non-targeted stimulus ( i.e. , a distraction).

In various examples, the degree of non-linearity of the accumulated beliefs used to make a single decision ( ie , whether to perform a response) may be adjusted based on the time-varying nature of the adjustment task and / or interference. As a non-limiting example, where time-varying properties are the trajectory, velocity, orientation, or size of one of the objects (target or non-target), an entity can acquire to develop a belief (in order to make a decision about whether to perform a response The amount of information for decision making may initially be small ( e.g. , in this case, making the object more difficult to discern by rendering it farther or smaller), and depends on how quickly the individual can obtain more Multi-information can grow at different rates (non-linearly) in order to develop beliefs ( for example , when objects appear to appear larger in the environment, change orientation, move more slowly, or move closer). Other non-limiting example time-varying properties of tasks and / or disturbances that can be adjusted to modulate the degree of non-linearity accumulated by beliefs include one or more of the following: a face expression rate of change; an object At least one of the colors; the type of the object; a deformation rate of an object of the first type changing to an object of the second type; and a mixed shape of one of the initiating elements (eg, a mixed shape of a facial expression).

Data indicating an individual's response to a task and an individual's response to at least one priming element are used to compute at least one performance metric, which includes at least one quantified indicator of an individual's cognitive ability under emotional load. In a non-limiting example, the performance metric may include the calculated interference cost under emotional load.

The difficulty level of a subsequent session (including the difficulty of tasks and / or disturbances and triggering elements) can be set based on performance metrics calculated for individual performance from a previous session, and can be optimized to modify an individual's performance. Performance measures (eg, reducing or optimizing interference costs under emotional load).

In a non-limiting example, the adaptability of the difficulty of a task and / or interference may be adapted with each different stimulus presented as a priming element.

In another non-limiting example, the example systems, methods, and devices herein may be configured to perform at a fixed time interval or at other settings on a task and / or interference (including triggering elements) at a level of difficulty Perform one or more adaptations, such as, but not limited to, a frequency of every second, at 10-second intervals, every 30 seconds, or at 1 / second, 2 / second, or greater (such as, but not limited to, 30 times / second) .

In one example, the difficulty level of a task or interference can be adjusted by changing the time-varying characteristics or changing the order or balance between presenting a target stimulus and a non-target stimulus, such as, but not limited to, an object A speed, a rate of change of a facial expression, a direction of an object's trajectory, a change of an object's orientation, at least one color of an object, a type of an object, or a size of an object.

In one non-limiting example of a visual motion task (a type of navigation task), one or more of the speed of navigation, the shape of the route (change in turning frequency, change in turning radius), and the number or size of obstacles can be changed In order to modify the difficulty of a navigation game level, the level of difficulty increases as the speed increases and / or the number and / or size of obstacles (milestone objects) increases.

In a non-limiting example, as a feedback, the difficulty level of a task and / or interference at a subsequent level can be changed immediately, for example , the difficulty of a subsequent level can be increased or decreased in relation to the information indicating the performance of the task small.

FIG. 14 shows a flowchart of a non-limiting, exemplary computer-implemented method for generating personalized cognitive processing suggestions for a subject, which may be implemented using one or more processors and a memory. One or more processors execute instructions stored in one or more memory storage devices, the instructions including computer-executable instructions for performing multiple operations. In block 1405, the operation receives parameters for at least one cognitive processing tool. At block 1410, the operation is to receive physiological data indicative of a condition of one of the individuals. In block 1415, the operation is to receive clinical data associated with the individual. In block 1420, a personalized cognitive processing recommendation is generated based on at least one of the physiological data or the clinical data, the recommendation including specifying one of the following: (i) at least one first cognitive processing tool; (ii) different At least one second cognitive processing tool on at least one first cognitive processing tool; or (iii) both (i) and (ii). In block 1430, the operation further includes receiving performance data indicative of individual performance of at least one task associated with the suggested at least one cognitive processing tool, as appropriate.
in conclusion

The embodiments described above may be implemented in any of a number of ways. For example, some embodiments may be implemented using hardware, software, or a combination thereof. When any aspect of an embodiment is implemented at least partially in software, whether provided in a single computer or distributed among multiple computers, the software code can be executed on any suitable processor or processor set.

For this reason, various aspects of the present invention may be at least partially embodied as a computer-readable storage medium (or a plurality of computer-readable storage media) encoded with one or more programs (for example, a computer memory, a compressed disc, Optical discs, magnetic tapes, flash memory, field programmable gate arrays or other semiconductor device circuit configurations, or other tangible computer storage media or non-transitory media), the one or more programs reside in one or more computers The methods implementing various embodiments of the techniques discussed above are executed when executed on other processors. The computer-readable medium or media may be removable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement the invention as discussed above Various aspects of technology.

The term "program" or "software" is used herein in a general sense to refer to any type of computer code or software that can be used to program a computer or other processor to implement various aspects of the technology of the present invention as discussed above. Computer executable instruction set. In addition, it should be understood that according to one aspect of this embodiment, one or more computer programs that execute the method of the present technology when executed need not reside on a single computer or processor, but may be a module The distributed method is distributed among several different computers or processors to implement various aspects of the technology of the present invention.

Computer-executable instructions may be executed by one or more computers or other devices in many forms, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Generally, in various embodiments, the functionality of the program modules may be combined or distributed as required.

Furthermore, the techniques set forth herein may be embodied as a method in which at least one example has been provided. Actions performed as part of a method can be ordered in any suitable way. Accordingly, embodiments may be structured in which actions are performed in an order different from one illustrated, which may include performing certain actions simultaneously, even as shown as sequential actions in the illustrative embodiments.

All definitions as defined and used herein should be understood to govern the definition of a dictionary, the definitions in documents incorporated by reference, and / or the ordinary meaning of the defined terms.

Unless the contrary is explicitly indicated, the indefinite article "a (an and an)" as used herein in the description and the scope of the patent application should be understood to mean "at least one".

The phrase "and / or" as used herein in the description and the scope of the patent application should be understood to mean "either or both" of the elements so connected, that is, in some cases, to Elements that exist in a connected manner and elements that exist in a separate manner in other cases. Multiple elements listed with "and / or" should be construed in the same way, that is, "one or more" of the elements so connected. Elements other than those specifically identified by the "and / or" clause may exist as appropriate, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to one of "A and / or B" when used in conjunction with an open-ended language such as "includes" may refer to only A in one embodiment (including except for B as appropriate). Other elements); in another embodiment may refer to only B (contains elements other than A as appropriate); in yet another embodiment may refer to both A and B (contains other elements as appropriate) Wait.

As used herein in the specification and the scope of the patent application, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" should be interpreted as inclusive, that is, containing at least one of several elements or a list of elements (but also including One or more) and optionally additional items not listed. The term "only" clearly indicates the opposite, such as "only one of" or "exactly one of" or "consisting of" when used in the context of a patent application Refers to a number of elements or exactly one element in a list of elements. In general, the term "or" as used herein is preceded by an exclusive term (such as "any", "one of ...", "only one of ..." or ".. The "exactly one of them") should only be interpreted as indicating an exclusive choice (ie, "one or the other and not both"). When used in the context of a patent application, "consisting essentially of" shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and the scope of the patent application, the phrase "at least one" with respect to a list of one or more elements should be understood to mean any one or more of the elements selected from the list of elements. At least one element, but not necessarily each and every one of the elements specifically listed in the list of elements, and any combination of elements in the list of elements is not excluded. This definition also allows elements other than those specifically identified in the list of elements referred to by the phrase "at least one" to be present, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, "at least one of A and B" (or equivalently, "at least one of A or B", or equivalently, "at least one of A and / or B" (A) "may refer to at least one (including more than one as appropriate) A in one embodiment, and B does not exist (and optionally include elements other than B); in another embodiment, may refer to At least one (contains more than one as appropriate) B, and A is absent (and optionally includes elements other than A); in yet another embodiment, it can mean at least one (contains more than one as appropriate) A and at least One (including more than one if appropriate) B (and optionally other elements); etc.

In the scope of the patent application and in the above description, all transitional phrases (such as "include", "include", "carry", "have", "include", "relate", "hold", "by" "Construct" and the like) should be understood as an opening form, that is, including, but not limited to. Only transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, such as the United States Patent Office Manual of Patent Examining Procedures) as set out in Section 2111.03.

10‧‧‧ closed loop system

12‧‧‧ interface

14‧‧‧cognitive processing engine

16‧‧‧ closed loop repeat / closed loop 2 repeat

20‧‧‧data / output

50‧‧‧ Closed Loop System / Example Closed Loop System

52‧‧‧Trained monitoring component / monitoring component / example monitoring component / example training component

54‧‧‧Process generation component / personalized processing component / example personalization processing component / example trained personalization processing component / trained personalization processing component / processing personalization component / trained processing personalization component

55‧‧‧output / output data

56‧‧‧ closed loop repeat / closed loop 1 repeat

58‧‧‧Practitioner

60‧‧‧patient registration form

62‧‧‧Element / Output Data / Transfer Point

200‧‧‧ Example Equipment / Equipment

202‧‧‧Memory / Example Memory

204‧‧‧Processing Unit / Example Processing Unit / Configurable and / or Programmable Processor / Processor

206‧‧‧ processor executable instructions

208‧‧‧Computing component

210‧‧‧ Information

212‧‧‧nData

310‧‧‧ Exemplary Computing Device / Computing Device

312'‧‧‧ processor

314‧‧‧Associated Core

314'‧‧‧Associated Core

318‧‧‧multi-touch interface / keyboard

320‧‧‧ indicator device

322‧‧‧Interface

324‧‧‧Virtual Machine

326‧‧‧operating system

328‧‧‧Visual Display Unit

332‧‧‧Network Device

334‧‧‧Storage / Exemplary Storage

336‧‧‧Single-core processor or multi-core processor

340‧‧‧software applications

500‧‧‧ display characteristics

502‧‧‧Measurement characteristics

504‧‧‧Guide for character avatars or other processors

506‧‧‧ Object / Target Object

508‧‧‧ Objects / non-target objects

510‧‧‧ Milestone Object

602‧‧‧Character stand-in

604‧‧‧ Milestone Object

606‧‧‧Target Object

608‧‧‧ Non-target

702‧‧‧Character stand-in

704‧‧‧ interference

710‧‧‧Environment

712‧‧‧Internal route / route

714‧‧‧ Obstacles / strategically located obstacles

716‧‧‧ Character avatars or other navigable elements

718‧‧‧ Strategically placed shaped objects

719‧‧‧ Entrance

720‧‧‧Environment / Example Landscape / Example Environment

722‧‧‧ route

724‧‧‧obstacles

726‧‧‧ Character avatar or other navigable element

729‧‧‧ Origin

740‧‧‧path

742‧‧‧turn

744‧‧‧ dotted arrow

746‧‧‧ dotted arrow

800‧‧‧ Example entrance

802‧‧‧ head up display

902‧‧‧ Exemplary route

904‧‧‧ obstacle

906‧‧‧ wall

908‧‧‧Target shaped object

910‧‧‧ Non-limiting exemplary head-up display

912‧‧‧Head-up display

914‧‧‧ cone

916‧‧‧ cube

918‧‧‧Ring

1005‧‧‧Happy expression / very happy face

1006‧‧‧Neutral expression / Neutral face

1007‧‧‧Angry / Extremely Angry Face

1010‧‧‧ happy

1012‧‧‧ happy face

1014‧‧‧ Neutral

1016 ‧ ‧ anger

1018‧‧‧Example display features / display features

1100‧‧‧ Object

1102‧‧‧ Object

1104‧‧‧ Object

1106‧‧‧ Object

1202‧‧‧Character Double

1204‧‧‧ Milestone Object

1206‧‧‧ Non-target

1208‧‧‧Target object / Required trigger element

1210‧‧‧ Non-target

1212‧‧‧ Features

1214‧‧‧circle

1216‧‧‧jet booster element

1300‧‧‧Example display features / display features

1302‧‧‧Character stand-in

1304‧‧‧ Milestone Object

1306‧‧‧ Non-target

1308‧‧‧Target Object / Required Trigger Element

1310‧‧‧ Non-target

1405‧‧‧box

1410‧‧‧box

1415‧‧‧box

1420‧‧‧box

1430‧‧‧box

A‧‧‧starting point / point / origin

B‧‧‧ target position / target point / end point / point

C‧‧‧ target end point / point

O ₁ ‧‧‧ obstacle

O ₂ ‧‧‧ obstacle

S ₁ ‧‧‧ target shaped object

S ₂ ‧‧‧ target shaped object

S ₃ ‧‧‧ target shaped object

S ₄ ‧‧‧ target shaped object

α ₁ ‧‧‧Width

α ₂ ‧‧‧Width

α ₃ ‧‧‧ length

Δα‧‧‧Δ angle

θ1‧‧‧ angle

Those skilled in the art will understand that the figures illustrated in this article are for illustration purposes only. It should be understood that in certain instances, various aspects of the described embodiments may be shown exaggerated or enlarged to facilitate understanding of one of the described embodiments. In the drawings, similar component symbols generally refer to similar features, functionally similar, and / or structurally similar components throughout the various drawings. The drawings are not necessarily drawn to scale, but rather focus on illustrating the principles of this teaching. These drawings are not intended to limit the scope of the teachings of the present invention in any way. Systems and methods can be better understood with reference to the following diagrams in accordance with the following illustrative illustrations, in the diagrams:

FIG. 1 is a schematic diagram illustrating the generation of a personalized cognitive processing proposal according to the principles in this article.

FIG. 2 is a block diagram of an exemplary computing device according to the principles herein.

FIG. 3 is a block diagram of an exemplary computer system according to the principles herein.

Figures 4A to 4D show an example user interface with instructions to a user according to the principles herein, which instructions can be implemented into an example user interface.

5A to 5T show examples of tasks and disturbances being performed at a user interface in accordance with the principles herein.

6A-6D show examples of tasks and disturbances being performed at a user interface in accordance with the principles herein.

7A to 7D show non-limiting examples of computerized rendering of a route to present navigation tasks in accordance with the principles herein.

8A-8C illustrate a computerized rendering of an entrance to an environment of a non-limiting example navigation task in accordance with the principles herein.

9A-9U show views of various portions of a computerized rendering of an environment, one of a non-limiting example navigation task according to the principles herein.

10A to 10B show examples of a triggering element and a user interface including instructions for user interaction according to the principles herein.

11A-11D show examples of time-varying features of an example object (target or non-target) that is realizable to an example user interface according to the principles herein.

12A to 12T show one non-limiting example of tasks and interference dynamics that can be realized at the user interface in accordance with the principles herein.

13A to 13P show one non-limiting example of tasks and interference dynamics that can be realized at the user interface according to the principles herein.

FIG. 14 is a flowchart of an exemplary method according to the principles herein.

Claims (45)

A system for generating personalized cognitive processing suggestions for a body, the system including: One or more processors; and A memory for storing processor-executable instructions and communicatively coupled with the one or more processors, After the processor-executable instructions are executed by the one or more processors, the one or more processors are configured to: a) receiving parameters for at least one cognitive processing tool; b) receiving at least one of physiological data indicative of a condition of the individual or clinical data associated with the individual; and c) generating the personalized cognitive processing recommendation based on at least one of the physiological data or the clinical data, the recommendation including specifying one of the following: (i) at least one first cognitive processing tool; (ii) different At least one second cognitive processing tool in the at least one first cognitive processing tool; or (iii) both (i) and (ii). The system of claim 1, wherein the clinical information is obtained from at least one patient registration form. The system of claim 1, wherein the one or more processors are further configured to: Receive performance data indicative of individual performance of at least one task associated with the proposed at least one cognitive processing tool. The system of claim 3, wherein the personalized cognitive processing proposal is further based on the received performance data. If the system of claim 1, further comprising: d) repeating steps b) and c) after the individual executes the personalized cognitive processing recommendation, wherein the information received during the repetition of step b) is included in association with the at least one cognitive processing tool of the recommendation Information collected after the individual performance of at least one task. The system of claim 5, wherein the one or more processors are further configured to: Monitoring a state of the condition of the individual based on analysis of at least one of the physiological data, the clinical data, or data instructing the individual to interact with at least one cognitive monitoring tool; The information received during the repetition of step b) includes information indicative of the state of the condition based on the monitoring. The system of claim 1, wherein generating the personalized cognitive processing proposal includes using a prediction model that is trained using a plurality of training data sets, each training data set corresponding to one of the plurality of individuals previously classified An individual, and each training data set includes information representing at least one indicator of the cognitive ability of the classified individual and information indicating a diagnosis of a state or progress of the condition of the classified individual. The system of claim 7, wherein the prediction model includes at least one of a linear / logistic regression, principal component analysis, a generalized linear mixed model, a random decision forest, a support vector machine, or an artificial neural network. The system of claim 1, wherein the condition comprises multiple sclerosis or lupus. The system of claim 1, wherein the condition includes dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, autism spectrum disorder, 16p11.2 repetition , Attention deficit hyperactivity disorder, sensory processing disorder (SPD), mild cognitive impairment, Alzheimer's disease, schizophrenia, depression or anxiety. The system of claim 1, wherein the one or more processors are further configured to: An output is generated that indicates (i) the onset probability of one of the conditions of the individual, (ii) one of the progression stages of the condition, or (iii) one of a combination thereof. The system of claim 1, wherein the one or more processors are further configured to: Monitoring a state of the condition of the individual based on at least one of the physiological data, the clinical data, or data instructing the individual to interact with one of the at least one cognitive processing tool. The system of claim 1, wherein the at least one cognitive processing tool comprises at least one of an interference processing tool, a spatial navigation tool, or an emotion processing tool. The system of claim 13, wherein the proposal includes an interference processing tool, and the one or more processors are further configured to: Generate a user interface; A first instance of a task with an interference is presented at the user interface via the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response The first instance of the task is presented via the user interface, and therefore requires a second response from the individual to the first instance of the task without the interference; among them: At least one of the first instance of the task and the interference includes a computerized element; The first response from the entity to the first instance of the task and the response to the interference from the entity are measured substantially simultaneously; Receiving information indicating the first response and the second response; and The analysis indicates the data of the first response and the second response to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual. The system of claim 14, wherein the one or more processors are configured to present the task as a continuous visual motion tracking task, and wherein the first execution of the task is a system of one of the continuous visual motion tasks. One time interval. The system of claim 14, wherein the one or more processors are configured to present the interference as a target discrimination interference via the user interface. The system of claim 13, wherein the proposal includes a space navigation tool, and the one or more processors are further configured to: Generate a user interface; Presenting, via the user interface, a first task that requires navigation through a prescribed route of an environment; A first indicator is presented via the user interface, the first indicator being configured to navigate from a starting point in the environment to a target ending point with or without input from the individual. The prescribed route; The user interface is configured to display to the individual a command to perform a second task, which requires the individual to: (i) navigate at least one of the prescribed routes in reverse, or (ii) at least a second time Navigate at least part of the prescribed route at one time; A second indicator is presented via the user interface, the second indicator being configured to navigate in the environment in response to the individual's physical movements to control one of the following: (i) the first The relative direction of one of the two indicators, or (ii) the moving speed of one of the second indicators, or (iii) (i) and (ii) both to perform the second task; Obtaining measurement data by measuring data indicating the physical movement of the individual to control the second indicator while performing the second task; and The measurement data is analyzed to generate a performance metric related to the performance of the second task, the performance metric providing an indication of the cognitive ability of the individual. If the system of claim 17, wherein generating the performance metric includes considering at least one of the following: a total time spent successfully completing the second task; an incorrect turn by the second indicator A number; a number of incorrect movement directions made by the second indicator; or a degree of deviation of the route navigated by the user from the prescribed route in the second task. The system of claim 13, wherein the suggestion includes an emotion processing tool, and the one or more processors are further configured to: Generate a user interface; A first instance of a task with an interference is presented at the user interface via the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response and a response from the individual to one of the at least one triggering element; among them: At least one of the first instance of the task and the interference includes the at least one triggering element; Substantially measuring (i) the first response from the individual to the first instance of the task and (ii) the response from the individual to the at least one triggering element, thereby providing the individual with an emotional load A measure of emotional processing capacity; Receiving information indicating the first response and the entity's response to the at least one triggering element; and The analysis indicates the first response and the data of the response of the individual to the at least one triggering element to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual under emotional load. The system of claim 19, further comprising an actuating component, wherein the one or more processors are further configured to control the actuating component to generate an auditory stimulus, a tactile stimulus, or a vibrational stimulus, and wherein the triggering The element includes at least one of the auditory stimulus, the tactile stimulus, or the vibrational stimulus. The system of claim 19, further comprising one or more sensor components, wherein the one or more processors are configured to control the one or more sensor components to measure individual performance indicative of the task Information. The system of claim 21, wherein the one or more sensor components include a gyroscope, an accelerometer, a motion sensor, a position sensor, a pressure sensor, an optical sensor, At least one of a video camera, an audio sensor, or a vibration sensor. The system of any one of the preceding claims, wherein the system is at least one of a virtual reality system, an augmented reality system, or a mixed reality system. A computer-implemented method for generating personalized cognitive processing suggestions for a subject, the method comprising: Use one or more processors to execute instructions stored in one or more memory storage devices. These instructions include computer-executable instructions to perform the following operations, including: a) receiving parameters for at least one cognitive processing tool; b) receiving at least one of physiological data indicative of a condition of the individual or clinical data associated with the individual; and c) generating the personalized cognitive processing recommendation based on at least one of the physiological data or the clinical data, the recommendation including specifying one of the following: (i) at least one first cognitive processing tool; (ii) different At least one second cognitive processing tool in the at least one first cognitive processing tool; or (iii) both (i) and (ii). The method of claim 24, wherein the clinical information is obtained from at least one patient registration form. The method of claim 24, wherein the operations further include: Receive performance data indicative of individual performance of at least one task associated with the proposed at least one cognitive processing tool. The method of claim 26, wherein the personalized cognitive processing proposal is further based on the received performance data. The method of claim 24, wherein the operations further include: d) repeating steps b) and c) after the individual executes the personalized cognitive processing recommendation, wherein the information received during the repetition of step b) is included in association with the at least one cognitive processing tool of the recommendation Information collected after the individual performance of at least one task. The method of claim 28, wherein the operations further include: Monitoring a state of the condition of the individual based on analysis of at least one of the physiological data, the clinical data, or data instructing the individual to interact with at least one cognitive monitoring tool; The information received during the repetition of step b) includes information indicative of the state of the condition based on the monitoring. The method of claim 24, wherein generating the personalized cognitive processing proposal includes using a prediction model that is trained using a plurality of training data sets, each training data set corresponding to one of the plurality of individuals previously classified An individual, and each training data set includes information representing at least one indicator of the cognitive ability of the classified individual and information indicating a diagnosis of a state or progress of the condition of the classified individual. The method of claim 30, wherein the prediction model includes at least one of a linear / logistic regression, principal component analysis, a generalized linear mixed model, a random decision forest, a support vector machine, or an artificial neural network. The method of claim 24, wherein the condition comprises multiple sclerosis or lupus. The method of claim 24, wherein the condition includes dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, autism spectrum disorder, 16p11.2 repetition , Attention deficit hyperactivity disorder, sensory processing disorder (SPD), mild cognitive impairment, Alzheimer's disease, schizophrenia, depression or anxiety. The method of claim 24, wherein the operations further include: An output is generated that indicates (i) the onset probability of one of the conditions of the individual, (ii) one of the progression stages of the condition, or (iii) one of a combination thereof. The method of claim 24, wherein the operations further include: Monitoring a state of the condition of the individual based on at least one of the physiological data, the clinical data, or data instructing the individual to interact with one of the at least one cognitive processing tool. The method of claim 24, wherein the at least one cognitive processing tool comprises at least one of an interference processing tool, a spatial navigation tool, or an emotion processing tool. The method of claim 36, wherein the proposal includes an interference processing tool, and the operations further include: Generate a user interface; A first instance of a task with an interference is presented at the user interface via the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response The first instance of the task is presented via the user interface, and therefore requires a second response from the individual to the first instance of the task without the interference; among them: At least one of the first instance of the task and the interference includes a computerized element; The first response from the entity to the first instance of the task and the response to the interference from the entity are measured substantially simultaneously; Receiving information indicating the first response and the second response; and The analysis indicates the data of the first response and the second response to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual. The method of claim 37, wherein the task is presented as a continuous visual motion tracking task, and wherein the first execution of the task is a first time interval of the continuous visual motion task. The method of claim 37, wherein the interference is presented as a target discrimination interference. The method of claim 36, wherein the proposal includes a space navigation tool, and the operations further include: Generate a user interface; Presenting, via the user interface, a first task that requires navigation through a prescribed route of an environment; A first indicator is presented via the user interface, the first indicator being configured to navigate from a starting point in the environment to a target ending point with or without input from the individual. The prescribed route; The user interface is configured to display to the individual a command to perform a second task, which requires the individual to: (i) navigate at least one of the prescribed routes in reverse, or (ii) at least a second time Navigate at least part of the prescribed route at one time; A second indicator is presented via the user interface, the second indicator being configured to navigate in the environment in response to the individual's physical movements to control one of the following: (i) the first The relative direction of one of the two indicators, or (ii) the moving speed of one of the second indicators, or (iii) (i) and (ii) both to perform the second task; Obtaining measurement data by measuring data indicating the physical movement of the individual to control the second indicator while performing the second task; and The measurement data is analyzed to generate a performance metric related to the performance of the second task, the performance metric providing an indication of the cognitive ability of the individual. The method of claim 40, wherein generating the performance metric includes considering at least one of: a total time spent successfully completing the second task; and an incorrect turn by the second indicator A number; a number of incorrect movement directions made by the second indicator; or a degree of deviation of the route navigated by the user from the prescribed route in the second task. The method of claim 36, wherein the suggestion includes an emotion processing tool, and the operations further include: Generate a user interface; A first instance of a task with an interference is presented at the user interface via the user interface, so it is necessary to obtain a first instance of the first instance of the task from the individual in the presence of the interference. A response and a response from the individual to one of the at least one triggering element; among them: At least one of the first instance of the task and the interference includes the at least one triggering element; Substantially measuring (i) the first response from the individual to the first instance of the task and (ii) the response from the individual to the at least one triggering element, thereby providing the individual with an emotional load A measure of emotional processing capacity; Receiving information indicating the first response and the entity's response to the at least one triggering element; and The analysis indicates the first response and the data of the response of the individual to the at least one triggering element to calculate at least one performance metric, the at least one performance metric including at least one quantified indicator of the cognitive ability of the individual under emotional load. The method of claim 42, wherein the operations further include controlling the concertine component to generate an auditory stimulus, a tactile stimulus, or a vibrational stimulus, and wherein the triggering element includes one of the auditory stimulus, the tactile stimulus, or the vibrational stimulus. At least one. The method of claim 42, wherein the operations further comprise controlling one or more sensor components to measure information indicative of the performance of the individual for the task. The method of claim 44, wherein the one or more sensor components include a gyroscope, an accelerometer, a motion sensor, a position sensor, a pressure sensor, an optical sensor, At least one of a video camera, an audio sensor, or a vibration sensor.