KR20200128555A - Cognitive screens, monitors, and cognitive therapy targeting immune-mediated and neurodegenerative disorders - Google Patents

Abstract

Systems and methods for generating personalized cognitive therapy recommendations for individuals. The system includes one or more processors; And a memory for storing processor-executable instructions. Upon execution of the instruction, the one or more processors receive parameters for the at least one cognitive treatment tool; Receive physiological data indicative of the individual's condition and/or clinical data relating to the individual; Generate personalized cognitive treatment recommendations based on physiological and/or clinical data. The recommendation is (i) at least one first cognitive treatment tool, (ii) at least one second cognitive treatment tool different from at least one first cognitive treatment tool, or (iii) both (i) and (ii) Includes the specifications of. Optionally, the one or more processors receive performance data indicative of the individual's performance for one or more tasks related to at least one cognitive treatment tool of the recommendation.

Description

Cognitive screens, monitors, and cognitive therapy targeting immune-mediated and neurodegenerative disorders

relation Cross-reference to application

This application has priority to US Provisional Patent Application No. 62/638,299 filed March 4, 2018 in the name of the invention "cognitive screens, monitors and cognitive treatments targeting immuno-mediated and neuro-degenerative disorders" Claims and benefits therefrom, the entire disclosure of which is incorporated herein by reference in its entirety, including the drawings.

Field of invention

The present disclosure relates to personalized cognitive therapy, such as for immune-mediated and neurodegenerative disorders.

In individuals with conditions such as immune-mediated or neurodegenerative disorders, there are increasing concerns and unmet needs related to baseline evaluation of cognition, short and long term monitoring of cognition, and treatment of cognition. As access to novel pharmacological treatments targeting the underlying inflammatory and neurodegenerative aspects of these diseases increases, concerns related to life-threatening cognitive impairments have become increasingly important. The latter has led mainly to the emergence of orthodontic techniques that are accessible in the clinical setting and lack access compared to the number of patients in need.

Devices, systems and methods are provided for customizing cognitive therapy.

In one aspect, embodiments relate to a system for generating personalized cognitive treatment recommendations for an individual. The system includes one or more processors and a memory that stores processor-executable instructions and is communicatively coupled with the one or more processors. Upon execution of the processor-executable instructions by the one or more processors, the one or more processors: a) receive parameters for at least one cognitive treatment tool; b) receiving at least one of physiological data representing the condition of the individual, or clinical data related to the individual; c) generating personalized cognitive treatment recommendations based on at least one of physiological data or clinical data. The recommendation is (i) at least one first cognitive treatment tool, (ii) at least one second cognitive treatment tool different from at least one first cognitive treatment tool, or (iii) both (i) and (ii) Includes the specifications of.

One or more of the following features may be included in any aspect of any embodiment. Clinical data can be obtained from at least one patient register.

The one or more processors may be further configured to receive performance data indicative of an individual's performance for at least one task related to the at least one cognitive treatment tool of the recommendation.

Personalized cognitive therapy recommendations may be further based on received performance data.

Steps b) and c) may be repeated after the individual performs the personalized cognitive treatment recommendation, and the data received during the repetition of step b) allows the individual to perform at least one task related to at least one cognitive treatment tool of the recommendation. And then include the collected data.

The one or more processors may be further configured to monitor the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data indicative of an interaction of the individual with the at least one cognitive monitoring tool, The data received during the iteration of step b) includes data representing the condition of the condition based on the monitoring.

Generating customized cognitive therapy recommendations may include using predictive models trained using multiple training datasets, each training dataset corresponding to a previously classified individual of multiple individuals, and each training data The set includes data indicative of at least one indicator of cognitive ability of the classified individual and data indicative of a diagnosis of the condition or progression of the condition in the classified individual.

The predictive models are linear/logistic regression, principal component analysis, generalized linear mixed model, random decision forest, and support vector machine. , And/or an artificial neural network.

Conditions may include multiple sclerosis and/or lupus.

Conditions include dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, and autism spectrum disorder. , presence of the l6pll.2 duplication, 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 indicating (i) the likelihood of developing a condition in the individual, (ii) the stage of progression of the condition, or (iii) a combination thereof.

The one or more processors may be further configured to monitor the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data indicative of an interaction of the individual with the at least one cognitive treatment tool.

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

Recommendations may include interference processing tools, and one or more processors may be further configured to generate a user interface. Through the user interface, the first instance of the task can be presented with interference in the user interface, so that if there is interference, it requests a first response from the individual to the first instance of the task. Through the user interface, the first instance of the task can be presented, requesting a second response from the individual to the first instance of the task if there is no interference. At least one of the first instance of the task and the interference may comprise a computerized element. The first response from the individual to the first instance of the task and the response from the individual to the interference can be measured substantially simultaneously. Data representing the first response and the second response may be received. Data indicative of the first response and the second response may be analyzed to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability.

The one or more processors may be configured to present the task as a continuous visual-motor tracking task, and the first instance of the task may be a first time interval of the continuous visual-motor task. One or more processors may be configured to present the interference as target identification interference through a user interface.

Recommendations may include spatial navigation tools, and one or more processors may be further configured to generate a user interface. Through the user interface, a first task can be presented that requires navigation of a specified path through the environment. Through the user interface, a first indicator configured to search for a designated path from an initial point of an environment to a target end point may be presented regardless of whether an individual inputs or not. The user interface may be configured to present a command to the individual to perform a second task, the second task being to the individual (i) navigating backwards at least a portion of a specified route or (ii) at least one additional number of times. Asks to search at least part of the route. Through the user interface, the second indicator controls either (i) the relative direction of the second indicator or (ii) the speed of movement of the second indicator, or (iii) both (i) and (ii) to perform the second task. To explore the environment in response to an individual's body movements. The measurement data can be obtained by measuring data indicative of an individual's body motion in order to control a second indicator in performing the second task. The measurement data can be analyzed to generate a performance metric for the performance of the second task, and the performance metric provides an indication of the individual's cognitive ability.

Generating the performance metric is the total time taken to successfully complete the second task, the number of erroneous turns made by the second metric, the number of erroneous directions of travel made by the second metric, and/or the second compared to the specified path. It may include taking into account the degree of deviation of the user-navigation path in the task.

The recommendation may include an emotion processing tool, and one or more processors may be further configured to generate a user interface. Through the user interface, in the user interface the first instance of the task with interference is presented, in case of interference, the first response from the individual to the first instance of the task and the response from the individual to at least one associative factor. Demand. At least one of the first instance of the task and the interference may include at least one associative element. The first response from the individual to the first instance of the task and the response from the individual to at least one associative factor can be measured substantially simultaneously, providing a measure of the individual's ability to process emotions under the emotional load. Data is received indicative of the first response and the individual's response to at least one associative factor. Data representing a first response and an individual's response to at least one evocative factor are analyzed to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability 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 effect an auditory, tactile, or vibratory stimulus, and the associative component is an auditory stimulus, tactile stimulus. It may include at least one of stimulation or vibration stimulation.

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

One or more sensor components may include a gyroscope, accelerometer, motion sensor, position sensor, pressure sensor, optical sensor, video camera, auditory sensor, and/or 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, embodiments relate to a computer-implemented method for generating personalized cognitive treatment recommendations for an individual. The method includes using one or more processors to execute instructions stored in one or more memory storage devices including computer-executable instructions for performing a task. The task receives parameters for at least one cognitive therapy tool; Receive at least one of physiological data indicative of the individual's condition or clinical data related to the individual; And generating a personalized cognitive treatment recommendation based on at least one of physiological data or clinical data. The recommendation is (i) at least one first cognitive treatment tool, (ii) at least one second cognitive treatment tool different from at least one first cognitive treatment tool, or (iii) both (i) and (ii) Includes the specifications of.

One or more of the following features may be included. Clinical data can be obtained from at least one patient register.

The task may further include receiving performance data indicative of the individual's performance for at least one task related to the at least one cognitive treatment tool of the recommendation.

Personalized cognitive therapy recommendations may be further based on received performance data.

The task may further include repeating steps b) and c) after the individual performs the personalized cognitive treatment recommendation, and the data received during the iteration of step b) may be performed by the individual and at least one cognitive treatment tool of the recommendation. Contains data collected after performing at least one related task.

The task may further comprise monitoring the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data representing an interaction of the individual with at least one cognitive monitoring tool, the step The data received during the repetition of b) contains data representing the condition of the condition based on the monitoring.

Generating customized cognitive therapy recommendations may include using predictive models trained using multiple training datasets, each training dataset corresponding to a previously classified individual of multiple individuals, and each training data The set includes data indicative of at least one indicator of cognitive ability of the classified individual and data indicative of a diagnosis of the condition or progression of the condition in the classified individual.

Predictive models may include linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural networks.

Conditions may include multiple sclerosis and/or lupus.

Conditions include dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, and autism spectrum disorder. , presence of the l6pll.2 duplication, attention deficit hyperactivity disorder, sensory-processing disorder (SPD), mild cognitive impairment, Alzheimer's disease, schizophrenia, depression, and/or anxiety.

The task may further comprise generating an output indicating (i) the individual's likelihood of developing the condition, (ii) the stage of progression of the condition, and/or (iii) a combination thereof.

The task may further include monitoring the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data representing an interaction of the individual with the at least one cognitive treatment tool.

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

Recommendations may include interference processing tools, and one or more processors may be further configured to generate a user interface. Through the user interface, the first instance of the task can be presented with interference in the user interface, so that if there is interference, it requests a first response from the individual to the first instance of the task. Through the user interface, the first instance of the task can be presented and, in the absence of interference, request a second response from the individual to the first instance of the task. At least one of the first instance of the task and the interference may comprise a computerized element. The first response from the individual to the first instance of the task and the response from the individual to the interference can be measured substantially simultaneously. Data representing the first response and the second response may be received. Data indicative of the first response and the second response may be analyzed to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability.

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

Interference may be presented as target identification interference.

Recommendations may include spatial navigation tools, and one or more processors may be further configured to generate a user interface. Through the user interface, a first task can be presented that requires navigation of a specified path through the environment. Through the user interface, a first indicator configured to search for a designated path from an initial point of an environment to a target end point may be presented regardless of whether an individual inputs or not. The user interface may be configured to present a command to the individual to perform a second task, the second task being to the individual (i) navigating backwards at least a portion of a specified route or (ii) at least one additional number of times. Asks to search at least part of the route. Through the user interface, the second indicator controls either (i) the relative direction of the second indicator or (ii) the speed of movement of the second indicator, or (iii) both (i) and (ii) to perform the second task. To explore the environment in response to an individual's body movements. The measurement data can be obtained by measuring data indicative of an individual's body motion in order to control a second indicator in performing the second task. The measurement data can be analyzed to generate a performance metric for the performance of the second task, and the performance metric provides an indication of the individual's cognitive ability.

Generating the performance metric is the total time it took to successfully complete the second task, the number of erroneous turns made by the second metric, the number of erroneous directions of travel made by the second metric, and/or the second task compared to the specified path. In may include considering the degree of deviation of the user-navigation path.

The recommendation may include an emotion processing tool, and one or more processors may be further configured to generate a user interface. Through the user interface, the user interface presents the first instance of the task with interference, in case of interference, the first response from the individual to the first instance of the task and the response from the individual to at least one evocative factor. Demand. At least one of the first instance of the task and the interference may include at least one associative element. The first response from the individual to the first instance of the task and the response from the individual to at least one associative factor can be measured substantially simultaneously, providing a measure of the individual's ability to process emotions under the emotional load. Data is received indicative of the first response and the individual's response to at least one associative factor. Data representing a first response and an individual's response to at least one evocative factor are analyzed to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability under emotional load.

The task may further include controlling an actuation component to exert an auditory, tactile, or vibratory stimulus, wherein the associative element includes at least one of an auditory, tactile, or vibrating stimulus.

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

One or more sensor components may include a gyroscope, accelerometer, motion sensor, position sensor, pressure sensor, optical sensor, video camera, auditory sensor, and/or vibration sensor.

Those skilled in the art will understand that the drawings described herein are for illustrative purposes only. It should be understood that in some instances various aspects of the described implementation may be exaggerated or enlarged to facilitate an understanding of the described implementation. In the drawings, like reference characters generally refer to similar features, functionally similar, and/or structurally similar elements throughout the various drawings. The drawings are not necessarily to scale, but instead focuses on illustrating the principles of the teaching. The drawings are not intended to limit the scope of the present teaching in any way. Systems and methods may be better understood from the following exemplary description with reference to the following figures:
1 is a schematic diagram illustrating the creation of a personalized cognitive treatment recommendation according to the principles herein.
2 is a block diagram of an exemplary computing device in accordance with the principles herein.
3 is a block diagram of an exemplary computer system in accordance with the principles herein.
4A-4D show an exemplary user interface with instructions for a user that can be rendered into an exemplary user interface, in accordance with the principles herein.
5A-5T show examples of rendering of tasks and interferences in a user interface, according to the principles herein.
6A-6D show examples of rendering of tasks and interferences in a user interface, according to the principles herein.
7A-7D show a non-limiting example of a computerized rendering of a course presenting a search task, according to the principles herein.
8A-8C show a computerized rendering of an entry into an environment of a non-limiting exemplary search task, in accordance with the principles herein.
9A-9U show partial views of computerized rendering of the environment of a non-limiting example search task, in accordance with the principles herein.
10A-10B show examples of user interfaces and associative elements including commands for user interaction, in accordance with the principles herein.
11A-11D show examples of time-varying features of an exemplary object (target or non-target) that can be rendered with an exemplary user interface, according to the principles herein.
12A-12T show non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface, according to the principles herein.
13A-13P show non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface, according to the principles herein.
14 is a flow diagram of an exemplary method in accordance with the principles herein.

It should be appreciated that all combinations of concepts discussed in more detail below (unless such concepts contradict each other) are considered to be part of the inventive subject matter disclosed herein. In addition, it should be appreciated that terms expressly used herein, which may appear in any disclosure incorporated by reference, are to be accorded the meaning most consistent with the specific concepts disclosed herein.

The following is a cognitive platform configured to combine with one or more other types of measurement components and to analyze data collected from user interactions with the cognitive platform and/or from at least one measurement of one or more other types of components, and And/or a more detailed description of various concepts and embodiments related to the methods, apparatus, and systems of the present invention, including platform products. As a non-limiting example, the cognitive platform and/or platform product may be configured for cognitive training and/or clinical purposes. Exemplary systems, methods, and devices can be applied to the monitoring and/or treatment of cognition in individuals with immune-mediated or neurodegenerative disorders.

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

In yet another exemplary implementation, the cognitive platform may be separate from and configured to be associated with one or more physiological or monitoring components and/or cognitive testing components.

In any of the examples herein, a cognitive platform and a system comprising a cognitive platform may be configured to present computerized tasks and platform interactions that inform cognitive assessments (including screening and/or monitoring) and/or deliver cognitive therapy. have.

In any example herein, the platform product herein may be formed from, based on, or integrated with the AKILI® platform product by Akili Interactive Labs, Inc. (Boston, MA), which may be used for cognitive evaluation (screening and/or Or to present computerized tasks and platform interactions to inform, including monitoring, or to deliver cognitive therapy.

As the disclosed concepts are not limited to any particular implementation manner, it should be appreciated that the various concepts introduced above and discussed in more detail below may be implemented in any of a number of ways. Examples of specific implementations and applications are provided primarily for illustrative purposes. Exemplary methods, devices, and systems, including cognitive platforms or platform products, are intended to provide data that can be used in the evaluation and/or screening, monitoring, and treatment of an individual. Can be used by health care practitioners.

The present disclosure relates to the growing interest and unmet needs associated with baseline assessment of cognition, short-term and long-term monitoring and treatment in individuals with immune-mediated or neurodegenerative disorders. Techniques for relatively specific diagnosis and sub-classification of the population exist for categories of disorders (such as but not limited to the diagnosis of progressive multiple sclerosis-primary or secondary-versus relapsed multiple sclerosis). Cognitive dysfunction is recognized as a co-morbidity of an immune-mediated or neuro-degenerative disorder, and there is no subclassification based on the cognitive profile of patients suffering from the same immune-mediated or neuro-degenerative disorder, so specific patients with cognitive impairment for other reasons Risk of prescribing an inadequate orthodontic therapy to the patient. As an example, two patients with multiple sclerosis diagnosed with the same type of disease, relapsed multiple sclerosis and undergoing similar or comparable anti-inflammatory treatment, have underlying brain disorders (e.g., lesions, microlesions, and other microstructural disorders). Or functional changes) can be of a different nature and affect different locations or networks across the central nervous system (gray matter or white matter), so you may experience completely different forms of cognitive impairment.

For example, an individual diagnosed with multiple sclerosis may have different types of cognitive profiles depending on the location, nature, and size of the lesions in the central nervous system. As a result of this heterogeneous lesion pattern, the same type and/or sequence of cognitive therapy effective to treat the cognitive dysfunction identified in the cognitive profile of the first individual with an immune-mediated or neurodegenerative disorder was diagnosed as having the same disorder. It may not work for the second individual.

In a non-limiting example, methods, devices, and systems comprising a cognitive platform or platform product may be used to determine a personalized cognitive treatment regimen for an individual with a condition such as, but not limited to, an immune-mediated or neurodegenerative disorder, and / Or can be used as a tool to help monitor the individual's progress as the individual interacts with the cognitive platform according to a customized cognitive therapy regimen. Exemplary tools include classification tools related to deep learning based on support vector machines to find patterns related to clinician-provided data through patient registers, such as written magnetic resonance imaging or oral reporting, integrated using natural language processing. It can be constructed and trained using one or more sets of training data obtained from individuals with known conditions, such as, but not limited to, immune-mediated or neurodegenerative disorders.

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 to be based at least in part.

Exemplary platform products and cognitive platforms according to the principles described herein can be applied to many different types of conditions, including immune-mediated and neurodegenerative disorders such as, but not limited to, multiple sclerosis and lupus.

Exemplary systems according to the principles described herein are 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). ), l6pll.2 presence of duplicates and/or impaired executive dysfunction (attention deficit hyperactivity disorder (ADHD), sensory-processing disorder (SPD), mild cognitive impairment (MCI), Alzheimer's disease, multiple sclerosis, schizophrenia, depression, or It can be applied to many other types of conditions, including, but not limited to, anxiety.

The present disclosure is directed to a computer-implemented device formed from an exemplary cognitive platform or platform product configured to implement software and/or other processor-executable instructions for the purpose of implementing an exemplary closed loop system. In an example, the closed-loop system may be configured to adjust each individual patient's digital cognitive treatment recommendations to tailor the cognitive treatment regimen according to the actual nature of the cognitive impairment associated with each individual patient. Cognitive therapy regimens also depend on data provided by individuals, clinicians, doctors and/or other medical or health care practitioners, as well as individual perceptual-motors and movements resulting from the disease process itself or from current physiological conditions during treatment delivery. /Or can be adjusted for lack of sensory-motor. Such physiological conditions may include fatigue/drowsiness/wakening, or other data obtained through evaluation by the device or self-reported by the patient. Adjustments may also be made to the means of device control/interaction, and non-limiting examples of such inputs include user interfaces or image capture devices (touch-screen or other types of graphical user interfaces) configured to record user interactions. Other pressure sensitive screens, such as, but not limited to a camera), pointing device 320 (e.g., a mouse), camera or other image recording device, microphone or other sound recording device, accelerometer, gyroscope, or tactile sense, It includes a touch, swipe, or other gesture associated with a sensor for vibration, or an audible signal.

In an example, the system is configured to implement a set of algorithms and related methods according to the first set of mathematical algorithms (trained monitoring component 52) and is related to cognitive performance and physiological performance (such as reaction time or targeting ability but Not limited) Implemented in computing devices (such as but not limited to digital smart devices) that generate multiple metrics. This monitoring step differentiates the specific needs of one individual patient according to the distinct physio-pathological context associated with its cognitive impairment.

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 through an interface 12 used by an individual to interact with the cognitive therapy engine 14 for multiple closed loop iterations 16. The closed loop system 50 is based on at least one closed loop iteration 56 of data between the monitoring component 52 and the treatment generating component 54, monitoring to assess and/or monitor the condition of the individual. Implemented via component 52 and treatment creation component 54 to generate personalized cognitive therapy recommendations for the individual. In an example, there are multiple iterations between interface 12 and cognitive therapy engine 14 to implement a continuous adjustment of the level of difficulty of the task presented in interface 12.

Closed loop system 10 may be any one or more of the cognitive tools described herein. The closed loop system 10 is configured for the interface 12 to present one or more tasks generated by the cognitive therapy engine 14 to an individual and/or to present one or more questions or information material to the individual. In a non-limiting example, the cognitive therapy engine 14 presents task(s) to the individual at the interface 12 to include interference processing and/or spatial exploration and/or emotional processing and/or cognitive tools described herein. It may be configured to implement one or more task(s) related to other types of applicable cognitive tools. The interface is also configured to measure data indicative of one or more physical interactions of an individual in performing one or more tasks and/or collect other data indicative of the individual's performance and/or condition. The cognitive treatment engine 14 is also configured to analyze the measured and/or collected data at the interface 12 to generate an indication of an individual's cognitive abilities. In an example, data is collected and analyzed to assess an individual's condition. In another example, the cognitive therapy engine 14 is also configured to adjust the difficulty level of at least one of the task(s) presented in the interface 12, wherein the analysis of the data measured and/or collected in the interface 12 It can be used to provide an indication of changes in an individual's cognitive abilities. In certain exemplary implementations of the closed loop system 10, the cognitive treatment engine 14 also provides one presented to the individual at the interface 12 along with the measured and/or collected data to generate an indicator of the individual's cognitive ability. It can be configured to analyze responses to the above questions.

As shown in FIG. 1, the closed loop system 10 provides the difficulty of one or more tasks presented in the interface 12 based on analysis using the cognitive treatment engine 14 of the data measured and/or collected in the interface 12. It is configured to implement multiple closed loop iterations 16 to adjust the level. In certain instances, coordination can be exerted using a stepwise method.

In an example, interface 12 may be configured as a graphical user interface presented on a display to present one or more tasks. In yet another example, interface 12 may be configured to present one or more tasks based on auditory, vibration, and/or tactile signals. For example, interface 12 may present at least one actuator, tactile unit, or vibration unit, or one or more tasks of a cognitive tool and measure data indicative of an individual's physical or other behavior in interacting with one or more tasks. And/or other similar components for otherwise collecting. In another example, interface 12 is configured to measure and/or otherwise collect data to measure a measure indicative of an individual's physical or other behavior in interacting with one or more tasks using at least one camera or other image capture device. Can be.

Closed loop system 50 is configured to receive data 20 output from closed loop system 10. In the non-limiting example of FIG. 1, the monitoring component 52 is configured to receive the data 20 output from the closed loop system 10. The monitoring component 52 is configured to assess and/or monitor the condition of the individual based at least in part on the analysis of the data 20. The closed-loop system 50 may also include symptoms of the individual's condition, the individual's mood and emotional state, mobility, clinical information about the individual obtained from a healthcare provider (HCP) or other healthcare practitioner (which may include relevant nData). (58), Data collected from questions presented to individuals about nData (such as but not limited to magnetic resonance imaging (MRI), heart rate monitors, thermometers, etc.) from one or more physiological measurement equipment and laboratories can be considered as input. have. A non-limiting example of nData from physiological measurements using MRI is the type, location, and distribution of lesions in brain regions of individuals with multiple sclerosis. For example, nData may include data representing a brain region of an individual with a lesion, such as, but not limited to, a prefrontal cortex, a hippocampal network, an amygdala, a caudal nuclear region of the brain, or an olfactory cortical region of the brain. In the example, the monitoring component 52 receives the input data and configures the input data to build a profile of the individual based on an indication of cognitive abilities received from the closed loop system 10 and other nData received for the individual. Is configured to analyze. The clinical information may include a clinical measure of an individual's symptoms and nData representing a physiological condition of the individual, a clinical scale and a clinician's diagnosis based on nData, and other data.

In an example, the monitoring component 52 may also receive clinical data from one or more patient registers 60. The patient register 60 may receive de-identified data from a hospital or other health network. The patient register collects trend data (such as, but not limited to, deficiency levels, disease progression data, and MRI scans, measures of patient mobility, measures of visual acuity, cognitive ability, etc.) showing the level of biomarkers for individuals and their disease state. It can be used to provide data indicative of a physiological measure of a plurality of individuals including and their disease states. The patient register 60 contains data from multiple patients with markers and progressions that may be associated with some of the data entered into the monitoring component 52.

The monitoring component 52 may be configured as a predictive model based on training computational techniques and machine learning tools using a set of training input data collected from previously classified individuals on known stages or progression of a condition of interest. As used herein, the term “predictive model” includes models that are trained and developed based on models that provide continuous output values and/or models based on individual levels. In any of the examples herein, the predictive model includes a classifier model. Non-limiting examples of computational 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, and/or artificial neural networks.

The exemplary closed-loop system 50 uses trained computational techniques and machine learning tools to convert the predictive model of the monitoring component 52 into an output 20 from the closed-loop system 10 and/or one of the cognitive monitoring tools. It may be configured to apply to received data indicative of an individual's response to one or more other tasks, and/or data from one or more physiological measures, and/or clinical data to generate an output 55. In an example, the predictive model is the degree of onset of the condition, the stage of progression of the condition, the evaluation of the individual's cognitive health, a measure of the individual's performance in performing at least one task of the cognitive tool, the individual's motor function or the state or change of the cognitive condition, It may be configured to generate an output 55 such as, but not limited to, an individual's profile, including data indicative of the type and/or dose of the drug, biologic, pharmaceutical agent, or other treatment regimen that the individual is following. Cognitive monitoring tools can be any cognitive tool that provides data indicating the state of an individual's cognitive ability at a given point in time without applying treatment to the individual or improving the individual's cognition. As a non-limiting example, the cognitive monitoring tool may be a cognitive tool configured to present one or more tasks to be performed by an individual, but with little or no coordination of difficult-level tasks. In an example, the cognitive monitoring tool may be configured to present one or more tasks related to interference handling to the individual with little or no adjustment to the level of difficulty. In any of the examples herein, the cognitive therapy tool may be configured to function as a cognitive monitoring tool to assess an individual's cognitive ability without applying treatment.

Exemplary systems, methods, and devices in accordance with the principles herein are configured for monitoring based on measured data 20 from a plurality of individuals from interactions with one or more tasks presented using the cognitive therapy engine of the closed loop system 10. It can be configured to train element 52. In this example, the training data set includes data measured from previously classified individuals regarding cognitive abilities and indications of physiological conditions. For example, monitoring component 52 may be trained using a plurality of training data sets, where each training data set is associated with an individual previously classified from a group of individuals. Each training data set is based on the classified individual's interactions with the exemplary equipment, systems, or computing devices described herein, using one or more cognitive tool(s) to determine the classified individual's It contains data representing one or more parameters of performance. Exemplary monitoring components 52 may also include data representing the performance of the classified individual on cognitive and/or behavioral tests, and/or the cognitive status of the classified individual, the likelihood of developing a disease or disorder (including performance dysfunction) or Data representing the diagnosis of an ongoing stage can be considered as input.

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

In any of the examples herein, the exemplary trained monitoring component 52 is for a quantifiable assessment of an individual's cognitive abilities and/or an individual's disease or condition condition (e.g., the severity and location of lesions in the brain). It can be used as an intelligent proxy. That is, once the monitoring component 52 is trained, the monitoring component 52 output can be used to provide an indication of the cognitive capabilities of a plurality of individuals without using physiological measures or other cognitive or behavioral assessment tests. In an example, the trained monitoring component 52 may be used as an intelligent proxy to provide an indication of the individual's likelihood of developing a condition, or the stage of progression of the condition. In an example, the trained monitoring component 52 may be used as an intelligent proxy for subsequent measurement of an individual's condition. For example, as shown in FIG. 1, performance metrics and/or changes in disease or condition may be communicated to the HCP or other health care practitioner regarding the individual (with the consent of the individual).

In an example, the trained monitoring component 52 may be used as a biomarker of a particular clinical condition of an individual based on measurements of data from one or more interactions with the individual with cognitive tools.

Closed loop system 50 also receives output 55 from monitoring component 52 and clinical data from one or more patient registers 60 to generate personalized cognitive treatment recommendations (PCTRs) for individuals and 1) a customized treatment component 54 for transferring the element 62 to the closed loop system 10. The patient register includes images from patients who have previously performed scans to measure their nData (including scans of lesions in the brain) and other measures of the individual's clinical and physical condition. Using data received from monitoring component 52 and other data, such as, but not limited to, data from one or more patient registers 60, customized treatment component 54 generates a PCTR. In a non-limiting example, the PCTR is an output specifying a treatment regimen of an individual including a recommendation of a percentage of the time and level of treatment (A _i ) that the individual of one or more cognitive tools or other engines should perform. PCTR is just 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 you can provide recommendations that use more. For example, the PCTR may designate A ₁ % as a first recognition tool, A ₂ % as a second recognition tool, and A ₃ % as a third recognition tool. When the value A _i = 0, the PCTR indicates that the cognitive tool is not used as part of the treatment regimen of the cognitive tool for the individual. As shown in Figure 1, the PCTR output generated by the personalized treatment component 54 for the individual is also sent to the monitoring component 52 to improve monitoring of the condition and progression of the individual and other individuals of interest. To further train and refine the hazard monitoring component 52, the closed loop system 50 performs an iterative process (closed loop iteration 56) between the monitoring component 52 and the customized treatment component 54. It can be configured to exert.

Based on the transmitted PCTR (as shown in element 62 in Fig. 1), the closed loop system 10 can be configured to exert the PCTR. For example, based on the PCTR's specification for A _i for each different type of cognitive tool, the cognitive treatment engine 14 can adjust the difficulty level of the one or more task(s) presented by the closed loop system 10. It is possible to have the interface 12 present one or more tasks related to a specific cognitive tool for a specific duration at a specified level of intensity or quantity.

As a non-limiting example, the PCTR may include a specification for the duration of a regimen with a cognitive tool, the intensity of a treatment session (including adjusting the difficulty level), and the type of cognitive tool to use in the treatment.

The customized treatment component 54 is a training computational technique and machine learning tool that uses a set of training input data collected from previously classified individuals with respect to known stages or progression of the condition of interest and known measures and scores of one or more cognitive tools. It can be configured as a predictive model based on. Non-limiting examples of computational techniques and machine learning tools may include, but are not limited to, linear/logistic regression, principal component analysis, generalized linear mixed models, randomized decision forests, support vector machines, or artificial neural networks.

The exemplary closed loop system 50 uses trained computational techniques and machine learning tools to generate a predictive model of the customized treatment component 54 to output data 55 and/or data from one or more physiological measures and/or It may be configured to apply to clinical data to generate output data 62. In an example, the predictive model is PCTR, the degree of onset of the condition, the stage of progression of the condition, the evaluation of the individual's cognitive health, the individual's motor function or data indicating the state or change of the cognitive condition, a drug, a biologic, a pharmaceutical agent, or an individual. It can be configured to generate output data 62, such as but not limited to data indicative of the type and/or dose of other treatment regimens followed.

Exemplary systems, methods, and apparatus according to the principles herein may be configured to train a customized treatment component 54 based on output 55 from de-identified data from the output of monitoring for a plurality of individuals. have. In this example, the training data set includes monitoring data output generated from previously classified individuals regarding indications of cognitive abilities and physiological conditions. For example, the customized treatment component 54 may be trained using a plurality of training data sets, where each training data set is associated with an individual previously classified from a group of individuals. Each training data set contains data indicative of one or more parameters indicative of the monitored condition of the classified individual. Exemplary customized treatment components 54 may also include data representing a classified individual's performance in cognitive and/or behavioral tests, and/or the onset of a classified individual's cognitive status, disease or disorder (including performance dysfunction). Data representing a possible or advanced stage of diagnosis can be considered as input.

In any example herein, the exemplary trained personalized treatment component 54 provides a quantifiable assessment of the individual's cognitive ability and/or the individual's disease or condition (e.g., the extent and location of the lesion in the brain). It can be used as an intelligent proxy for That is, once the customized treatment component 54 is trained, the customized treatment component 54 output is a treatment that can provide treatment for an individual's cognitive abilities without the use of physiological measures or other cognitive or behavioral assessment tests. It can be used to provide an indication of the type of regimen. In an example, the trained personalized treatment component 54 can be used as an intelligent proxy to provide an indication of the likelihood of a change in the progression of the condition. In an example, the PCTR for an individual may be communicated to the HCP or other health care practitioner for the individual (with the individual's consent).

Training of customized treatment components 54 based on machine learning or other computational techniques may use scores that represent the cognitive ability of individuals using cognitive tools and other aspects (e.g., data from other patients or HCPs), In addition, cognitive tools and data from patients who do not interact with their disease progression can be used to help train the customized treatment component 54 to generate a personalized treatment (PCTR). Based on data from multiple individuals (eg, from thousands of other patients), the monitoring component 52 and/or the customized treatment component 54 can be trained as disease markers. For example, the monitoring component 52 and/or the customized treatment component 54 predicts an individual's disease level based on the individual's score using the cognitive tool, and predicts their cognitive status and their profile. , Regimen duration, intensity, and potentially the type of area affected by the disease state (eg, multiple sclerosis lesions) can be trained to provide predictions of potential efficacy.

In an example, the monitoring component 52 may be trained to analyze an individual's sensors and motor capabilities and monitor how they are affected by the progress of the individual's disease state. The output data 55 from the monitoring component 52 may include performance measures that are transmitted to the HCP or other healthcare practitioner as an alert about the condition of the individual. As an example, the output from monitoring component 52 may indicate that the monitored individual may not benefit from a prescribed medication regimen and/or prescribed cognitive tool regimen, or satisfactorily or prescribed medication regimen and/or prescribed awareness. It may be sent as a warning that improvement may be made at a faster rate than predicted based on the use of the tool regimen.

The performance measure of the monitoring component 52 can be used as a proxy marker and/or biomarker for an individual's condition, triggering an HCP or healthcare practitioner visit schedule, or triggering a re-evaluation or change of a medication regimen prescribed to a patient. For example, if the monitoring component 52 provides an output 55 indicative of changes in an individual's motor function or cognitive state, the HCP or healthcare practitioner may increase the dose of the drug to treat a worsening or ameliorating condition. You can decide whether or not.

As a non-limiting example, the type of tool specified in the PCTR output from the customized treatment component 54 and the percentage/percentage of interactions recommended for a particular cognitive tool are the brain regions of the individual determined to be affected by the lesion and/ Or, it may depend on the type of individual's cognitive abilities determined to be affected. For example, PCTR is determined from nData that a lesion affects the prefrontal cortex or can be located in or near the prefrontal cortex and/or cData or nData is the same, but not limited to working memory and/or executive function. You can specify the percentage amount (including the duration of the interaction) of individuals using cognitive tools implementing interference handling tasks, provided that the ability is indicated to be affected. As another example, PCTR is determined from nData that the lesion may affect or be located in or near the extended hippocampal network of the brain, the caudal nucleus or the olfactory cortical region, and/or cData or nData is a working memory. Individuals using cognitive tools to implement tasks including spatial exploration and/or anecdotal memory components if they indicate that cognitive abilities, such as, but not limited to, spatial memory, motor control, and/or executive functions are affected. You can specify the percentage amount of (including the duration of the interaction). As another example, PCTR is determined from nData that the lesion may affect or be located in or near the amygdala-dependent network, and/or cData or nData is such as, but not limited to, working memory, mood, and depressed states. If it indicates that cognitive abilities that are not affected are affected, then a percentage amount of the individual (including the duration of the interaction) can be specified using a cognitive tool that implements a task that includes emotional processing.

In an example, output data 62 from treatment customization component 54 may be sent to an HCP or other healthcare practitioner as an alert about the individual's condition. As an example, output data 62 from treatment customization component 54, including PCTR, may indicate that the monitored individual may not benefit from a prescribed medication regimen and/or prescribed cognitive tool regimen, or satisfactorily or prescribed. It may be sent as a warning that improvement may be at a faster rate than predicted based on the use of a medication regimen and/or prescribed cognitive tool regimen.

In another example, PCTR can designate different combinations of cognitive tool types in a treatment regimen based on recently occurring lesions in different areas of the brain. The PCTR can target a desired level of cognitive ability and treatment regimen for an individual, including the desired adherence and compliance to the use of cognitive tools. For example, individuals with major depressive disorder may be prescribed a session increase with cognitive tools that implement emotion processing tasks to potentially increase the adherence of treatment to cognitive tools that address depression and other cognitive states.

In some instances, the PCTR measures a specified amount of exposure and duration to cognitive tools such as psychoeducation (educational material on disease processes and coping skills), mindfulness (including meditation, focus on breathing, relaxation, etc.), or cognitive behavioral therapy. Can include. As an example, the mindfulness portion of a therapeutic PCTR regimen can be used as part of a regimen to reduce anxiety or depression to help improve adherence and adherence to other cognitive tool(s). In addition, mindfulness exercises can help reduce fatigue, thereby reducing the amount of time an individual has to spend using other cognitive tools prescribed in PCTR therapy.

The monitoring component 52 (monitoring) includes the evaluation of cognition according to defined tasks in a comparable manner between subjects, and the evolution of customized treatment protocols and specific clinical cognitive assessments and/or biomarkers related to cognitive impairment or disease activity. (E.g., anticipation of an exacerbation event or progression of disease severity or possibly correction of disease). Here, during the health care visit, the health care provider can measure cognitive function and pathophysiology. The input to the monitoring component 52 may be a performance metric generated by the patient during the performance of a defined task as described below. Another input to the monitoring component 52 is good, such as cognitive and depressive symptoms and/or brain lesion load based on magnetic resonance imaging, or immune status based on tissue sampling techniques, using outcome-based tools and clinical interviews. Data obtained by the patient's clinician or other health care provider for mental health such as pathophysiology using gold standards may be included. In some examples, performance-based mechanisms can be validated. Non-limiting exemplary inputs from clinicians or other healthcare providers for systemic lupus erythematosus (SLE) include SLE Disease Activity Index (SLEDAI) physiological markers and SLE Neuropsychiatric Symptoms (SLE-NP) checklist. I can. Another input to the monitoring component 52 may be a healthcare provider drug, biologic, or other treatment information (eg, disease-altering therapy selection and regimen, glucocorticoids, etc.). One of the outputs of the monitoring component 52 may be a customized treatment protocol and/or a customized cognitive profile based on performance for health care provider assessments (validated outcome-based instruments and clinical interviews). Another output of the monitoring component 52 may be a customized sensory-motor profile including perceptual-motor capabilities (eg, vision and hearing/sensitivity, and dexterity). Another output of the monitoring component 52 is good standards such as brain lesion load based on magnetic resonance imaging or immune status based on tissue sampling techniques, as applicable, in addition to information pertaining to the drug, biologic, or other treatment regimen followed by the individual. It may be an evolution of performance for customized task protocols with respect to the profile of pathophysiological information (i.e., clinical data) from health care providers such as. Additional output of the monitoring component 52 may be provided to the health care provider in the form of a performance metric and/or alert when a certain pre-set performance threshold is reached and a clinical reassessment is required (e.g., neurodegenerative In connection with an impending relapse or change in the disease stage).

The output of the monitoring component 52 is used by another set of algorithms and related methods according to a second set of mathematical algorithms (trained treatment customization component 54) to appropriate and adapt the therapeutic intervention to each patient's It can provide customized recommendations that adjust the nature of cognitive interventions to the actual cognitive profile. These recommendations serve as personalized cognitive therapy recommendations (PCTR).

In particular, the treatment customization component 54 (customization of treatment) is the output of the monitoring component 52, e.g., clinical information from the health care provider, the patient's self-reported results, e.g., fatigue experience, perception. It can include several inputs, including cognitive deficits and emotional/emotional states. Another input may be a computing device and a graphical user interface and/or other I/O devices for receiving input from a user, for example a keyboard or any suitable multi-point touch interface 318, pointing device 320 (e.g. For example, a mouse), 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 a physiological measure obtained by at least one actuator. (For example, eye openness %, vocalization, actigraphic marker, etc.).

Still another input to the treatment customization component 54 could be a customized cognitive profile associated with the patient's measured cognitive impairment (including, but not limited to, processing speed and the attentional process of notifications and orientations, as well as execution control). have. Another input to the treatment customization component 54 may be a customized sensori-motor profile that includes perceptual-motor skills (eg, vision and hearing/sensitivity, and dexterity). Finally, another input including neuropsychology, cognition, pathophysiology information, treatment regimen, and disease progression information may come from the patient register.

The PCTR generated by the treatment customization component 54 may include one or more of the following:

A ₁ % Interference treatment for cognitive control and attention, according to the state of the prefrontal cortex;

A ₂ % spatial search and anecdotal memory component, depending on the state of the extended hippocampal network;

A ₃ % emotional evaluation and control, depending on the state of the amygdala-dependent network;

A ₄ % other engines suitable, for example, other executive functions such as working memory and cognitive flexibility; And

A ₅ % Psychological education, mindfulness, cognitive-behavioral training, etc.

For example, patients with recurrent mitigating multiple sclerosis, which are mostly present in the prefrontal cortex, accompanied by lower levels in the dilated hippocampal network, and present no lesion load elsewhere, 60% of patients with interfering treatment and 40% of spatial exploration and anecdotal memory. You can get a PCTR containing the components.

The customized treatment algorithms used in the training of the treatment customization component 54 are, for example, physiological (MRI-brain lesion localization), cognitive performance profile, and complementary performance, respectively, for perceptual or sensory-motor skills and emotional states. It can be based on a -based examination or questionnaire. The treatment customization component 54 also utilizes clinical symptoms including the allocation of key dynamics as well as the level of participation of each individual in digital therapy, i.e., adaptation (including duration) of treatment based on their ongoing efforts beyond mere task time. I can.

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 treatment customization component 54. As used herein, “periodic repetition” may be repeated at regular or irregular time intervals. For example, the closed loop system 50 may be monitored at regular time intervals or irregularly (e.g., based on input of clinical data from HCPs or healthcare practitioners, or for an individual's disease state or performance metric). Initiated based on the indication of the monitoring component 52 for a threshold change in a parameter) input data from the monitoring component 52 to the customized treatment component 54 or the monitoring configuration from the customized treatment component 54 It may be configured to perform an iteration of the exchange of the output of the PCTR to the element 52. The advantage of this closed loop 1 implementation is the rapid adjustment of personalized cognitive treatment recommendations based on incoming updates for cognitive, pathophysiological or other factors, which are based on clinic-only standard treatments, otherwise only yearly or more. It will lead to a revised course of treatment every few months, if not long.

PCTR is provided to a digital treatment engine to treat patients. The PCTR is translated into a series of tasks and tasks that target functions of interest, including cognitive, perceptual, and sensory-motor skills, through interaction with the device and/or in combination with body movements directed by the program.

The PCTR can be manually directed by the health care provider. Data sets, such as from patient register 60, obtained from individuals with known conditions such as immune-mediated or neurodegenerative disorders (e.g., using natural language processing, drugs, biologics, or other treatment regimens). Integrated written magnetic resonance imaging or oral report; disease progression) is not limited to machine learning tools associated with deep learning to find disease profiles and progression patterns that can be associated with neuropsychological outcomes and can be used to optimize cognitive therapy efficacy. It may be used as an exemplary example to train treatment customization component 54. Through training these datasets informed by feedback on patient performance and progression, along with cognitive and/or cognitive therapy participation and/or other factors affecting cognitive therapy efficacy, the treatment customization component 54 is fully PCTR can be provided automatically.

In an example, the PCTR may also include recommendations for use of sensory stimulation with one or more cognitive tools. Sensory stimulation along a specific frequency band may facilitate cognitive therapy and/or help reduce inflammation, for example during active device use or otherwise during sleep. As a non-limiting example, for appropriate sensory stimulation using an approximately 40 Hz gamma frequency pattern, the computing device may use an operating component to present an auditory stimulus or initiate another auditory-based interaction with the user and/or initiate a vibrational stimulus. To present or initiate other vibration-based interactions with the user and/or to present a tactile stimulus or other tactile-based interactions with the user and/or to present a visual stimulus or initiate other visual-based interactions with the user. Can be. Such sensory stimuli may be implemented in a graphical user interface and/or other I/O device for conveying input to the user, e.g., sensors for tactile, vibrating or auditory signals, and/or at least one actuating component. have.

Device control can be adjusted according to a custom sensory-motor profile, for example, instead of tapping a target with a finger, the patient can nod or use voice commands.

The PCTR is transmitted (at transmission point 62 in FIG. 1) and is transmitted by a digital therapy engine (e.g., cognitive therapy engine 14) to each distinct cognitive therapy algorithm (interference processing, spatial search, emotional/emotional, Systemic movements that incorporate cognitive and physical training, complemented with sensory/motor identification (e.g., visual or auditory), fine-muscle motor control training, cognitive-behavioral therapy, mindfulness, psychological education or others), and each patient To construct a tailored cognitive therapy program for the treatment process (stepwise profile, the percentage of each cognitive therapy administered to the patient over the course of treatment (%)), the algorithm and stimulus (type and duration) will be used to recommend the appropriate configuration. I can.

Closed Loop 2 Iterations (16) provides successive or repeated iterations of performance thresholds to achieve any defined task based on real-time performance in order to achieve maximum cognitive advantage by keeping the user at the desired level of difficulty. . For example, the closed loop system 10 may be based on successive or repeated iterations of the exchange of input data, measurement data, or output data between the interface 12 and the cognitive therapy engine 14, and/or data input. May be configured to perform a difficulty adjustment of one or more sessions or tests for at least one of the tasks presented in the interface 12 based on the analysis performed in the cognitive treatment engine 14 or measured in the interface 12.

Performance metrics generated by the device based on the performance of a task recommended by the patient may be provided as input to the monitoring component 52. For example, the exponent of the emotional load, the spatial search ability or the interference processing under the neutrality of memory.

The new adaptive treatment closed loop allows the adjustment of the PCTR to periodic intervals to tailor treatment to the evolving cognitive profile of patients receiving cognitive treatment.

In some embodiments, the health care provider collects appropriate inputs for the treatment customization component 54 and the treatment customization component 54 receives from the HCP his or her interpretation of the inputs and knowledge of various possible treatments. PCTR is calculated based on the collected data. In another embodiment, machine learning techniques may be used to train treatment customization component 54 using data received from the HCP to generate a PCTR.

Exemplary methods, devices and systems are configured to measure data indicative of a user's performance in one or more tasks to provide a user performance metric that can ultimately be used as a disease biomarker. Exemplary tasks may include interference processing tasks, and/or spatial search and memory tasks, and/or emotional/emotional tasks. Exemplary performance metrics derive an assessment of the user's cognitive ability and/or measure the user's response to cognitive therapy, and/or data or other quantitative indicators about the user's condition (including physiological and/or cognitive status). Can be used to provide A non-limiting exemplary cognitive platform or platform product in accordance with the principles herein is a metric calculated based on the cognitive platform and/or data collected from the interaction of an individual with the platform product and/or analysis of that data (and related calculations). Based on the immune-mediated or neurodegenerative disorder, the level of expression of proteins that may be of clinical interest in the condition, and/or cognitive platforms when administering drugs, biologics or other pharmaceutical agents to individuals and/or It can be configured to classify individuals with respect to conditions such as, but not limited to, the potential efficacy of the use of a platform product. Other non-limiting exemplary cognitive platforms or platform products in accordance with the principles herein are calculated based on the cognitive platform and/or data collected from the interaction of an individual with the platform product and/or analysis of that data (and related calculations). Based on the metric, it may be configured to classify individuals with respect to the likelihood and/or stage of development of an immune-mediated or neurodegenerative condition. The immune-mediated condition can be, but is not limited to, multiple sclerosis or lupus.

All performance indicators and/or classifications of an individual regarding the likelihood and/or stage of progression of a neurodegenerative condition in accordance with the principles herein are signals to a medical device, a healthcare computing system, or other device, and/or a healthcare worker, health care provider. It is sent to a practitioner, physical therapist, behavioral therapist, sports medicine practitioner, pharmacist, or other physician to determine changes in the dosage of a drug, biologic, or other pharmacological agent to an individual or to a drug, biologic or other pharmaceutical It may allow formulation of a treatment course for an individual or modify an existing course of treatment, including determining the optimal type or combination of agents.

In any of the examples herein, the platform product or cognitive platform may be configured with any combination of a medical device platform, a monitoring device platform, a screening device platform, a treatment device platform, or other device platform.

The present disclosure also relates to an exemplary system comprising a cognitive platform and a platform product configured to couple with one or more physiological or monitoring components and/or cognitive testing components. In some examples, the system includes a platform product and cognitive platform integrated with one or more other physiological or monitoring components and/or cognitive testing components. In another example, the system is a platform product that is stored separately from and communicates with one or more physiological or monitoring components and/or cognitive testing components to receive data indicative of measurements performed using such one or more components. And cognitive platforms.

As used herein, the term “cData” refers to data collected from measurements of interactions between a user and a platform product or a computer-implemented device formed of a cognitive platform.

The term “nData” as used herein refers to other types of data that may be collected according to 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 may be collected in real time.

In a non-limiting example, nData can be collected from measurements using one or more physiological or monitoring components and/or cognitive testing components. In any examples herein, one or more physiological components are configured to perform physiological measurements. Physiological measurements provide data for quantitative measurements of physiological parameters and/or data that can be used to visualize physiological structures and/or functions.

By way of non-limiting example, nData is a measure of protein formation (e.g., whether proteins form aggregates) in tissues or body fluids (including blood) of an individual and/or in tissues or body fluids (including blood) collected from an individual. Can provide an indicator) of the protein type and/or protein form. In some examples, measurements can be made on tissues and/or body fluids that are extracted in the body or from the brain of an individual. Expression groups can be defined based on the threshold expression level of a protein of clinical interest in a neurodegenerative condition, wherein the measured value of the expression level above a pre-specified threshold defines the first expression group and The measured value of the expression level below the value defines the second expression group. In another example, nData can be neuropsychological or other clinical instrument data.

Reference to “drug” herein is understood to include drugs, biologics and/or other pharmaceutical agents.

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

In another non-limiting example, nData can include any data that can be used to characterize an individual's condition, such as age, gender or other similar data.

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

In any of the examples herein, the one or more physiological components may include any means of measuring physical properties of the body and nervous system, including electrical activity, heart rate, blood flow, and oxygenation levels to provide nData. This may include camera-based heart rate sensing, galvanic skin response measurement, blood pressure measurement, electroencephalogram, electrocardiogram, magnetic resonance imaging, near infrared spectroscopy, voice pattern, actigraphy and/or pupil dilation measurements to provide nData.

Other examples of physiological measurements to provide nData are body temperature using an electrocardiogram (ECG), measurement of the heart or other heart-related functions, electrical activity using an electroencephalogram (EEG), event-related potential (ERP), magnetic resonance. An eye tracking device including a processing unit programmed to determine imaging (MRI), functional magnetic resonance imaging (fMRI), blood pressure, potential of a portion of the skin, galvanic skin response (GSR), magnetic-electroencephalogram (MEG), degree of pupil dilation, or Other optical sensing devices, functional near infrared spectroscopy (fNIRS) and/or positron emission tomography (PET) scanners. EEG-fMRI or MEG-fMRI measurements allow simultaneous acquisition of electrophysiology (EEG/MEG) nData and hemodynamic (fMRI) nData.

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

PET scanners can be used to perform functional imaging to observe metabolic processes and other physiological measures in the body through detection of gamma rays emitted indirectly by positron-emitting radionuclides (tracers). Tracers can be introduced into the user's body using biologically active molecules. Indicators of metabolic processes and other physiological measures of the body can be derived from scans, including computer reconstruction of two-dimensional and three-dimensional images from nData of tracer concentrations. nData may include a measure of tracer concentration and/or a PET image (eg, a two-dimensional or three-dimensional image).

In any example herein, the task may be a spatial search task according to the principles herein. In this example, the computing device is configured to present a frontal aerial view of the landscape including one or more interior courses and obstacles. In this example, portions of the course are configured to include paths and passages that allow traversal of avatars or other guideable elements. The search task requires the individual to create a path to a strategically placed obstacle from an initial point ("A") to at least one target position ("B"). The computing device may be configured to present instructions to the individual to navigate the course. The computing device may also include the value of any of these parameters as a function of time, speed of movement, direction, speed, selection of a navigation strategy, a waiting or delay period before continuing or changing direction in a given direction of the course, or other inactivity. The individual may traverse the course, including specifying and/or controlling one or more of the duration, the time interval to complete the course, and/or the frequency or number of introductions to a bird's eye view or front view (including a map) of the landscape. It may be configured to provide an individual with an input device or other type of control element to enable it.

The computing device may be configured to collect data representing performance metrics that quantify the navigation strategy used by the individual from an initial point (“A”) to reach one or more target points (“B”). For example, a computing device collects data representing, among other measures, an individual's decision to proceed from an initial point ("A") along a dashed or dotted line, the speed of movement, the direction of an avatar or other guideable element. Can be configured to In various examples, performance metrics that can be measured using a computing device include the value of any of these parameters as a function of time, speed of movement, direction, speed, selection of a navigation strategy, continuation or direction in a given direction of the course. May include data indicating the frequency or number of introductions to the waiting or delayed period or other period of inactivity before the change, the time interval to complete the course, and/or a bird's eye view or front view (including a map) of the landscape. . As another non-limiting example, the performance metric may include a measure of the degree of optimization of a route explored by an individual through a course, such as through determining the shortest or near-shortest route through the course.

In another example herein, a task may include one or more activities in which the user is required to participate. One or more of the tasks may be computer-implemented as computer stimuli or interactions (described in more detail below). For targeting tasks, the cognitive platform may request a time-specific and/or location-specific response from the user. For a search task, the cognitive platform may request a location-specific and/or action-specific response from the user. In the case of a memory task, the cognitive platform may request a stimulus-specific, location-specific and/or time-specific response from the user. For facial expression recognition or object recognition tasks, the cognitive platform may request a time-specific and/or location-specific response from the user. For integrated cognitive and full-body motion tasks, the cognitive platform may require stimulus-specific, location-specific and/or time-specific responses along with specific body movements/choreography. A multi-tasking task can include any combination of two or more tasks. In a non-limiting example, a user response to a task, such as, but not limited to, targeting and/or searching and/or facial expression recognition or object recognition task(s) may be recorded using the input device of the cognitive platform. Non-limiting examples of such input devices include user interfaces or image capture devices (such as, but not limited to, touch screens or other pressure sensitive screens, or cameras), including any form of graphical user interface configured to record user interactions. It may include a related touch, swipe, or other gesture. In another non-limiting example, user responses recorded using a cognitive platform for tasks such as, but not limited to, targeting and/or navigation and/or facial expression recognition or object recognition task(s) include a cognitive platform. It may include user actions that cause changes in the location, direction, or movement of the computing device. Such changes in the location, orientation, or movement of the computing device may be recorded using an input device placed on or otherwise coupled to a computing device such as, but not limited to, a sensor. Non-limiting examples of sensors include motion sensors, position sensors, and/or image capture devices (such as but not limited to cameras).

In an exemplary implementation involving a multi-tasking task, the computer device allows the cognitive platform to target and/or seek and/or recognize facial expressions or objects for a short time frame to the user (including in real time and/or substantially simultaneously). It is configured to present two or more different types of tasks, such as, but not limited to, tasks (such as using at least one specially programmed processing unit). The computer device can also be used to collect (at least one specially programmed processing unit) data representing the type of user response received to the multi-tasking task within a short time frame (including in real time and/or substantially simultaneously). Together). In this example, two or more different types of tasks may be presented to an individual within a short time frame (including real-time and/or substantially simultaneously), and the computing device (including real-time and/or substantially simultaneously) It may be configured to receive data indicative of user response(s) to two or more different types of tasks within a time frame.

In some examples, the short time frame can be any time interval at a resolution of up to about 1.0 milliseconds or more. The time interval can be, but is not limited to, any fraction of the duration of periodicity of at least about 2.0 milliseconds until any reasonable end time. Time intervals are approximately 3.0 milliseconds, approximately 5.0 milliseconds, approximately 10 milliseconds, approximately 25 milliseconds, approximately 40 milliseconds, approximately 50 milliseconds, approximately 60 milliseconds, approximately 70 milliseconds, approximately 100 milliseconds or more Can, but is not limited thereto. In another example, the short time frame may be a fraction of a second, about 1 second, between about 1.0 and about 2.0 seconds, or up to about 2.0 seconds, or more, but is not limited thereto.

In some examples, the platform product or cognitive platform may be configured to collect data indicative of a response time of a user response to a task presentation time. For example, the computing device may be configured to provide a smaller or larger response time window for the user to provide a response to a task in a manner in which the platform product or cognitive platform adjusts the difficulty level.

As used herein, the term "computerized stimulus or interaction" or "CSI", whether active or passive, refers to a computerized element presented to a user to facilitate user interaction or other interaction with a stimulus. As a non-limiting example, the computing device may present an auditory stimulus or initiate other auditory-based interactions with a user and/or present a vibrational stimulus or initiate other vibration-based interactions with a user and/or initiate a tactile stimulus. It may be configured to present or initiate other tactile-based interactions with the user and/or to present a visual stimulus or initiate other visual-based interactions with the user.

Any task in accordance with the principles herein may be presented to the user through a computing device, an operating component, or other device used to implement one or more stimuli or other interactive elements. For example, a task may be presented to a user by rendering a graphical user interface to present a computerized stimulus or interaction (CSI) or other interactive element. In another example, the task may be presented to the user as an auditory, tactile, or vibrating computerized component (including CSI) using an actuation component. The description of the use of one or more CSIs in the various examples herein (and analysis of data therefrom) also includes the use of a task that includes one or more CSIs in this example (and analysis of data therefrom).

In an example in which 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 the user. In some examples, the at least one graphical user interface is configured to measure a response as a user interacts with a CSI computerized element rendered using the at least one graphical user interface. In a non-limiting example, the graphical user interface may be configured to activate the CSI computerized element(s), and the graphical user interface may be configured to measure data indicative of the type or degree of user interaction with the platform product. At least one response can be requested. In another example, the graphical user interface may be configured such that the CSI computerized element(s) is passive and presented to the user using at least one graphical user interface but does not require a response from the user. In this example, at least one graphical user interface excludes recorded responses of user interactions, weights data representing responses (e.g., weights responses with a lower or higher value). Or), to measure data representing the user's response to the platform product as a measure of the user's false response (eg, to announce a notification or other feedback to the user about the false response).

In an example, the cognitive platform and/or platform product may be configured as a processor-implemented system, method, or apparatus comprising at least one processing unit. In an example, at least one processing unit may be programmed to render at least one graphical user interface to present a computerized stimulus or interaction (CSI) or other interactive element to the user for interaction. In yet another example, at least one processing unit may be programmed such that the operating component of the platform product exerts an auditory, tactile, or vibrational computerized element (including CSI) to perform stimulation or other interaction with the user. At least one processing unit includes at least one user response based on user interactions with CSI or other interactive elements (such as but not limited to cData) of components of the program product, including responses provided using the input device. Can be programmed to receive data representing. In an example in which at least one graphical user interface is rendered to provide a computerized stimulus or interaction (CSI) or other interactive element to a user, the at least one processing unit is the graphical user interface representing at least one user response. It can be programmed to receive data. At least one processing unit also analyzes cData to measure a measure of an individual's cognitive state and/or analyzes differences in individual performance based on determining differences between user responses (including based on differences in cData). And/or adjust the difficulty of auditory, tactile, or vibrating computational elements (including CSI), CSI or other interactive elements based on cData analysis (including measures of individual performance as determined in the analysis), and/or It may be programmed to provide output or other feedback from a platform product that may represent cognitive evaluation and/or response to cognitive treatment and/or an evaluated measure of cognition. In a non-limiting example, the at least one processing unit is also based on cData collected from the interaction of the individual with the cognitive platform and/or platform product and/or a metric calculated based on the analysis (and related calculations) of that cData. Thus, the level of expression of protein(s) that may be of clinical interest in an immune-mediated or neurodegenerative condition, condition and/or cognitive platform and/or platform product when an individual is administered a drug, biologic or other pharmaceutical agent. Can be programmed to classify individuals with respect to the potential efficacy of their use. In a non-limiting example, the at least one processing unit is also based on cData collected from the interaction of the individual with the cognitive platform and/or platform product and/or a metric calculated based on the analysis (and related calculations) of that cData. Thus, it can be programmed to classify individuals for likelihood and/or stage of development of an immune-mediated or neurodegenerative condition. The neurodegenerative condition can be, but is not limited to, lupus or multiple sclerosis.

In an example, the at least one processing unit renders at least one graphical user interface to present CSI or other interactive elements and/or the operating components of the platform product are auditory, tactile, or vibrating computerized elements (including CSI) or connected. Exercising a device (e.g., goggles, earphones, haptic device or other) to reduce inflammation/immunity and/or frequency-band accompaniment of the brain during cognitive therapy via a graphical user interface and/or regardless of graphical user interface interactions. It can be programmed to exert a stimulus to the user's passive consumption as presented in a pattern intended for response regulation.

Exemplary systems, methods and apparatus in accordance with the principles herein include at least one emotional/emotional to add emotional processing as an explicit component for a task in multi-tasking gameplay (MTG) or single-tasking gameplay (STG). Contains platform products (including APP usage) that use cognitive platforms configured to render elements (EAEs). In one example, EAE is used for a task configured to evaluate cognition or improve emotion-related cognition, and data collected as a measure of user interaction with the EAE rendered by the platform product (including cData) is a graphical user interface. Used for interaction or to determine a measure of cognitive assessment or improvement of a cognitive measure after treatment consisting of the auditory, tactile or vibrational component of the platform product. EAE collects data to measure the impact of emotions on non-emotional cognition, such as by having the graphical user interface render spatial tasks for the user to perform under emotional loads and/or allowing the graphical user interface to manipulate emotions. It may be configured to collect data to measure the impact of non-emotional perceptions on emotions, such as by having to render features that use a measure of risk-taking function. In one exemplary implementation, the graphical user interface (based on the measurement data) identifies the emotion represented by the CSI (based on the measurement data), maintains that identification in working memory, and maintains that identification in the working memory, and this It can be configured to render the task for comparison with the scale.

In yet another example, a platform product may be configured as a processor-implemented system, method or apparatus comprising a display component, an input device, and at least one processing unit. The at least one processing unit may be programmed to render at least one graphical user interface for display on the display component to present a computerized stimulus or interaction (CSI) or other interactive element to the user for interaction. . In yet another example, at least one processing unit may be programmed such that the actuating component of the platform product exerts an auditory, tactile, or vibrational computerized element (including CSI) to exert stimuli or other interactions with the user.

Non-limiting examples of input devices include touch screens, or other pressure-sensitive or touch-sensitive surfaces, motion sensors, position sensors, pressure sensors, joysticks, exercise equipment, and/or image capture devices (such as, but not limited to, cameras). ).

In certain instances, the input device is configured to include at least one component configured to receive input data indicative of the physical motion of the individual(s), wherein the data interacts with a cognitive platform and/or platform product, eg For example, it provides a measure of the body's movements of an individual(s) in performing one or more tasks and/or interfering tasks.

Analysis of an individual's performance may include calculating accuracy, hits, and/or number of failures during the session or from previously completed sessions using the computing device. Another indication that can be used to calculate a performance measure (eg, as a targeting stimulus) is the amount of time it takes an individual to respond after presentation of the task. Other indications may include, but are not limited to, response time, response variation, correct number of hits, missed errors, false alarms, learning rates, spatial deviations, subjective ratings and/or performance thresholds, and the like.

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 can detect different types of interference (e.g., interruption or interruption). present. The computing device is configured to present other types of interference as CSI or other interactive elements that distract the user from the primary task. In case of distraction, the computing device is configured to instruct the individual to provide a primary response to the primary task and not to provide a response (ie, to ignore the distraction). In the case of an interrupter, the computing device is configured to instruct the individual to provide a response as a secondary task, and the computing device is the user's response to the primary task (if responses are collected using more than one input device) (substantially simultaneously). Including), it is configured to obtain data representing the user's secondary response to the interrupter within a short time frame. The computing device is configured to calculate one or more measures of a user's performance in a primary task without interference, a performance with interference as a disturbance, and a performance with interruption as interference. Users' performance metrics can be calculated based on these metrics. For example, the user's performance can be calculated as the cost (performance change) for each type of interference (eg, disruption cost and interrupt/multi-tasking cost). The user's performance level for a task may be analyzed and reported as feedback including feedback on a cognitive platform for use in adjusting the difficulty of the task and/or feedback to individuals about the user's status or progress.

In a non-limiting example, the computing device may also provide a response time and/or an individual's response time to a user's response for a specified period of time or over a specific type of task (including non-target and/or target stimuli, specific types of tasks, etc.). It can be configured to analyze, store, and/or output a statistical measure of performance (eg, the percentage of correct or incorrect responses in the last number of sessions).

In a non-limiting example, the computerized element includes at least one task rendered as a visual task in a graphical user interface or presented as an auditory, tactile, or vibrating task. Each task can be rendered with an interaction mechanism designed to elicit a response from the user after the user is exposed to a stimulus for the purpose of collecting cData and/or nData.

In a non-limiting example, the computerized element comprises at least one platform interactive (gameplay) element of the platform rendered in a graphical user interface or as an audible, tactile or vibrating element of a program product. Each platform interaction (gameplay) element of a platform product contains an interaction mechanism (including in the form of a videogame-like mechanism) or visual (or cosmetic) feature that may or may not be the target of cData and/or nData collection. can do.

As used herein, the term “gameplay” includes user interaction (including other user experiences) with aspects of a platform product.

In a non-limiting example, the computerized element includes at least one element indicating positive feedback to the user. Each element may include an audible and/or visual cue that is emitted to the user indicating success in a task or other platform interaction element, i.e. the user response in the platform product is Success measurement threshold was exceeded.

In a non-limiting example, the computerized element includes at least one element that indicates negative feedback to the user. Each element may contain an audible and/or visual cue that is emitted to the user indicating a failure in the task or platform interaction (gameplay) element, i.e. the user response in the platform product is the success of the task or platform interaction element. The measurement threshold was not met.

In a non-limiting example, the computerized element includes at least one element for messaging, ie, communication with a user other than positive or negative feedback.

In a non-limiting example, the computerized element includes at least one element for indicating reward. The rewarding computer element may be a computer-generated feature that is delivered to the user to increase user satisfaction with CSI and consequently to increase positive user interaction (and thus enjoyment of the user experience).

In a non-limiting example, the cognitive platform can be configured to render multi-tasking interactive elements. In some examples, the multi-tasking interactive element is referred to as multi-tasking gameplay (MTG). The multi-tasking interactive element includes an interaction mechanism configured for the user to participate in several temporally overlapping tasks, ie tasks that may require multiple responses substantially simultaneously from the user.

In a non-limiting example, the cognitive platform can be configured to render single-task interactive elements. In some examples, the single-task interactive element is referred to as single-task gameplay (STG). The single-task interaction element includes an interaction mechanism configured to engage users in a single task at a given time interval.

In accordance with the principles herein, the term “cognitive” or “cognitive” refers to a mental action or process that acquires knowledge and understanding through thinking, experience, and sensations. This includes, but is not limited to, psychological concepts/domains such as executive function, memory, perception, attention, emotion/effect, motor control, and interference processing. An exemplary computer-implemented device in accordance with the principles herein may be configured to collect data representing user interactions with a platform product and calculate metrics that quantify user performance. A quantifier of user performance can be used to provide a measure of cognition (for cognitive evaluation) or to provide a measure of the status or progression of cognitive therapy.

In accordance with the principles herein, the term “treatment” or “treat” means a measurable measure of the user's ability, such as, but not limited to, improvement related to cognition, user's mood, emotional state, and/or level of engagement or attention to a cognitive platform. Refers to any manipulation of CSI in platform products (including APP forms) that result in improvement. The degree or level of improvement can be quantified based on user performance measures as described herein. In an example, the term “treatment” can also refer to therapy.

In accordance with the principles herein, the term “session” refers to a distinct period of time with a clear start and end, during which the user interacts with the platform product to receive an evaluation or treatment from the platform product (including the APP format).

In accordance with the principles herein, the term “evaluation” refers to at least one session of user interaction with a CSI or other feature or element of a platform product. Data collected from one or more assessments performed by a user using a platform product (including APP format) can be used to derive measures of cognition or other quantifiers, or other aspects of user capabilities.

In accordance with the principles herein, the term "emotional load" refers to a cognitive load specifically related to processing emotional information or controlling emotions.

In accordance with the principles herein, the term “cognitive load” refers to the amount of mental resources that a user may have to consume to complete a task. This term can also be used to refer to a challenge or difficulty level of a task or gameplay.

In an example, the platform product includes a computing device configured to present to a user a cognitive platform based on interference processing. In an exemplary system, method, and apparatus for implementing interference processing, at least one processing unit renders at least one first graphical user interface or an operating component generates an audible, tactile, or vibration signal to obtain a first CSI from a user It is programmed to present as the first task requiring a response of the first type. Exemplary systems, methods, and apparatus also include that at least one processing unit renders at least one second graphical user interface or an operating component generates an audible, tactile, or vibrating signal to generate a second CSI in the presence of the first interference. It is configured to present as a first interference with the first task requiring a second type of response from the user to the first task. 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 may be completely different from the first type of response. The at least one processing unit is also based on user interaction (such as but not limited to cData) with the platform product, such as but not limited to, by rendering at least one graphical user interface to receive data. It is programmed to receive data representing a response and a second type of response. The platform product is also configured to receive nData (including nData from measurements of physiological or monitoring components and/or cognitive testing components) representing measurements taken before, during and/or after the user interacts with the cognitive platform. Can be. The at least one processing unit also analyzes cData and/or nData to provide a measure of an individual's state (including physiological and/or cognitive state) and/or between a measure of a user's first type and a second type of response. It can be programmed to analyze differences in individual performance and differences in related nData based on determining differences (including those based on differences in cData). The at least one processing unit is also based on the analysis of cData and/or nData (including a measure of the individual's performance and/or condition (including physiological and/or cognitive condition) as determined in the analysis) for the first task and/or the first. Adjust the difficulty of the first intervention and/or provide output or other feedback from platform products capable of representing an individual's performance, and/or cognitive assessment, and/or response to cognitive therapy, and/or an assessed measure of cognition. Can be programmed to In a non-limiting example, the at least one processing unit is also calculated based on nData and cData collected from an individual's interaction with the cognitive platform and/or platform product and/or the analysis (and related calculations) of that cData and nData. Based on the metric, an immune-mediated or neurodegenerative condition, the level of expression of a protein that may have clinical interest in the condition, and/or a cognitive platform and/or when administering a drug, biologic or other pharmaceutical agent to an individual. Or it can be programmed to classify individuals as to the potential efficacy of the use of the platform product. In a non-limiting example, the at least one processing unit is also calculated based on nData and cData collected from an individual's interaction with the cognitive platform and/or platform product and/or the analysis (and related calculations) of that cData and nData. Based on the metric, it can be programmed to classify individuals as to the likelihood and/or stage of progression of an immune-mediated or neurodegenerative condition. Immune-mediated or neurodegenerative conditions can be, but are not limited to, lupus and multiple sclerosis.

In an example, feedback from an individual's performance difference based on determining the difference between a measure of a user's first type and a second type of response and input from a cognitive platform where nData represents the individual's real-time performance during one or more session(s). Can be used as The data in the feedback is the computational configuration of the computing device to determine the degree of coordination that the cognitive platform fits into the difficulty of the first task and/or the first interference the user interacts with within the same ongoing session and/or in subsequent sessions. Can be used as an input to an element.

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

In an exemplary system, method, and apparatus according to the principles herein based on interference processing, the graphical user interface is a component of interference processing, wherein one of the distinct features of the targeting task to which the user responds serves as an interference factor in interference processing. It is configured to be a feature of the platform that displays the emotion, shape, color and/or location that makes it.

Exemplary systems, methods, and devices in accordance with the principles herein are based on measures nData representing physiological and/or cognitive states (including indicators of neuropsychological disorders). Cognitive platforms and/or platform products configured (including APP use) to increase the accuracy of assessments and effectiveness of treatment by setting baseline metrics. CSI can be used to calibrate the nData component to the individual user dynamics of nData.

Exemplary systems, methods and devices in accordance with the principles herein are configured (including the use of APPs) to detect attention/carelessness, alertness, caution and/or fatigue conditions using nData to optimize delivery of CSI related to treatment or evaluation. Includes cognitive platforms and/or platform products.

Exemplary systems, methods, and devices in accordance with the principles herein are configured to detect specific CSIs related to treatment or evaluation and induce attention through subtle or overt manipulation of CSI using nData analysis with CSI cData (including the use of APP). ) Include cognitive platforms and/or platform products.

Exemplary systems, methods, and devices in accordance with the principles herein include cData and nData with nData within or throughout an evaluation or treatment session to create a user profile of cData and nData (including profiles of ideal, optimal or desired user responses) Cognitive platforms and/or platform products configured to manipulate CSI across or within sessions to use CSI pattern analysis and guide users to copy these profiles.

Exemplary systems, methods, and devices in accordance with the principles herein monitor nData for indicators of parameters related to user engagement and optimize the cognitive load generated by CSI to benefit from treatment in time with optimal engagement. Cognitive platforms and/or platform products configured (including APP use) to maximize the delivery and neuroplasticity of. The term “nerve plasticity” as used herein refers to the targeted reconstruction of the central nervous system.

Exemplary systems, methods, and devices in accordance with the principles herein provide alternate CSI by monitoring nData indicative of anger and/or frustration, or continuous user interaction with cognitive platforms (also referred to as “play”) by release from CSI. Includes cognitive platforms and/or platform products configured to facilitate (including use of APPs).

Exemplary systems, methods and devices in accordance with the principles herein are configured to alter CSI dynamics within or throughout an evaluation or treatment session to optimize nData related to the user's cognition or other physiological or cognitive aspects (using APP Including) cognitive platforms and/or platform products.

Exemplary systems, methods and devices in accordance with the principles herein are configured to adjust (including use of APP) CSI or CSI cognitive load when nData signals of task automation are detected or physiological measurements associated with task learning show signs of attenuation. Includes platforms and/or platform products.

Exemplary systems, methods, and devices in accordance with the principles herein are tailored treatments that combine signals from CSI cData with nData to facilitate improvement of indicators of cognitive ability and cognitive platforms (including APP use) configured to optimize cognition thereby. And/or platform products.

Exemplary systems, methods, and devices in accordance with the principles herein include cognitive platforms and/or platform products configured to verify/verify/authenticate the identity of a user using a profile of nData (including APP usage).

Exemplary systems, methods, and devices in accordance with the principles herein use nData to categorize individual user preferences to personalize CSI to optimize enjoyment and promote continued participation in evaluation or treatment sessions, using positive emotional responses to CSI. And a cognitive platform and/or platform product configured to detect (including APP use).

Exemplary systems, methods, and devices in accordance with the principles herein are the same as, but related to, a user profile that is classified or known to exhibit a user profile of cognitive improvement (improved working memory, attention, processing speed and/or perceptual sensing/identification And/or platform products configured to deliver treatments (including APP use) that create and adjust CSI to optimize the profile of new users as identified by the profile from nData. .

Exemplary systems, methods, and devices in accordance with the principles herein include cognitive platforms and/or platform products configured (including APP usage) to provide a user with a selection of one or more profiles configured for cognitive improvement.

Exemplary systems, methods, and devices in accordance with the principles herein include auditory and visual physiological measurements to detect interference from external environmental sources that may interfere with assessments or treatments performed by a user using a cognitive platform or program product. Cognitive platforms and/or platform products configured to monitor nData from (including APP usage).

Exemplary systems, methods, and devices in accordance with the principles herein use a cognitive platform or program product to detect interference from external environmental sources that may interfere with the evaluation or treatment performed by the user and adjust the platform or product accordingly. A cognitive platform and/or platform product configured to monitor nData from device auditory and visual measurements of the environment in order to inform the user to change environmental conditions or postpone until more appropriate.

Exemplary systems, methods, and devices in accordance with the principles herein use cData and/or nData (including metrics from data analysis) as determinants or users (including patients using medical devices) to treat treatment (cognitive treatment and/or Or a cognitive platform and/or platform product configured to determine (including use of APP) whether or not it is likely to respond to, such as, but not limited to, treatment with a biologic, drug or other pharmaceutical agent. For example, systems, methods, and devices allow users (including patients using medical devices) to be classified into certain groups based on a given individual or population of certain individuals (e.g., immune-mediated or neurodegenerative diseases). Individual(s)) can be configured to select whether to receive treatment based on specific physiological or cognitive measures that can be used as a validated signature to predict efficacy. These exemplary systems, methods and devices configured to perform the assays (and related calculations) described herein can be used as biomarkers to perform monitoring and/or screening. By way of non-limiting example, the exemplary systems, methods, and devices provide awareness of a given individual or population of a particular individual (e.g., individual(s) classified into a given group based on the state of an immune-mediated disease). It is configured to provide a quantitative measure of the degree of efficacy of the treatment (including the degree of efficacy associated with the use of a biologic, drug or other pharmaceutical agent). In some instances, an individual or a specific individual in a population may be classified as having a specific neurodegenerative condition.

A non-limiting exemplary classifier model uses the training cData and the corresponding nData and based on metrics collected from at least one interaction of the user with the exemplary cognitive platform and/or platform product, an individual's immune-mediated or neurodegenerative It can be trained to generate predictors for the state of the disease. Training nData is the immunity of each user corresponding to the cData collected for a given user (equal to, but not limited to, the user's score from at least one interaction with any exemplary cognitive platform and/or platform product). Data indicative of the mediated disease state and age may be included. In some examples, nData may include data representing the user's gender. In another example, the collected nData may indicate compliance or efficacy in consideration of cognitive treatment adjustments. For example, cData may be collected based on limited user interactions with any of the exemplary cognitive platforms and/or platform products herein, for example, on the order of a few minutes. The length of time of the limited user interaction can be, for example, about 5 minutes, about 7 minutes, about 10 minutes, about 15 minutes, about 20 minutes or about 30 minutes. An exemplary cognitive platform and/or platform product may be configured to implement an evaluation session (such as, but not limited to, an evaluation implemented using the Project:EVO™ platform).

Non-limiting exemplary systems, methods and devices in accordance with the principles herein are also immuno-mediated with high accuracy based on measurement data (including cData) from multiple user interactions with exemplary cognitive platforms and/or platform products. A cognitive platform and/or platform product configured to implement an exemplary classifier model configured to identify individuals with a positive state of a disease versus a negative state of an immune-mediated disease. For example, an exemplary classifier model uses the exemplary cognitive platform and/or platform product to convert the baseline performance data at the first moment of user performance of the first evaluation using the exemplary cognitive platform and/or platform product into three ( 3) Based on the measurement data (including cData) compared with the value of the performance data from the user's performance of the follow-up evaluation, an individual with a positive state of immune-mediated disease was identified with an accuracy of about 83%, and about 79%. Can be configured to identify individuals with negative conditions of immune-mediated disease with an accuracy of.

A non-limiting exemplary classifier model in accordance with the principles herein uses training cData and corresponding nData and based on metrics collected from multiple interactions of the user with the exemplary cognitive platform and/or platform product, an individual's immunity- It can be trained to generate predictors for the state of each disease. Training nData may include data representing the state of the immune-mediated disease and the age of each user. In some examples, nData may include data representing the user's gender. Corresponding cData is collected for a given user (equal to but not limited to that user's score from at least one interaction with any exemplary cognitive platform and/or platform product herein). For example, cData may be collected based on a plurality of interaction sessions of a user using any of the exemplary cognitive platforms and/or platform products herein, for example two or more interaction sessions. The length of time of each interaction session can be, for example, about 5 minutes, about 7 minutes, about 10 minutes, about 15 minutes, about 20 minutes, or about 30 minutes. An exemplary cognitive platform and/or platform product may be configured to implement multiple evaluation sessions (such as, but not limited to, evaluations implemented using the Project:EVO™ platform).

As noted above, example systems, methods, and apparatus in accordance with 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. 2 shows an exemplary apparatus 200 in accordance with the principles herein that may be used herein to implement a cognitive platform and/or platform product comprising the classifier model described above. The exemplary apparatus 100 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.

Exemplary memory 202 includes hardware memory, non-transitory tangible media, magnetic storage disks, optical disks, flash drives, computing device memory, random access memory such as, but not limited to, DRAM, SRAM, EDO RAM, and other types of memory. , Or a combination thereof, but is not limited thereto. Exemplary processing unit 204 may include, but is not limited to, a microchip, processor, microprocessor, special purpose processor, application specific integrated circuit, microcontroller, field programmable gate array, any other suitable processor, or combinations thereof. Not limited.

At least one memory 202 is configured to store processor-executable instructions 206 and computing component 208. In a non-limiting example, the computing component 208 is cData received from a cognitive platform and/or platform product associated with one or more physiological or monitoring components and/or cognitive testing components as described herein and/or Or it can be used to analyze nData. As shown in Fig. 2, the memory 202 also includes nData 212 (calculation results from application of an exemplary classifier model, measurement data from measurement(s) using one or more physiological or monitoring components). And/or a response to a task rendered in the graphical user interface of the device 100 and/or a task generated using an audible, tactile, or vibrational signal from an actuating component coupled or integrated with the device 200. It may be used to store data 210 such as, but not limited to, data representing an individual's response to the above task. Data 210 may be received from one or more physiological or monitoring components and/or cognitive testing components coupled or integrated with device 200.

In a non-limiting example, at least one processing unit 204 is a cognitive platform coupled with one or more physiological or monitoring components and/or cognitive testing components as described herein using a computing component 208. And/or execute processor-executable instructions 206 stored in memory 202 to at least analyze the cData and/or nData received from the platform product. At least one processing unit 204 also executes processor-executable instructions 206 stored in memory 202 to apply exemplary classifier models to cDdata and nData to determine the likelihood of developing an immune-mediated or neurodegenerative condition and/ Or it may be configured to generate a calculation result indicating a classification of an individual according to a stage of progression and/or a likelihood of developing a neurodegenerative condition (including an executive dysfunction). The at least one processing unit 204 may also contain cData and/or nData received from a cognitive platform and/or platform product associated with one or more physiological or monitoring components and/or cognitive testing components as described herein. Execute processor-executable instructions 206 to control the unit to transmit a value representing the analysis and/or control the memory 202 to store a value representing the analysis of cData and/or nData.

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

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, computing device 310 is a console that receives user input to implement a computing component, including applying signal detection metrics in a computer-implemented adaptive response-deadline procedure. Can be configured. For clarity, FIG. 3 also refers to and provides more detail to various elements of the example system of FIG. 2. 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 examples. Non-transitory computer-readable media may include one or more types of hardware memory, non-transitory tangible media (e.g., one or more magnetic storage disks, one or more optical disks, one or more flash drives), etc. Not limited. For example, memory 202 included in computing device 310 may store computer-readable and computer-executable instructions or software for performing the tasks disclosed herein. For example, memory 202 may be software configured to perform the various tasks disclosed (e.g., analyzing cognitive platform and/or platform product measurement data and response data, applying exemplary classifier models, or performing calculations). The application 340 can be stored. Computing device 310 also includes a configurable and/or programmable processor 204 for executing computer-readable and computer-executable instructions stored in memory 202 or other programs for controlling software and system hardware. And associated core 314, and, optionally, one or more additional configurable and/or programmable processing units, such as processor(s) 312' and associated core(s) 314' (e.g., In the case of computing devices with multiple processors/cores). Processor 204 and processor(s) 312' may be single core processors or multiple cores 314 and 314', respectively.

Virtualization can be used on the computing device 310 so that the console's infrastructure and resources can be dynamically shared. The virtual machine 324 may be provided to handle processes running on multiple processors so that the process appears to be using only one computing resource and not multiple computing resources. Multiple virtual machines can also be used on one processor.

The memory 202 may include a computing device memory or 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 or flash memory. The memory 202 may also include other types of memory or combinations thereof.

In a non-limiting example, the memory 202 and at least one processing unit 204 may be components of a peripheral device, such as, but not limited to, a dongle (including an adapter) or other peripheral hardware. Exemplary peripherals provide the functionality of any of the exemplary cognitive platforms and/or platform products, apply exemplary classifier models, and implement basic computing to implement any of the exemplary analyzes (including related calculations) described herein. It can be programmed to communicate or otherwise couple with the device. In some examples, the peripheral device is either directly to the underlying computing device (such as but not limited to via a USB or HDMI input) or cable (including coaxial cable), copper wire (including, but not limited to, PSTN, ISDN, and DSL). It can be programmed to communicate or couple indirectly via a fiber optic, or other connector or adapter. In another example, the peripheral device may be programmed to communicate wirelessly (such as but not limited to Wi-Fi or Bluetooth®) with the underlying computing device. Exemplary basic computing devices are smartphones (such as, but not limited to iPhone®, BlackBerry®, Android™-based smartphones), televisions, workstations, desktop computers, laptops, tablets, slates, electronic readers (e-readers). , Digital assistant, or other electronic reader or handheld, portable or wearable computing device, or any other equivalent device, Xbox®, Wii®, or other equivalent form of computing device.

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

Computing device 310 also includes one or more storage devices, such as hard drives, CD-ROMs, or other computer-readable media, for storing data and computer-readable instructions and/or software to perform the tasks disclosed herein. 334) (including a single-core processor or a multi-core processor 336). The exemplary storage device 334 (including a single core processor or multiple core processor 336) may also store one or more databases for storing any suitable information necessary to implement the exemplary system and method. The database may be updated manually or automatically at the appropriate time to add, delete and/or update one or more items in the database.

Computing device 310 is a standard telephone line, LAN or WAN link (e.g., 802.11, T1, T3, 56kb, X.25), broadband connection (e.g., ISDN, Lrame Relay, ATM), wireless connection, CAN (Controller Area Network) or one or more networks, for example, LAN (Local Area Network), MAN (Metropolitan Area Network), through various connections including, but not limited to, some combination of any or all of the above. A network interface 322 configured to interface through one or more network devices 332 to a wide area network (WAN) or the Internet. Network interface 322 may be an embedded network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem, or any type thereof capable of communicating and performing the tasks described herein. It may include any other device suitable for interfacing computing device 310 to a network. In addition, the computing device 310 may be a smartphone (such as, but not limited to, an iPhone®, BlackBerry®, or Android™-based smartphone), a television, a workstation, a desktop computer, a server, a laptop, a tablet, a slate, an electronic reader. (e-reader), digital assistant, or other electronic reader or handheld, portable or wearable computing device, or any other equivalent device, Xbox®, Wii®, or capable of communicating and described herein. It may be any computer device, such as another equivalent form of computing or communication device, that has or may be coupled to sufficient processor power and memory capacity to perform the task. One or more network devices 332 are the same as Wireless Application Protocol (WAP), Transmission Control Protocol/Intemet Protocol (TCP/IP), NetBIOS Extended User Interface (NetBEUI), or Internetwork Packet Exchange/Sequenced Packet Exchange (IPX/SPX). Other types of protocols, but not limited to this, can be used to communicate.

Computing device 310 includes any version of the Microsoft® Windows® operating system, iOS® operating system, Android™ operating system, other releases of Unix and Linux operating systems, versions of Macintosh computer MacOS®, embedded operating system, any It can run any operating system 326, such as an operating system, any open source operating system, any proprietary operating system, or any other operating system that can run on a console and perform the tasks described herein. In some examples, operating system 326 may run in native mode or in a mimic mode. In an example, the operating system 326 may run on one or more cloud machine instances.

In any of the examples herein, adjustments to the task type and/or CSI may be made in real time.

Examples of cognitive tools

Interference

Figures 4A-6D can be rendered using the exemplary systems, methods, and apparatus herein to render tasks and/or interferences (either or both have computer-implemented time-varying elements) for user interaction. A non-limiting example user interface is shown. The non-limiting example user interfaces of Figures 4A-6D may also be used for one or more of the following: display instructions to the individual to perform tasks and/or interference, interact with computer-implemented time-varying elements, and , To collect data representing computer-implemented time-varying factors and individual responses to tasks and/or interference, to show progress metrics, and to provide analytical metrics.

4A-4D show non-limiting example user interfaces rendered using the example systems, methods and apparatus herein. As shown in Figures 4A-4B, the exemplary programmed processing unit includes a display feature 500 for displaying to an individual an instruction to perform a task and/or interference, and a status indicator and/or analysis metric from the progress metric. Metric feature 502 to show the results of applying the analysis to data collected from personal interactions (including responses to tasks/interferences) to provide a user interface (including graphical user interfaces). . In any of the example systems, methods, and apparatus herein, a predictive model can be used to provide an analysis metric that is provided as a response output. In any of the example systems, methods, and apparatus herein, data collected from user interactions can be used as input to train a predictive model. As shown in Figures 4A-4B, exemplary programmed processing units also include, but are not limited to, an individual-controlled (navigation of a path or other environment in a visual-motor task and/or selecting an object in a target identification task). May be used to render an avatar or other processor-rendering guide 504 to a user interface (including a graphical user interface) that should be). As shown in Figure 4B, the display feature 500 performs the navigation task while the user interface (using dashed lines) depicts the type of movement of the avatar or other processor-rendering guide 504 required to perform the navigation task. It can be used to command an individual what is expected to be done. In an example, the search task may include a milestone object 510 that an individual must manipulate to traverse or avoid an avatar to determine a score. As shown in Figure 4C, the display feature 500 is the object(s) 506 and 508 for which the user interface can be rendered to the user interface (one type of object 506 is designated as a target while the user interface Other types of objects 508 that may be rendered to may be used to instruct the individual what is expected to perform the target identification task while depicting the type of object 508 (designated as non-target by erasing, for example, in this example). I can. As shown in Figure 4D, the display feature 500 depicts (using dashed lines) the type of movement of the avatar or other processor-rendering guide 504 that the user interface needs to perform the navigation task and the user interface is the target object. While rendering the object type specified as 506 and the object type specified as non-target object 508, it is expected to perform both the seek task to the primary task and the target identification as the secondary task (i.e., interference). Can be used to command individuals.

Perception (sensing & identification), motor function (sensing & identification), impulse/suppression control, and cognitive domains of visual task memory by analyzing measurement data representing an individual's response to a single-tasking task rendered as a targeting task. Can provide quantitative insight. By analyzing measurement data representing an individual's response to a single-tasking task rendered as an exploration task, it is possible to provide quantitative insight into the cognitive domain of visual movement tracking and motor function. Analyze measurement data representing the individual's response to the primary task (rendered as a navigation task) when there is an interference (rendered as a targeting task) in a multi-tasking task to provide quantitative insights into the cognitive domains of separation attention and interference management. Can provide.

5A-5T show non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface according to the principles herein. In this example, the primary task is the visual-motor exploration task and the interference is target identification (secondary task). As shown in FIGS. 5D, 5i-5k, and 5o-5q, the individual must perform the search task by controlling the motion of the avatar 602 along the path coincident with the milestone object 604. Figures 5A-5T show non-limiting example implementations where an individual is expected to operate an equipment or computing device (or other sensing device) to cause the avatar 602 to match the milestone object 604 as a response to a navigation task. , Where scoring is based on the individual's success in crossing (eg, hitting) the path with the milestone object 604. In another example, an individual is expected to operate the equipment or computing device (or other sensing device) so that the avatar 602 will miss the milestone object 604, and scoring is the individual in avoiding the milestone object 604. Is based on your success. 5A-5C show the dynamics of the target object 606 (a star with a first type of pattern). 5E-5H show the dynamics of a non-target object 608 (star with a second type of pattern). Figures 5i-5t show the dynamics of different parts of the search task, where the individual is expected to guide the avatar 602 to cross the path with the milestone object 604 in the absence of interference (an instance of the secondary task). .

In the example of Figures 5A-5T, the processing units of the exemplary systems, methods, and apparatus are configured to receive data indicative of an individual's body movements to cause the avatar 602 to navigate a path. For example, an individual may have to perform body movements to “adjust” the avatar, for example by changing the direction of rotation or moving the computing device. Such actions may cause a gyroscope, accelerometer, or other motion or position sensor device to detect motion and provide measurement data indicating the degree of success of an individual in performing a discovery task.

In the examples of FIGS. 5A-5C and 5E-5H, the processing units of exemplary systems, methods, and apparatus are configured to receive data indicative of an individual's physical motion to perform a target identification task. For example, the individual may tap before a test or other session to make a tap or other physical indication in response to the display of the target object 606 and not to make a physical indication in response to the display of the non-target object 608. You can be instructed. 5A-5C and 5E-5H, the target identification task acts as an interference to the primary search task (ie, an instance of the secondary task) in an interference processing multi-tasking implementation. As described above, example systems, methods, and apparatus may cause a processing unit to render a display feature to indicate an instruction to an individual about expected performance. Also, as described above, the processing units of the exemplary systems, methods, and devices (i) as data is collected representing a measure of the degree and type of an individual's response to the interference (whether the interference includes a target or a non-target). Receive data representing a measure of the degree and type of an individual's response to a primary task, or (ii) substantially simultaneously as data is collected representing a measure of the degree and type of an individual's response to a task ( That is, at substantially the same time) selectively receiving data representing a measure of the degree and type of an individual's response to interference, including a target stimulus (i.e., an interrupter), and the degree and type of an individual's response to the task. Optionally, data representing a measure of the degree and type of an individual's response to interference, including non-target stimuli (i.e., disturbances), including non-target stimuli (i.e., disturbances) at substantially the same time (i.e., at substantially the same time) as data representing a measure It can be configured not to receive.

6A-6D show other non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface in accordance with the principles herein. In this example, the primary task is the visual-motor exploration task, and the interference (as an instance of the secondary task) is target identification. Similar to Figures 5A-5T, the individual must perform the search task by controlling the movement of the avatar 702 along the path. The individual must provide a response to the task with or without interference 704 (rendered as a target for identification).

In a non-limiting example, the adjustment of the difficulty of the task and/or interference may be adjusted with each different stimulus presented as a computer-implemented time-varying factor.

In yet another non-limiting example, the exemplary systems, methods, and devices of the present disclosure set the difficulty level of the task and/or interference to a frequency of 2 or more times per second, every 10 seconds, every 30 seconds, or once per second. It may be configured to adjust one or more times at fixed time intervals such as but not limited to 30 times, but not limited thereto, or at other set schedules.

In a non-limiting example of a visual-motor task (a type of navigation task), one or more of the search speed, course shape (change frequency of turn, change radius of turn), number of obstacles and/or size modifies the difficulty of the search game level. The difficulty level increases with increasing speed and/or increasing the number and/or size of obstacles (eg, including some milestone objects to avoid or some milestone objects that cross/match).

In a non-limiting example, the level of difficulty of a subsequent level of task and/or interference may also be changed in real time with feedback, e.g., the level of difficulty of a subsequent level may be increased or decreased with respect to data representing the performance of the task. .

In an example, the recorded response to the targeting task may be a touch, swipe, or other gesture associated with the user interface or image acquisition device (including a touch-screen or other pressure sensitive screen or camera) to interact with the user interface, but It is not limited thereto. In another example, the recorded response to the targeting task is recorded using a sensor placed on or otherwise coupled to the computing device (such as, but not limited to, a motion sensor or a position sensor) of a computing device including a cognitive platform. It may be a user action that causes a change in position, direction or movement, but is not limited thereto.

In this and any other examples herein, cData and/or nData may be collected in real time.

In this and any other examples herein, adjustments to the type of task and/or CSI may be made in real time.

quest

7A-7D show non-limiting examples of computerized rendering of a course (path) presenting a search task.

7A is a non-limiting view of a computerized rendering of a course that can be used to present a path-learning task, or a navigation task in accordance with the principles herein, including a relative-orientation task, a way-finding task, or any combination thereof. Show an example. In this example, the computing device is configured to provide an overhead front view of the environment 710 including one or more interior courses 712 and obstacles 714. In this example, portions of course 712 are configured to include paths and passages that allow traversal of user indicators (such as but not limited to avatars or other guideable elements 716). In this example, the environment is rendered with a city-block type structure, but other example environments are included in the present disclosure. The Cartesian axis (x-, y-, z-axis) direction in the environment is used only as a guide for the description of the present disclosure, and is not intended to be limiting to the environment. The example environment also includes a number of strategically placed shaped objects 718 (donuts, spheres, cones, etc.) that allow a user to task a location. In this example, a perspective view of landscapes and obstacles that is sufficiently localized to allow a user to make a choice or decision on a strategy for traversing the course is presented without the benefit of a bird's eye view of the entire course or a significant portion of the course. The search task requires an individual to create a path to an obstacle 714 strategically placed up to at least one of the shaped objects 718 at an initial point. An example environment may include one or more access roads 719 remaining at the same or different locations relative to the environment 710. The computing device presents an instruction to the individual in the inspection stage to display the shaped object 718 to be positioned, and optionally, the obstacle in the environment 710 in the user exploration stage (including the guided path stage or free-exploration stage) (7l4) and can be configured to become familiar with the location and type of the shaped object 718. The computing device may also be used to select the speed of movement, direction, speed, navigation strategy, wait or delay periods or other periods of inactivity before continuing or changing direction in a given direction of the course, time intervals to complete the course, and/or a bird's eye view of the landscape. Or the frequency or number of recommendations for a front view (including as a map), a measure of the accuracy in reproducing a previously learned path (e.g., in one or more inspection steps), and a user indicator at a specific location in the space (predetermined A measure of the user's accuracy in using spatial memory rather than visual cues to orient (including but not limited to) the origin of a pre-specified search path), and/or used to explore and learn new environments. An input device or other type of control element (joystick, steering wheel, button, or other type of control element (joystick, steering wheel, button, or described above) that allows an individual to navigate the course 712, including specifying and/or controlling one or more of the measures of strategy being Other controllers) to the individual. In any of the examples herein, the measured value may include the value of any of these parameters as a function of time. As a non-limiting example, the performance metric may be the shortest or nearest path through the course, the time to complete a task, or a path-learning task, or a relative-orientation task, or a way-finding task, or any combination thereof. It may include a measure of the degree of optimization of the route explored by the individual through the course, such as determining other scoring mechanisms associated with it (as described herein).

In an exemplary implementation, the walls of the environment may be configured with different colors represented by 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 may be a different color, two or more may be the same color, or all may be the same color. The first specific color can be used to mark a wall that crosses the environment's x-axis (for example, color 3 and color 4 are the same), while the second specific color is a wall that crosses the environment's y-axis. Can be used to indicate (for example, color 3 and color 4 are the same).

The computing device may be used to perform a route-learning task, directions task, or combination task in order to reach one or more target locations, landmarks, shaped objects, or endpoints (“B”). It may be configured to collect data from 719 representing performance metrics that quantify the navigation strategy used by the individual (including route, speed, and number of turns and sweeping gaze). For example, the computing device may determine an individual's decision to proceed from an initial point ("A") or driveway 719 along a dashed or dotted line, among other measurements (as described above), speed of movement, user indicators ( Such as, but not limited to, an avatar or other guideable element 716). Data may be collected in one or more inspection steps. Data may also be collected at the exploration stage to provide a criterion or other comparative metric for calculating the scores described herein. In various examples, performance metrics that can be measured using a computing device include the value of any of these parameters as a function of time, speed of movement, direction, speed, selection of a search strategy, continuing in a given direction of the course, or May include data indicating the frequency or number of introductory periods of waiting or delays or other periods of inactivity before changing direction, time intervals to complete the course, and/or aerial or frontal views of the landscape (including maps). have. As another non-limiting example, the performance metric is the shortest or nearest path through the course, the time to complete a task, or a path-learning task, or a relative-orientation task, or a way-finding task, or any of these. It may include a measure of the degree of optimization of the route explored by the individual through the course, such as determining other scoring mechanisms associated with the combination of (as described herein).

As shown in the example of FIG. 7A, course 712 is one or more targets (e.g., shaped objects 718, landmarks, or other desired locations that an individual has been commanded to locate in traversing course 712). ) Can be included. In this example, the performance metric may include a scoring based on the specific type of target located and/or the total number of targets located and/or the time it takes to locate the target. In a non-limiting example, an individual may be instructed to navigate the course 712 such that multiple targets are placed in a specified order. In this example, the performance metric may include scoring based on the number of targets placed in the sequence and/or the time taken to complete the award.

7B shows another non-limiting example of another computerized rendering of an environment 720 that a computing device may render to present a search task in accordance with the principles herein. In this exemplary landscape 720, a portion of the course 722 is defined by an obstacle 724, and user indicators (such as, but not limited to an avatar or other guideable element 726) are taken at the origin 729. It is configured to be able to traverse to a designated target. As described above, the origin 729 may be in the same or different location with respect to the environment between the two inspection steps. As shown in FIG. 7B, the obstacle 724 may have a different cross-sectional shape, such as a substantially square cross-section O ₁ of the obstacle compared to the longitudinal cross-section 0 _{2 of} the obstacle. In this example, a perspective view of landscapes and obstacles that is sufficiently localized to allow a user to make a choice or decision on a strategy to traverse the course is presented without the benefit of a bird's eye view of the entire course or a significant portion of the course. The computing device can be used, among other measurements, such as, but not limited to, designating the origin (or other indication) that the user may need during the inspection phase of the path-learning task, but not limited to the forward or backward movement of the user during the examination phase of the path-learning task. The individual's decision to proceed along the dashed or dotted line, such as but not limited to, and/or the speed of movement, and/or the direction of the user indicator (such as but not limited to an avatar or other guideable element 716). It can be configured to collect data representing. In this example, performance metrics that can be measured using a computing device for a localized landscape are speed of movement, direction, speed, selection of a navigation strategy, waiting or delay period before continuing or changing direction in a given direction of the course, or Other periods of inactivity, the time interval to complete the course, and/or the frequency or number of recommendations for a bird's eye view or front view (including as a map) of the landscape, the previously learned path (e.g., in one or more inspection steps). Measures of accuracy in reproduction, in using spatial memory rather than visual cues to orient user indicators to (including trends) to a specific location in space (such as but not limited to the origin of a predefined pre-specified navigation path). And/or data representing a measure of the accuracy of the user's accuracy, and/or a measure of the strategies used to explore and learn new environments. In any of the examples herein, the measured value may include the value of any of these parameters as a function of time. As another non-limiting example, the performance metric may include a measure of the degree of optimization of a route explored by an individual through a course, such as but not limited to determining the shortest route or the near-shortest route through the course. .

The exemplary environment 720 includes a multi-target shaped object S _{i (} i = 1, 2, 3, 4) where an individual is commanded to be positioned in traversing the course 722 from the origin 729. In this example, the performance metric may include a scoring based on success in locating a particular target object, the number of targets located (including from multiple inspection steps) and/or the time it took to locate the target(s). have. In a non-limiting example, an individual may be instructed to navigate the course 722 such that multiple targets are placed in a specified order. In this example, the performance metric may include scoring based on the number of targets placed in the sequence and/or the time taken to complete the sequence.

In an exemplary way-finding task, the computing device is shown in a more localized perspective view (hereafter shown in FIGS. It can be configured to present the individual the ability to change to, but not limited to, the same perspective view.

As a non-limiting example implementation of the way-finding task, a bird's eye view as shown in Figs. 7A or 7B may be presented to the individual to obtain an overview of the course, but thereafter the more localized view shown in Figs. 9A-9U below. You have to navigate the course from the perspective view. In this example, the individual navigates the course by making selections and decisions from more localized perspective views similar to those shown in FIGS. 9A-9U below based on the spatial memory that the individual forms from the wider aerial views of FIGS. 7A or 7B. To do this, you may have to rely on allocentric search capabilities.

7C shows a non-limiting example of the types of dimensional constraints that may be imposed on the dimensions of passages, obstructions and environments. As shown in FIG. 7C, the width α1 of the obstacle is greater than or approximately equal to the width α ₂ of the passage. In a non-limiting example, α ₁ is twice α ₂ . The width α ₁ is also less than the length α ₃ of the environment wall so that no part of the environment is rendered inaccessible to the obstacle. In a non-limiting example, α ₁ is about 1/4 or 1/5 of α ₃ . Exemplary proportional values are provided for the relative dimensions (width and length) of passages, obstructions and environmental walls, but there is no intention of limiting other than requiring α ₃ > α ₂ > α ₁ .

7D shows a non-limiting example of a computerized environment, where the path 740 from point A to point B comprises at least one rotation 742 of a discrete angular amount (denoted by angle θ ₁ ). . In a non-limiting example of a task related to path integration (such as but not limited to dead-reckoning), the user must navigate through the path from the initial point A to the target endpoint (C), and from point C to " Use the index to return to "point" or otherwise indicate origin A. In an example, the system is controllable to allow the user to represent any angle within the range of 0° to at least about 180° relative to point C. In another example, the system is controllable to allow the user to represent any angle within the full range of 0° to 360° relative to point C. The measurement of the degree of success of performing the task is a measurement of the delta angle Da between the user's display in the relative direction of the origin (dotted arrow 744) and the actual relative direction of the origin (dotted arrow 746).

As shown in FIG. 7D, in any of the exemplary environments described herein, including in the examples of any of FIGS. 8A-9U below, the search path may include a curved or substantially non-linear portion.

8A-9U illustrate various perspective views of portions of a computerized rendering of an environment during various non-limiting example search tasks in accordance with the principles herein. In this example, the computing device is configured to present a different perspective view of a selected portion of the environment that the individual should navigate from the perspective of a user indicator (such as but not limited to an avatar or other guideable element). The exemplary perspective view is an illustration of navigation through an exemplary environment and should not limit the scope of the present disclosure. The exemplary image represents the type of order of perspective views the user may encounter as the user navigates through the environment.

8A-8C show different perspective views of an exemplary driveway 800 (shown here as lit openings) as a user activates control of a computing device to pass through the driveway to enter the environment. 8A-8C also show examples of types of Heads-up Display (HUD) 802 that a computing device can use to display to the user as the user navigates the environment. In this example, the computing device causes the user to display the "READY TO EXPLORE" command as HUD 802.

9A-9U show non-limiting examples of a series of perspective views of the environment as the computing device enables the user to perform exploration to become more familiar with the environment. In the example of FIG. 9A, portions of the exemplary course 902 are defined by obstacles 904 and walls 906, and as the user explores the environment (such as but not limited to avatars or other guideable elements). It is configured to allow rotation of user indicators. Also shown is an example of a target shaped object 908 (a sphere in this example) that a user may be commanded to be placed in one or more inspection sessions. 9B and 9C show examples of perspective views rendered as a user rotates and moves in the environment by operating a computing device control. 9D-9U show perspective views of the environment as the user moves forward, backward, and rotates an obstacle in the environment. 9D-9U also show that this is an exploration phase and a non-limiting example HUD rendered to the user by the computing device to indicate the amount of time the user is allowed for exploration (whether guided path or free-exploration) 910) Shows not only the display, but also the HUD 912 indicating the time taken as the user navigates through the exploration stage. 9D-9U show other non-limiting example shaped objects located around the environment, including cone 914, cube 916, and donut 918.

In a non-limiting example, an individual may be presented with a perspective view such as shown in FIGS. , May be commanded to perform a search from these unknown locations. As an example of such a navigation task, an individual can use the computing device control to look around, make the most of his or her abilities to determine his current location, and point to a previously explored (and estimated-known) location within the environment. You can do it. Performance metrics for these tasks include the accuracy of the directional response, and the time required to generate this response. As another example of such a search task, an individual may have to move his or her avatar from an unknown location to a presumed-known location in the environment. Performance metrics for these tasks are the time required to reach the target location, and the difference between the path used to reach the target location and one or more optimal paths (e.g., the optimal path determined using mathematical or algorithmic calculations or modeling methods) It may include.

As shown in Figures 9A-9U, the relative dimensions of the passages, obstacles and environmental walls are α ₃ > α ₂ > α _{1 (} as described in connection with FIG. 7C) and the user presented in the perspective view cross-channel Or is configured to obstruct observing the contents of an adjacent passage until it is within a certain distance of rotation. As a non-limiting example, the α ₃ :α ₂ :α ₁ dimension may be related in a ratio of 10:2:1.

Emotion processing

As described herein, using the exemplary systems, methods, and devices herein is to improve the cognitive skills of individuals under emotional loads based on the science of brain plasticity, from one user session to another (or one user session). It may be implemented to coordinate tasks and/or interferences (at least one including an associative element) from user tests to other user tests. Adaptability is a beneficial design factor for an effective plasticity-utilizing tool. In an exemplary system, method, and apparatus, the processing unit controls parameters of task and/or interference, such as, but not limited to, the timing, location, and characteristics of the stimulus so that the individual's body motions can be recorded during the interaction(s). Is configured to As described above, an individual's body movements are affected by their neural activity while interacting with a computing device to perform single-tasking and multi-tasking tasks. The science of interference processing (based on results from physiological and behavioral measurements) is that the aspect of adaptability causes changes in the individual's brain in response to multiple sessions (or tests) of training based on neuroplasticity, thereby altering the individual's cognitive skills. Show that you can improve. Exemplary systems, methods, and apparatus are configured to implement tasks and/or interferences with at least one associative element, wherein the individual performs interference processing under an emotional load. As supported by the results of the open study described above, the impact on individuals performing tasks under emotional loads can utilize new aspects of cognitive training to improve individuals' cognitive abilities.

10A-13P are non-limiting examples that may be rendered using the exemplary systems, methods and apparatus herein to render tasks and/or interferences (one or both of which have evocative elements) for user interaction. Shows an exemplary user interface. The non-limiting example user interfaces of Figures 10A-13P may also be used for one or more of the following: presenting to the individual an instruction to perform a task and/or interference, interacting with an associative element, and/or task and/or Or to collect data representing an individual's response to interference and evocative factors, to show progress metrics, and to provide analytical metrics.

10A-10B show examples of associative elements (targeted or non-targeted) that can be rendered into an exemplary user interface in accordance with the principles herein. 10A shows an example of an associative element rendered with different types of facial expressions, including a facial expression of static incentive (happiness) and a facial expression of negative incentive (anger). For example, the associative element may be rendered as a face with a happy expression 1005, a neutral expression 1006, or an angry expression 1007. FIG. 10A also shows different degrees of facial expression with a gradual decrease in the degree of happiness from a very happy face 1005 (the highest degree) to a neutral face 1006 and also a very angry face 1007 (the highest degree). Degrees) to neutral faces (1006), showing a modulation of the expression of an associative factor showing different degrees of facial expressions, gradually reducing the degree of anger, each potentially eliciting different levels of emotional emotional response in the individual Raises. 10B shows an exemplary user interface with associative elements rendered with different types of facial expressions (happy expression 1010, neutral expression 1014, angry expression 1016). FIG. 10B also shows an exemplary display feature 1018 for displaying commands to an individual to perform tasks and/or interferences and interact with an associative element. In the non-limiting example of FIG. Can be used to command individuals.

11A-11D illustrate examples of features of object(s) (targeted or non-targeted) that can be rendered as time-varying properties for an exemplary user interface, in accordance with the principles herein. 11A shows an example in which the modification of the time-varying characteristic of the aspect of the object 1100 rendered in the user interface is a dynamic change in the position and/or speed of the object 1100 with respect to the environment rendered in the graphic user interface. 11B is a diagram illustrating the size and/or direction of a trajectory/motion, and/or a direction of the object 1102 with respect to the environment rendered in the graphic user interface. Shows an example of dynamic change. 11C shows an example in which the modification to the time-varying characteristic of the aspect of the object 1104 rendered on the user interface is a shape or other type of dynamic change of the object 1104 with respect to the environment rendered on the graphical user interface. In this non-limiting example, the time-varying properties of the object 1104 are affected using morphing from an object of the first type (star object) to an object of the second type (round object). In another non-limiting example, the time-varying properties of object 1104 are affected by rendering the blendshape as a proportional combination of an object of a first type and an object of a second type. 11C shows an example in which the modification to the time-varying characteristic of the aspect of the object 1104 rendered on the user interface is a shape or other type of dynamic change of the object 1104 rendered on the graphical user interface (in this non-limiting example, , Star object or round object). 11D shows an example in which the modification of the time-varying characteristic of the aspect of the object 1106 rendered in the user interface is a dynamic change in the pattern, color or visual feature of the object 1106 with respect to the environment rendered in the graphical user interface ( In this non-limiting example, a star object having a first pattern to a round object having a second pattern). In another non-limiting example, the time-varying characteristic of an object may be a rate of change in facial expressions compared to or depicted in the object. In any of the examples herein, the time-varying properties described above may be applied to an object that includes an associative element to modify the emotional load of an individual's interaction with the device (eg, computing device or cognitive platform).

12A-12T show non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface according to the principles herein. In this example, the task is a visual-motor exploration task, and the interference is target identification (as a secondary task). The associative element renders the face with a different expression, and the associative element is part of the interference. The exemplary system is programmed to instruct the individual to perform a visual-motor task and target identification (along with identification of specific facial expressions in response to the associative element). As shown in FIGS. 12A to 12T, an individual must perform a search operation by controlling the movement of the avatar 1202 along a path coincident with the milestone object 1204. 12A-12T are scoring based on the individual's success in crossing (e.g., hitting) a path with a milestone object 804 by an individual, wherein the avatar 1202 and the milestone object 1202 are It shows a non-limiting example implementation that is expected to operate the equipment or computing device (or other sensing device) to match. In another example, an individual may activate the equipment or computing device (or other sensing device) to cause the avatar 1202 to miss the milestone object 1204 with scoring based on the individual's success in avoiding the milestone object 1204. Is expected. 12A-12T also show the dynamics of a non-target object 1206 with a first type of associative element (neutral facial expression), where the time-varying characteristic is the trajectory of the object's motion. 12A-12T also show the dynamics of a target object 1208 having a second type of associative element (a happy facial expression), where the time-varying characteristic is the trajectory of the object's motion. 12A-12T also show the dynamics of another non-target object 1210 with a third type of associative element (an angry facial expression), where the time-varying characteristic is the trajectory of the movement of the object.

In the example of FIGS. 12A-12T, the processing units of the exemplary systems, methods, and apparatus are configured to receive data indicative of an individual's body motion that causes the avatar 1202 to navigate a route. For example, an individual may have to perform a body action to “adjust” an avatar, for example by changing the direction of rotation or otherwise moving the computing device. Such an operation may allow a gyroscope, accelerometer, or other motion or position sensor device to detect the movement, thereby providing measurement data indicating a degree of success in performing an individual's search task.

In the example of Figures 12A-12T, the processing units of the exemplary systems, methods and apparatus are configured to receive data indicative of an individual's body motions to perform target identification and identify specified associative elements (i.e., specified facial expressions). . For example, an individual does not tap to make a tap or other physical indication in response to the display of a target object having a designated associative element 1208 and to make a physical indication in response to the display of a non-target object 1206 or 1210. You may be instructed to avoid testing or other sessions (based on the type of associative factor). In FIGS. 12A-12C and 12E-12H, target identification acts as an interference (ie, secondary task) to the primary search task in an interference processing multi-tasking implementation. As described above, exemplary systems, methods, and apparatus allow the processing unit to render a display feature (e.g., display feature 500 in Figures 4A-4D) to determine the expected performance (i.e., which evocative element responds). , And how to perform target identification and search tasks) to the individual. Also, as described above, the processing units of the exemplary systems, methods, and devices can be applied to the primary task substantially simultaneously as (i) data representing a measure of an individual's response to an associative factor is collected (for a specified associative factor). Receive data representing a measure of the degree and type of an individual's response to a task, or (i) substantially simultaneously (i.e. substantially the same time) as data is collected representing a measure of the extent and type of an individual's response to a task E) Selectively receive data representing a measure of an individual's response to an associative factor designated as a target stimulus (i.e., an interrupter), and substantially as data representing a measure of the degree and type of an individual's response to a task is collected It can be configured not to selectively collect a measure of an individual's response to a non-designated associative element non-target stimulus (ie, disturbance) at the same time (ie, at substantially the same time).

In Figures 12A-12T, a feature 812 containing the word “GOOD” is rendered near the avatar 1202 to satisfy the analysis of data representing the individual's response to target identification interference, including navigation tasks and evocative elements. Signal to the individual that it represents a natural performance The drawing includes at least one modification to the avatar 1202 symbolizing excitement, such as, but not limited to, ring 1214 or other active element, and/or star-shaped jet booster element 1216 (and “STAR- It shows an example of a change in the type of reward presented to an individual as another indication of satisfactory performance, showing a reward graphic, such as but not limited to the "ZONE" graphic. Several other types of compensation elements can be used, and the proportion and type of compensation elements displayed can be changed and modulated with time-varying elements.

13A-13P show non-limiting examples of the dynamics of interference and tasks that can be rendered in a user interface in accordance with the principles herein. In this example, the task is a visual-motor exploration task, and the interference is target identification (as a secondary task). The associative element renders the face with different facial expressions, and the associative element is part of the interference. 13A shows an exemplary display feature 1300 that may be rendered to instruct an individual to perform a visual-motor task and target identification (with identification of a particular facial expression in response to an associative element). 13A-13P, the individual must perform the discovery task by controlling the motion of the avatar 1302 along the path avoiding (ie, not matching) the milestone object 1304. 13A-13P show that the individual is scoring based on the individual's success in not crossing the path with the milestone object 1304 (e.g., not hitting). It shows a non-limiting example implementation that is expected to operate the equipment or computing device (or other sensing device) to avoid 1204. 13A-13P also show the dynamics of a non-target object 1306 having a first type of associative element (a happy facial expression), where the time-varying characteristic is the trajectory of the object's motion. 13A-13P also show the dynamics of a target object 1308 having a second type of associative element (an angry facial expression), where the time-varying characteristic is the trajectory of the movement of the object. 13A-13P also show the dynamics of another non-target object 1310 with a third type of associative element (an angry facial expression), where the time-varying characteristic is the trajectory of the object's motion.

In the example of FIGS. 13A-13P, the processing units of the exemplary systems, methods, and apparatus are configured to receive data indicative of an individual's body movements that cause the avatar 1302 to navigate a route. For example, an individual may have to perform body movements to "adjust" the avatar, for example by changing the direction of rotation or moving the computing device. Such an operation may allow a gyroscope, accelerometer, or other motion or position sensor device to detect movement, thereby providing measurement data indicating a degree of success in performing an individual's search task.

In the example of Figures 13A-13P, the processing units of the exemplary systems, methods, and apparatus are configured to receive data indicative of an individual's body motions to perform target identification and identify specified associative elements (i.e., specified facial expressions). . For example, the individual does not tap to make a tap or other physical indication in response to the display of the target object having the designated associative element 1308 and to make the physical indication in response to the display of the non-target object 1306 or 1310. It can be instructed before testing or other sessions (based on the type of associative factor). 13A-13P, target identification acts as an interference (ie, secondary task) to the primary search task in an interference processing multi-tasking implementation. As described above, exemplary systems, methods, and apparatus allow the processing unit to render a display feature (e.g., display feature 500 in Figures 4A-4D) to determine the expected performance (i.e., which evocative element responds). , And how to perform target identification and search tasks) to the individual. Also, as described above, the processing units of the exemplary systems, methods, and devices can be applied to the primary task substantially simultaneously as (i) data representing a measure of an individual's response to an associative factor is collected (for a specified associative factor). Receive data representing a measure of the degree and type of an individual's response to a task, or (i) substantially simultaneously (i.e. substantially the same time) as data is collected representing a measure of the extent and type of an individual's response to a task E) Selectively receive data representing a measure of an individual's response to an associative factor designated as a target stimulus (i.e., an interrupter), and substantially as data representing a measure of the degree and type of an individual's response to a task is collected It can be configured not to selectively collect a measure of an individual's response to a non-designated associative element non-target stimulus (ie, disturbance) at the same time (ie, at substantially the same time).

In various examples, the degree of nonlinearity of the accumulation of belief in an individual's decision making (ie, whether or not to execute a response) may be modulated based on adjustment of the time-varying characteristic of the task and/or interference. As a non-limiting example, if the time-varying characteristic is the trajectory, velocity, direction, or size (targeted or non-targeted) of the object, the amount of information available to an individual to develop belief (to make a decision on whether to execute a response) is It can be made smaller initially, and if, for example, objects are rendered more distant or smaller, making them more difficult to identify, depending on how quickly more information becomes available to the individual to develop belief (nonlinearly) It can be increased at a rate (for example, as objects become larger, change orientation, move slowly, or render to appear to move closer in the environment). Another non-limiting example of the time-varying nature of the task and/or interference that can be adjusted to modulate the degree of nonlinearity of the accumulation of beliefs is the rate of change of facial expressions, at least one color of the object, the type of the object, the first type of And one or more of a morphing ratio at which the object is changed to an object of the second type, and a blend shape of an associative element (eg, a blend shape of a facial expression).

Data representing the individual's response to the task and the individual's response to at least one evocative factor is used to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability under an emotional load. In a non-limiting example, the performance metric includes a calculated interference cost under emotional load.

The difficulty level of subsequent sessions (including the difficulty of tasks and/or interferences, and the difficulty of the association factor) can be set based on the performance metrics calculated for the individual's performance from the previous session, to modify the individual's performance metrics. It can be optimized (for example, to lower or optimize the cost of interference under emotional loads).

In a non-limiting example, the adjustment of the difficulty of the task and/or interference may be adjusted with each different stimulus presented as an associative element.

In yet another non-limiting example, the exemplary systems, methods, and apparatuses herein can vary the difficulty level of a task and/or interference (including associative elements) every second, every 10 seconds, every 30 seconds or once per second, twice per second, or It may be configured to adjust one or more times at fixed time intervals, such as, but not limited to, a higher frequency (such as, but not limited to, 30 times per second) or at other set schedules.

In an example, the level of difficulty of a task or interference is the same as but not limited to the speed of the object, the rate of change of the facial expression, the direction of the trajectory of the object, the change of the direction of the object, at least one color of the object, the type of the object, or the size of the object It can be adjusted by changing the properties, or by changing the order or balance of presentation of target versus non-target stimuli.

In a non-limiting example of a visual-motor task (a type of navigation task), one or more of the search speed, course shape (change frequency of turn, change radius of turn), number of obstacles and/or size modify the difficulty of the search game level And/or the difficulty level increases with increasing speed and/or increasing the number and/or size of obstacles (milestone objects).

In a non-limiting example, the level of difficulty of a subsequent level of task and/or interference may also be changed in real time with feedback, e.g., the level of difficulty of a subsequent level may be increased or decreased with respect to data representing the performance of the task. .

14 shows a flow diagram of a non-limiting exemplary computer-implemented method for generating personalized cognitive therapy recommendations for an individual that may be implemented using one or more processors and memory. One or more processors execute instructions stored in one or more memory storage devices, including computer-executable instructions, to perform tasks. At block 1405, the task receives parameters for at least one cognitive therapy tool. At block 1410, the task receives physiological data indicative of the individual's condition. At block 1415, the task receives clinical data relating to the individual. In block 1420, a personalized cognitive treatment recommendation is generated based on at least one of physiological data or clinical data, and the recommendation is (i) at least one first cognitive treatment tool, (ii) at least one first cognitive treatment At least one second cognitive therapy tool different from the tool, or (iii) a specification of both (i) and (ii). At block 1430, the task optionally further comprises receiving performance data indicative of the individual's performance for at least one task associated with the at least one cognitive treatment tool of the recommendation.

conclusion

The above-described embodiments can 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 the embodiment is implemented at least partially in software, the software code may be executed on any suitable processor or set of processors, whether provided on a single computer or distributed across multiple computers.

In this regard, various aspects of the present invention are a computer-readable storage medium (multi-computer readable storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform a method of implementing various embodiments of the techniques Possible storage media) (e.g., computer memory, compact disk, optical disk, magnetic tape, flash memory, circuit configuration of field programmable gate arrays or other semiconductor devices, or other types of computer storage media or non-transitory media) Can be implemented at least partially as Computer-readable media or media may be transferable, such that a program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various aspects of the present technology as discussed above.

The term “program” or “software” is used to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement various aspects of the subject technology as discussed above. It is used herein in its general sense. Moreover, according to one aspect of this embodiment, the one or more computer programs that, when executed, perform the methods of the present technology need not be on a single computer or processor, but are distributed in a modular manner between multiple different computers or processors to be It should be noted that various aspects of the can be implemented.

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

Further, the techniques described herein may be implemented in a manner in which at least one example is provided. The actions performed as part of the method can be ordered in any suitable way. Thus, embodiments in which actions are performed in a different order than those illustrated can be constructed. This has been shown as a sequential action in the exemplary embodiment, but may include performing some actions simultaneously.

All definitions as defined and used herein are to be understood as controlling the dictionary definitions, definitions in the documents incorporated by reference, and/or the general meaning of the defined terms.

The indefinite articles "a" and "an" as used in this specification and in the claims are to be understood to mean "at least one" unless expressly indicated to the contrary.

The phrase “and/or” as used herein in the specification and claims means “either or both” of the elements so conjoined, ie, in some cases conjoinedly and in other cases separately. Should be understood as meaning. Several elements listed with “and/or” should be construed in the same way, ie as “one or more” of the elements so joined. Elements other than those specifically identified by the "and/or" clause may optionally be present, whether or not related to the specifically identified element. Thus, by way of non-limiting example, reference to “A and/or B” when used with an open language such as “comprising”, in one embodiment, includes A alone (optionally including elements other than B); In another embodiment, B alone (optionally including elements other than A); In another embodiment, both A and B (optionally including other elements) may refer to and the like.

As used herein in the specification and claims, “or” is to 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 being inclusive, ie the inclusion of one or more as well as at least one of the number or list of elements, and optionally additional It should be interpreted as the inclusion of items not listed. Only clearly opposite terms such as "only one of" or "exactly one of", or, when used in the claims, "consisting of" are the number of elements or It will refer to the inclusion of exactly one element of the list. In general, the term "or" as used herein is an exclusive alternative when preceded by an exclusive term such as "either", "one of", "only one of". (Ie, "one or the other but not both"). "Consisting essentially of" as used in the claims should have its usual meaning as used in the field of patent law.

As used herein in the specification and claims, the phrase “at least one” in connection with a list of one or more elements means at least one element selected from any one or more elements in the list of elements. It is to be understood that it does not necessarily include at least one of each and every element specifically listed within the element list and does not exclude any combination of elements from the element list. This definition also allows an element, whether or not related to a specifically identified element, that the phrase “at least one” may arbitrarily represent elements other than those specifically identified within the list of elements referred to. do. 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”) is, in one embodiment, B may refer to at least one optionally including one or more A, absent (optionally including elements other than B); In another embodiment, A refers to at least one optionally comprising one or more B, absent (optionally comprising elements other than A); In another embodiment, it refers to at least one optionally comprising one or more A and at least one optionally comprising one or more B (and optionally including other elements), and the like.

In the above specification as well as the claims, "comprising", "including", "carrying", "having", "containing", "comprising ( All transitional phrases such as" involving)", "holding", "composed of", etc. are to be understood to mean open-ended, ie, including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" are closed or semi-closed transitional phrases, respectively, as specified in the US Patent Office Patent Examination Procedure Manual, section 2111.03.

Claims (45)

In a system for generating personalized cognitive therapy recommendations for individuals,
One or more processors; And
A memory for storing processor-executable instructions and communicatively coupled with one or more processors,
Upon execution of processor-executable instructions by one or more processors, one or more processors:
a) receiving parameters for at least one cognitive therapy tool;
b) receiving at least one of physiological data indicative of the individual's condition or clinical data related to the individual;
c) being configured to generate personalized cognitive treatment recommendations based on at least one of physiological data or clinical data; The recommendation is (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) (i) and (ii) both Including the specifications of the
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the clinical data is obtained from at least one patient register.
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the one or more processors:
Further configured to receive performance data indicative of an individual's performance for at least one task related to the at least one cognitive treatment tool of the recommendation,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 3, wherein the personalized cognitive treatment recommendation is further based on received performance data.
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, further comprising d) repeating steps b) and c) after performing the personalized cognitive treatment recommendation, wherein the data received during the repetition of step b) is at least one of the recommendations. Comprising data collected after the individual has performed at least one task related to the cognitive therapy tool,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 5, wherein the one or more processors:
Further configured to monitor the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data indicative of an interaction of the individual with the at least one cognitive monitoring tool;
Wherein the data received during the iteration of step b) comprises data indicative of the condition of the condition based on the monitoring,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein generating a personalized cognitive treatment recommendation comprises using a predictive model trained using a plurality of training data sets, each training data set corresponding to a previously classified individual among the plurality of individuals, and , Wherein each training data set comprises data indicative of at least one indicator of cognitive ability of the classified individual and data indicative of a diagnosis of the condition or progression of the condition in the classified individual,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 7, wherein the prediction model is linear/logistic regression, principal component analysis, generalized linear mixed model, random decision forest, and support vector. Including at least one of a support vector machine, and/or an artificial neural network,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the condition comprises multiple sclerosis or lupus.
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the condition is dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, and autism spectrum. Autism spectrum disorder, presence of the l6pll.2 duplication, attention deficit hyperactivity disorder, sensory-processing disorder (SPD), mild cognitive impairment (mild cognitive impairment), Alzheimer's disease (Alzheimer's disease), schizophrenia (schizophrenia), depression (depression), including at least one of anxiety (anxiety),
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the one or more processors:
(i) the individual's likelihood of developing the condition, (ii) the stage of progression of the condition, or (iii) a combination thereof, further configured to generate an output,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the one or more processors:
Further configured to monitor the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data representing an interaction of the individual with the at least one cognitive treatment tool,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 1, wherein the at least one cognitive therapy tool comprises at least one of an interference processing tool, a spatial search tool, or an emotion processing tool.
A system for generating personalized cognitive therapy recommendations for individuals. 14. The method of claim 13, wherein the recommendation comprises an interference processing tool, the one or more processors:
Create a user interface;
Present a first instance of the task with interference to the user interface through the user interface, and request a first response from the individual to the first instance of the task if there is interference;
Present the first instance of the task through the user interface, requesting a second response from the individual to the first instance of the task if there is no interference;
here:
At least one of the interference with the first instance of the task comprises a computerized element;
Substantially simultaneously measuring a first response from the individual to the first instance of the task and a response from the individual to the interference;
Receive data indicative of the first response and the second response;
Further configured to analyze data indicative of the first response and the second response to calculate at least one performance metric comprising at least one quantified indicator of an individual's cognitive ability,
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 14, wherein the one or more processors are configured to present the task as a continuous visu-motor tracking task, wherein the first instance of the task is a first time interval of the continuous visual-motor task.
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 14, wherein the one or more processors are configured to present the interference as target identification interference through a user interface.
A system for generating personalized cognitive therapy recommendations for individuals. 14. The method of claim 13, wherein the recommendation comprises a spatial search tool, the one or more processors:
Create a user interface;
Presenting a first task requiring navigation of a designated path through an environment through a user interface;
Presenting a first indicator configured to search for a designated path from an initial point of the environment to a target end point regardless of whether an individual inputs or not through a user interface;
Configure a user interface to display an instruction to the individual to perform a second task, wherein the second task is to: (i) search in the reverse direction of at least a portion of a specified route or (ii) at least one additional number of times Request to search at least a portion of the route;
Individual to control either (i) the relative direction of the second indicator or (ii) the speed of movement of the second indicator, or (iii) both (i) and (ii) through the user interface to perform the second task Present a second indicator configured to navigate in the environment in response to a body motion of the person;
Obtaining measurement data by measuring data representing body movements of an individual to control a second indicator in performing the second task;
Further configured to analyze the measurement data to generate a performance metric for the performance of the second task, the performance metric providing an indication of the individual's cognitive ability,
A system for generating personalized cognitive therapy recommendations for individuals. 18. The method of claim 17, wherein generating the performance metric was the total time it took to successfully complete the second task, the number of erroneous turns made by the second metric, the number of erroneous directions of travel made by the second metric, or a designated path. Comprising at least one of the degree of deviation of the user-navigation path in the second task compared to,
A system for generating personalized cognitive therapy recommendations for individuals. 14. The method of claim 13, wherein the recommendation comprises an emotion processing tool, and the one or more processors:
Create a user interface;
By presenting a first instance of the task with interference to the user interface via the user interface, a first response from the person to the first instance of the task in case of interference and from the person to at least one evocative element Demand a response from;
here:
At least one of the first instance of the task and the interference comprises at least one associative element;
(i) a first response from the individual to a first instance of the task and (ii) a response from the individual to at least one associative factor substantially simultaneously, measuring the individual's ability to process emotions under an emotional load. );
Receive data indicative of the first response and the individual's response to the at least one associative element;
The first response and the data indicative of the individual's response to the at least one associative factor are further configured to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability under emotional load. ,
A system for generating personalized cognitive therapy recommendations for individuals. 20. The method of claim 19, further comprising an actuating component, wherein one or more processors are further configured to control the actuating component to effect an auditory, tactile, or vibrational stimulus, wherein the associative element is auditory. Including at least one of stimulation, tactile stimulation or vibration stimulation,
A system for generating personalized cognitive therapy recommendations for individuals. The method 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 data indicative of the individual's task performance.
A system for generating personalized cognitive therapy recommendations for individuals. The method of claim 21, wherein the one or more sensor elements comprise at least one of a gyroscope, accelerometer, motion sensor, position sensor, pressure sensor, optical sensor, video camera, auditory sensor, or vibration sensor.
A system for generating personalized cognitive therapy recommendations for individuals. The method of any one of claims 1 to 22, wherein the system is at least one of a virtual reality system, an augmented reality system, or a mixed reality system,
A system for generating personalized cognitive therapy recommendations for individuals. In a computer-implemented method for generating personalized cognitive therapy recommendations for individuals,
a) receiving parameters for at least one cognitive therapy tool;
b) receiving at least one of physiological data indicative of the individual's condition or clinical data related to the individual;
c) generating a personalized cognitive treatment recommendation based on at least one of physiological data or clinical data, the recommendation being (i) at least one first cognitive treatment tool, (ii) at least one first cognitive treatment tool Executing instructions stored on one or more memory storage devices including other at least one second cognitive therapy tool, or (iii) computer-executable instructions for performing an operation comprising the specifications of both (i) and (ii) Comprising using one or more processors to
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the clinical data is obtained from at least one patient register.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the task:
Further comprising receiving performance data indicative of the individual's performance for at least one task related to at least one cognitive treatment tool of the recommendation,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 26, wherein the personalized cognitive treatment recommendation is further based on received performance data.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the task:
d) further comprising repeating steps b) and c) after the individual has performed the personalized cognitive treatment recommendation, wherein the data received during the iteration of step b) is at least one associated with at least one cognitive treatment tool of the recommendation. Including data collected after the individual has performed the tasks of
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 28, further comprising: monitoring the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data indicative of an interaction of the individual with at least one cognitive monitoring tool. Including;
Wherein the data received during the iteration of step b) comprises data indicative of the condition of the condition based on the monitoring,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein generating a personalized cognitive treatment recommendation comprises using a predictive model trained using a plurality of training data sets, each training data set corresponding to a previously classified individual among the plurality of individuals, and , Each training data set comprises data indicative of at least one indicator of cognitive ability of the classified individual and data indicative of a diagnosis of the condition or progression of the condition in the classified individual,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 30, wherein the predictive model comprises at least one of linear/logistic regression, principal component analysis, generalized linear mixed model, random decision forest, support vector machine, or artificial neural network.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the condition comprises multiple sclerosis or lupus.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the condition is dementia, Parkinson's disease, cerebral amyloid angiopathy, familial amyloid neuropathy, Huntington's disease, autism spectrum disorder, the presence of l6pll.2 overlap, attention deficit hyperactivity disorder, sensory processing disorder, mild cognitive Disorder, Alzheimer's disease, schizophrenia, depression, including at least one of anxiety,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the task further comprises generating an output indicative of (i) the likelihood of developing the condition in the individual, (ii) the stage of progression of the condition, or (iii) a combination thereof.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the task:
Further comprising monitoring the condition of the individual's condition based on an analysis of at least one of physiological data, clinical data, or data indicative of an interaction of the individual with at least one cognitive treatment tool,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 24, wherein the at least one cognitive therapy tool comprises at least one of an interference processing tool, a spatial search tool, or an emotion processing tool.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. 37. The method of claim 36, wherein the recommendation comprises an interference handling tool, the task:
Create a user interface;
Present a first instance of the task with interference to the user interface through the user interface, and request a first response from the individual to the first instance of the task if there is interference;
Present the first instance of the task through the user interface, requesting a second response from the individual to the first instance of the task if there is no interference;
here:
At least one of the interference with the first instance of the task comprises a computerized element;
Substantially simultaneously measuring a first response from the individual to the first instance of the task and a response from the individual to the interference;
Receive data indicative of the first response and the second response;
Further comprising analyzing data indicative of the first response and the second response to calculate at least one performance metric comprising at least one quantified indicator of the individual's cognitive ability,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 37, wherein the task is presented as a continuous visual-motor tracking task, and the first instance of the task is a first time interval of the continuous visual-motor task.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 37, wherein the interference is presented as a target identification interference,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 36, wherein the recommendation comprises a spatial search tool, and the task:
Create a user interface;
Presenting a first task requiring navigation of a designated path through an environment through a user interface;
Presenting a first indicator configured to search for a designated path from an initial point of the environment to a target end point regardless of whether an individual inputs or not through a user interface;
The user interface is configured to display a command to the individual to perform a second task, wherein the second task is to (i) search the reverse direction of at least a portion of the specified path or (ii) the specified path at least one additional number of times Ask to search for at least a portion of;
Individual to control either (i) the relative direction of the second indicator or (ii) the speed of movement of the second indicator, or (iii) both (i) and (ii) through the user interface to perform the second task Present a second indicator configured to navigate in the environment in response to a body motion of,
Obtaining measurement data by measuring data representing body movements of an individual to control a second indicator in performing the second task;
Further comprising analyzing the measurement data to generate a performance metric for the performance of the second task, wherein the performance metric provides an indication of the individual's cognitive ability.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. 41. The method of claim 40, wherein generating the performance metric is the total time it took to successfully complete the second task, the number of erroneous rotations made by the second metric, the number of erroneous directions of travel made by the second metric, or a specified path. Comprising at least one of the degree of deviation of the user-navigation path in the second task compared to,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 36, wherein the recommendation comprises an emotion processing tool, and the task is:
Create a user interface;
Presenting the first instance of the task with interference to the user interface through the user interface, requesting a first response from the individual to the first instance of the task and a response from the individual to at least one associative factor if there is interference and;
here:
At least one of the first instance of the task and the interference comprises at least one associative element;
(i) the first response from the individual to the first instance of the task and (ii) the response from the individual to at least one associative factor are measured substantially simultaneously to provide a measure of the individual's ability to process emotions under the emotional load. and;
Receive data indicative of the first response and the individual's response to the at least one associative element;
Further comprising calculating at least one performance metric including at least one quantified indicator of the individual's cognitive ability under emotional load by analyzing data representing the first response and the individual's response to at least one associative factor doing,
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. 43. The method of claim 42, wherein the task further comprises controlling an actuation component to exert an auditory stimulus, a tactile stimulus, or a vibration stimulus, and the associative element comprises at least one of an auditory stimulus, a tactile stimulus, or a vibration stimulus.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. 43. The method of claim 42, further comprising the task controlling one or more sensor components to measure data indicative of the individual's task performance.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals. The method of claim 44, wherein the one or more sensor components comprise at least one of a gyroscope, accelerometer, motion sensor, position sensor, pressure sensor, optical sensor, video camera, auditory sensor, or vibration sensor.
A computer-implemented method for generating personalized cognitive therapy recommendations for individuals.

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