WO2023216680A1 - 一种数字生物标志物的形成系统、形成方法及基于数字生物标志物的脑健康评判系统 - Google Patents

一种数字生物标志物的形成系统、形成方法及基于数字生物标志物的脑健康评判系统 Download PDF

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WO2023216680A1
WO2023216680A1 PCT/CN2023/078507 CN2023078507W WO2023216680A1 WO 2023216680 A1 WO2023216680 A1 WO 2023216680A1 CN 2023078507 W CN2023078507 W CN 2023078507W WO 2023216680 A1 WO2023216680 A1 WO 2023216680A1
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data
digital
user
chess
card
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PCT/CN2023/078507
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English (en)
French (fr)
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WO2023216680A9 (zh
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安宁
杨矫云
丁会通
胡恩泽
明鸣
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合肥工业大学
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Publication of WO2023216680A9 publication Critical patent/WO2023216680A9/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/041—Abduction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to the field of digital medical technology, and in particular to a digital biomarker and a brain health evaluation system based thereon.
  • Cognitive impairment refers to the impairment of one or more functions in the above-mentioned areas, which affects the patient's social functions and quality of life to varying degrees, and in severe cases may even lead to the patient's death. Cognitive impairment is not only a simple medical problem, but also a serious social problem. Nervous system degenerative diseases, cardiovascular and cerebrovascular diseases, nutritional and metabolic disorders (especially diabetes), infection, trauma, tumors, drug abuse and other reasons can lead to cognitive impairment.
  • the assessment of the cognitive ability of human individuals mainly relies on the cognitive impairment screening scale, through the staff's evaluation of human individuals, and then the cognitive ability assessment of human individuals is achieved.
  • Current digital evaluation forms such as savonix digital scales
  • localized versions of the above-mentioned quantitative evaluation forms in China usually embed the corresponding evaluation software into the APP of the mobile device, and evaluate the user and application through the game process.
  • Interactive evaluation the inventor found that they still have the following technical deficiencies: a.
  • Traditional scales and traditional cognitive assessments are time-consuming and labor-intensive, difficult to collect data, and difficult to test.
  • the Chinese patent document with publication number CN106327049A discloses a cognitive assessment system, including an information module, a test module and an analysis module; the information module is used to obtain medical information matching the test module based on the subject's information, and establish a complete Cognitive assessment database; the test module obtains the cognitive test data of the subject through testing, including the following five sub-modules: attention and executive function test module, memory test module, mathematics and calculation ability test module, language test module, action and behavior The control and planning test module; the analysis module determines the subject's cognitive assessment results based on the medical information obtained by the information module and the cognitive test data obtained by the test module.
  • this invention still requires workers to perform measurements to achieve cognitive assessment, which places high demands on workers and is difficult to automate.
  • the cognitive decline of human individuals is a slow and imperceptible process.
  • the cognitive ability scores obtained through one or two cognitive impairment screening scale assessments cannot well reflect the changes in cognitive abilities of human individuals. , and as mentioned earlier, due to the long-term evaluation process of the cognitive impairment screening scale, high requirements on staff, and difficulty in completing most of the assessment content for human individuals with low education levels, people with cognitive impairment frequently pass the test. Changes in cognitive ability assessed by screening scales are difficult to operationalize. Therefore, it is necessary to improve the existing technology.
  • Traditional biomarkers generally refer to indicators obtained through biochemical tests and are used to mark changes in the structure or function of tissues such as organs. For example, blood tests in traditional hospitals can produce insightful data, but because they are obtained through biochemical tests rather than through connected digital health devices, they are not digital biomarkers.
  • the current development goal of digital biomarkers is to effectively supplement traditional biomarkers rather than replace them; on the other hand, digital biomarkers can effectively promote the transformation of the health care model from passive response to active prevention.
  • digital biomarkers researchers will not only be able to better explain disease, but they will also be able to leverage increasingly large amounts of health data to analyze what normal and healthy individual states represent and, more importantly, predict future health outcomes. As a result, interest in digital biomarker research is expected to soar in the coming years.
  • the present invention provides a brain health evaluation system based on digital biomarkers.
  • the system for population screening of brain health at least includes:
  • the digital chess and cards can at least collect the motion data of the digital chess and cards when the user uses the digital chess and cards.
  • the motion data at least includes spatial coordinate data during the use/movement of the digital chess card by the user.
  • the motion data may also include the acceleration magnitude/direction of the digital chess card when it is used/moved by the user, the rotation angular velocity/angular acceleration along each coordinate axis, etc.
  • the server can at least obtain the motion data, calculate and analyze based on the motion data to derive digital biomarkers corresponding to reference markers, and evaluate/screen the user's brain health based on the digital biomarkers. check.
  • trajectory analysis sensor decomposes a complete card touching action into multiple sub-parts to record and easily loses potentially important digital biomarkers such as mid-air pause/dwell time and complete card touching trajectory.
  • the second moment can be determined by an intelligent trajectory analysis sensor.
  • the intelligent trajectory analysis sensor determines that this is the second moment.
  • the smart trajectory analysis sensor when and only when the smart trajectory analysis sensor in the digital chess and card starts to search for the wearable device around the smart trajectory analysis sensor when it is at the second moment, the smart trajectory analysis sensor obtains the distance The intelligent trajectory analysis sensor detects the identifier of the wearable device closest to it.
  • the intelligent trajectory analysis sensor chooses to measure the distance between the intelligent trajectory analysis sensor and the surrounding wearable devices at the second moment, that is, after using/moving a certain digital chess card, the digital chess card is already located close to
  • the side of the user who is using the current digital chess and card game can maintain a relatively long distance from the wearable devices of other users, so that users who are using the current digital chess and card game and non-users who are not using the current digital chess and card game are closely connected to the current digital chess and card game device.
  • the distance has significant difference, thus improving the accuracy of the attribution problem of the spatial coordinate data collected each time by digital chess and cards.
  • a smart trajectory analysis sensor begins to measure the distance between itself and surrounding wearable devices and sends an identity request to the wearable device closest to the digital chess piece. Then, the wearable device closest to the digital chess card sends an identifier used to identify the identity information of the user wearing the wearable device to the intelligent trajectory analysis sensor in the digital chess card.
  • the intelligent trajectory analysis sensor When the intelligent trajectory analysis sensor receives the identifier of the wearable device closest to the digital chess and card, the intelligent trajectory analysis sensor inserts the identifier into the current movement data of the intelligent trajectory analysis sensor to the digital evaluation unit and/or server. , to identify which user the motion data collected at that time comes from.
  • the intelligent trajectory analysis sensor can only measure the distance of the wearable device within the distance threshold range.
  • the distance threshold can be set artificially according to the actual scenario, for example, the distance threshold can be fifteen centimeters.
  • the ranging requirements of the intelligent trajectory analysis sensor can be significantly reduced, thereby significantly reducing the data collection volume of the intelligent trajectory analysis sensor, thereby enabling the intelligent trajectory analysis sensor to more quickly identify the distance of the current intelligent trajectory analysis sensor.
  • the identifier may be composed of characters, numbers, etc.
  • the identifiers can be A1, A2, A3, and A4, where a group of users uses the same set of digital chess and cards.
  • the letter A can represent which group of users they belong to, and the number can identify the first user who belongs to the corresponding user group.
  • the intelligent trajectory analysis sensor After the intelligent trajectory analysis sensor completes transmitting motion data to the digital evaluation unit and/or the cloud server through the wireless gateway, the intelligent trajectory analysis sensor automatically clears the recorded identifier so that the intelligent trajectory analysis sensor can be used next time.
  • an identifier is received from the wearable device to identify the identity information of the next user.
  • the intelligent trajectory analysis sensor sends current movement data to the digital evaluation unit and/or server.
  • the digital evaluation unit and/or server When the digital evaluation unit and/or server receives the sports data, it first identifies the identifier in the current sports data to identify the data source of the current sports data, and then enters the current sports data into the corresponding sports data.
  • the identifier corresponds to the user's database, thereby solving the problem of attribution of data collected for each digital chess and card game.
  • the motion data collected by the intelligent trajectory analysis sensor installed in the digital chess and card can also be sent to a digital evaluation unit and/or server, or sent to a digital evaluation unit and/or server through the wearable device. .
  • the method of screening/assessing users' brain health based on digital chess and cards is more acceptable to the elderly, allowing this system to be put into use in multiple communities as a preliminary screening for brain health assessment of elderly users. tool, thereby significantly reducing the economic and social costs of screening/assessing users' brain health, and also avoiding the waste of medical resources; compared with only conducting cognitive test assessments through APPs, through digital chess and card entities and digital assessment units
  • the user's data can be obtained in more dimensions.
  • the APP can only obtain the user's two-dimensional data, while the intelligent trajectory analysis sensor in the digital chess and card game can obtain three-dimensional or even more-dimensional data; the present invention can assist in achieving brain health. Moving the diagnostic threshold forward, it can be used as a simpler, objective risk assessment tool for Alzheimer's disease rather than as a diagnostic tool for cognitive impairment.
  • the intelligent trajectory analysis sensor can also measure the vibration value of itself and/or the chess and card entity.
  • the intelligent trajectory analysis sensor can start recording the spatial coordinate data of the intelligent trajectory analysis sensor or the chess and card entity when the intelligent trajectory analysis sensor or the chess and card entity vibrates/moves.
  • the intelligent trajectory analysis sensor can stop recording the spatial coordinate data of the intelligent trajectory analysis sensor or the chess and card entity when the intelligent trajectory analysis sensor or the chess and card entity stops vibrating/moving.
  • the smart trajectory analysis sensor can start recording the spatial coordinate data of the smart trajectory analysis sensor or the chess and card entity when the smart trajectory analysis sensor or the chess and card entity vibrates/moves and the smart trajectory analysis sensor is not at the first height, wherein,
  • the first height is the height of the chess and card platform where the user uses digital chess and cards.
  • the chess and card platform can be a mahjong table, etc.
  • the spatial coordinate data collected by the intelligent trajectory analysis sensor can have a maximum accuracy of at least five millimeters.
  • the recording duration of the accuracy of the spatial coordinate data collected by the intelligent trajectory analysis sensor can be up to ten seconds.
  • the digital chess and card includes at least a chess and card entity and an intelligent trajectory analysis sensor disposed or integrated in the chess and card entity.
  • the intelligent trajectory analysis sensor can at least be used to obtain the spatial coordinates of the chess and card entity in the process of being used by the user and/or the timestamp associated with the spatial coordinates.
  • the user is able to wear a wearable device.
  • the wearable device is configured to at least be able to send to the user an identifier capable of identifying the user's identity information.
  • the intelligent trajectory analysis sensor can obtain the identifier of the wearable device.
  • the wearable device can be worn on the user's wrist.
  • the wearable device can also be worn on other body parts of the user.
  • the wearable device can also collect or be inputted the user's individual characteristic data and/or physiological data.
  • the physiological data at least includes heart rate, blood pressure, pulse oximetry, body temperature and related timestamp.
  • Individual characteristics data include at least familiarity with chess and cards, education level, and disease history.
  • the wearable device can send individual characteristic data and/or physiological data to the digital evaluation unit or the server.
  • the physiological data may also include information related to the user's activities, such as the number of steps the user walks every day.
  • the physiological data may also include the shaking frequency and amplitude of the user's body parts.
  • the shaking frequency and amplitude of the user's body parts can be obtained through the accelerometer collection within the wearable device.
  • the wearable device can at least collect shaking data of the hand of a user wearing the wearable device.
  • the server can combine/fuse the data collected by the wearable device with the sports data of digital chess and cards to jointly assess the cognitive abilities and/or other diseases of multiple users.
  • a digital evaluation unit can also be included.
  • the digital evaluation unit is configured to monitor the interaction process between the user and the digital evaluation unit to capture the user's interaction data, language data, eye movement data and/or pupil data.
  • the digital evaluation unit can send the interaction data, language data, eye movement data and/or pupil data of the above-mentioned interaction process to the server, so that the server can be based on the interaction data, language data, eye movement data and/or eye movement data of the interaction process.
  • the user's assessment data is calculated from movement data and/or pupil data.
  • the interaction data is data generated by the user in the process of interacting with the digital evaluation unit 1, including but not limited to click data, frequency data, action continuous data, trajectory data, information content selection data, sliding selection data, and content changes. Logical relationship data, etc.
  • the digital evaluation unit can identify and extract voice information and graphic information from the used language data. Through the extraction of language data, the user's emotional information, emotional information, etc. can be identified.
  • Graphic information refers to graphic information, language and/or pictures or texts used for communication.
  • ordinary users commonly use emoticons and emoticon images to express language and emotions in communication.
  • Users' emotional information, emotional information, etc. can also be extracted directly or indirectly through graphic and text information.
  • Speech information is, for example, speech commands, speech patterns in words and phrases.
  • the digital evaluation unit can extract phonemes and repeated combinations of phonemes.
  • the digital evaluation unit can identify and extract features of each speech pattern, including pitch, amplitude and spectrum.
  • the eye movement data is data related to the user's eye movement.
  • the eye movement data may include but is not limited to: data related to eye movements such as gaze, saccades, and following.
  • the eye movement data may also include: the user's gaze point position, gaze time and pupil diameter.
  • the above eye movement data can be obtained by monitoring the user's eye behavior with an electrooculogram sensor or other related eye movement sensors.
  • Pupil data includes but is not limited to pupil size data, pupil change data, etc.
  • the digital chess and cards can send the motion data to the digital evaluation unit and/or the server.
  • the digital evaluation unit can acquire the motion data and send the motion data to the server.
  • an intelligent environment equipment unit can also be included.
  • the intelligent environment equipment unit can be used to obtain daily data in the user's daily life.
  • the server can at least adjust the type/type of raw data collected by the digital chess and cards, wearable devices, digital evaluation units and intelligent environment equipment units based on the reference markers.
  • the reference marker is a digital biomarker that can effectively represent the target attribute of the user's brain health.
  • target attributes include at least but not limited to: the ability to perform all cognitive processes.
  • target attributes may also include: the ability to perform mental processes.
  • the target attributes may also include: learning and judgment, language and memory abilities.
  • the reference marker may be a digital biomarker that can effectively represent changes in the user's cognitive ability or status.
  • the reference marker may also be a digital biomarker that effectively represents other physical health levels of the user.
  • the source of digital biomarkers is not limited to a single data category, but can come from one or more of interaction data, language data, eye movement data, eye movement data, and daily data.
  • the server can adjust the type of movement data of the user's hands collected by the digital chess and cards based on the reference markers.
  • /Type that is, the intelligent trajectory analysis sensor only needs to measure/collect the vibration value of the digital chess card between the first moment and the second moment and the associated timestamp for calculation of digital biomarkers, without the need to measure/ Collect the spatial coordinate data of the digital chess and cards when the user moves the hand, or the intelligent trajectory analysis sensor can also collect the spatial coordinate data of the digital chess and cards. However, the intelligent trajectory analysis sensor does not need to send the collected spatial coordinate data to the digital evaluation unit or server.
  • the wearable device when used to screen people's brain health, if the server analysis shows that the reference markers for cognitive decline also include the user's heart rate when using digital chess and cards, the wearable device only needs to focus on measuring the user's heart rate when using digital chess and cards. heart rate data.
  • the server analysis determines that the reference markers of cognitive decline also include the frequency of users' forgetful behaviors
  • the smart environment equipment unit only needs or at least requires a water immersion sensor to monitor How often users forget to turn off the tap.
  • the type and/or quantity of raw data collected by digital chess and cards, digital evaluation units, wearable devices, smart environment equipment units, etc. can be adjusted through effective digital biomarkers inferred by the server based on causal analysis theory. , thereby reducing the data processing volume of the server and improving the server's real-time processing speed of health data collected based on various sensors/devices, which can then be analyzed and fed back to doctors, consumers, researchers and others through the server faster.
  • the present invention also provides a data processing method based on digital biomarkers.
  • the method is:
  • the digital chess and card collects movement data of the digital chess and card when the user uses the digital chess and card;
  • the server obtains the motion data collected by the digital chess and card game, calculates and analyzes the motion data to obtain a digital biomarker that is consistent with the data type of the reference marker, and evaluates the user based on the digital biomarker. Brain health assessment/screening.
  • the server can access the valid reference markers previously recorded or analyzed by the server, and calculate the difference between the digital biomarker generated by the server calculation and the reference marker or previous valid digital biomarker based on the difference.
  • the user's brain health is assessed/screened based on the differences.
  • the server can predict users' changing trends within a specified time range in the future based on users whose cognitive abilities have declined among the groups based on the differences.
  • the reference markers are obtained by the following steps: obtaining original data; extracting feature data from the original data based on machine learning, and further screening candidate digital biomarkers from the feature data; based on causal learning Causal inference is performed from the candidate digital biomarkers to computational analysis to derive the reference marker.
  • the original data is obtained by the following steps: the digital chess and card collects movement data of the digital chess and card when the user uses the digital chess and card; the wearable device collects other physiological data of the user; the digital evaluation unit acquires the user's evaluation data, Language data and eye movement data; the intelligent environment equipment unit collects daily data in the user's daily life; the server obtains one of the movement data, other physiological data, assessment data, language data, eye movement data and daily data, or A variety of raw data for server analysis.
  • the cognitive ability evaluation system at least includes:
  • Digital evaluation unit configured to:
  • the interaction process between the user and the digital evaluation unit can be monitored to obtain interaction data, language data, eye movement data and/or pupil data,
  • a server connected to the digital evaluation unit can obtain the interaction process information to calculate evaluation data
  • the server is configured to at least obtain the interaction data, language data, eye movement data and/or pupil data, and based on the interaction data, language data, eye movement data and/or pupil data and evaluation data, The user's brain health is judged.
  • the cognitive ability evaluation system can also include digital chess and cards.
  • the digital chess card is configured to at least capture movement data of the digital chess card when the user uses the digital chess card.
  • the digital evaluation unit or the server is provided with an early warning module and a display module.
  • the early warning module can compare the user's cognitive ability score corresponding to the current period with the user's cognitive ability score corresponding to the previous period, and the early warning module can detect when the user's cognitive ability score drops by more than the preset trigger threshold. Generate the first warning message.
  • the first warning information can be displayed through the display module.
  • the digital chess and cards can send the motion data to the digital evaluation unit and/or the server.
  • the digital evaluation unit can acquire the motion data and send the motion data to the server.
  • an intelligent environment equipment unit can also be included.
  • the intelligent environment equipment unit can be used to obtain daily data in the user's daily life.
  • a data processing method is:
  • the digital evaluation unit monitors the interaction process between the user and the digital evaluation unit to obtain interaction data, language data, eye movement data and/or pupil data;
  • the digital chess and card collects movement data of the digital chess and card when the user moves the digital chess and card;
  • the digital evaluation unit and/or server calculates evaluation data based on the interaction process information, and can at least evaluate the user's brain based on the language data, eye movement data, evaluation data, movement data and reference markers. Health is judged.
  • the present invention provides a digital biomarker.
  • the initial data of the digital biomarker is at least composed of collecting behavioral data of various operations of the user using digital chess and cards, such as mahjong, poker, chess, Go, etc.; the collected initial data will be sent to the analysis unit and/or server, The initial data will be analyzed and calculated by the analysis unit and/or the artificial intelligence program on the server to obtain a series of digital biomarkers that can characterize the user's cognitive abilities, such as perceptual cognition, sensory cognition, thinking cognition, etc., and the resulting number Biomarkers can be marked on the real-time data collected when users use digital chess and cards, and the user's cognitive ability can be assessed through marking.
  • the present invention when using smart wearable devices, multiple users wear their own smart wearable devices respectively.
  • the behavioral data of each user wearing the smart wearable device is analyzed respectively. That is, the initial data collected and analyzed according to the present invention includes the user's behavioral data, and the behavioral data is determined by the It is collected from the digital chess and cards during the user's use of the digital chess and cards.
  • one set of digital chess and cards can correspond to multiple sets of smart wearable devices, wherein at least one prop in the digital chess and cards can have at least one sensor, and each smart wearable device can be pre-established with the prop with the sensor. Link to collect behavioral data.
  • the method of screening/assessing the user's health status based on digital chess and cards is more acceptable to ordinary users, that is, on the one hand, it will not affect the user's normal life during the long-term use of this digital chess and cards; on the other hand, Since this digital chess and card can arouse the continued interest of ordinary users, it enables users to actively and long-term use of this digital chess and card, and the user's behavioral data collected by this digital chess and card can be used as one of the initial data and sent to the server It is then processed and analyzed by the server to calculate the digital biomarker that can characterize the user's cognitive ability; in addition, it can also significantly reduce the economic cost of screening/assessing the user's health status, as well as the social impact. costs and avoid wastage of medical resources.
  • the smart trajectory analysis sensor when and only when the smart trajectory analysis sensor in the digital chess and card starts to search for the portable smart device around the smart trajectory analysis sensor when it is at the second moment, the smart trajectory analysis sensor obtains the distance The smart trajectory analyzes the sensor's nearest identifier of the portable smart device.
  • the user moves the digital chess and card once or once.
  • the user's intelligent trajectory analysis sensor searched for multiple other users or other users' portable smart devices. This is because when a user uses a digital chess and card, especially when a certain digital chess and card is used or moved, the digital chess and card is located on the side close to the user who is using the current digital chess and card, and thus can communicate with other users' portable smart devices.
  • the intelligent trajectory analysis sensor chooses to measure the distance between the intelligent trajectory analysis sensor and the portable smart devices around it at the second moment, that is, at the moment when a certain digital chess and card is used or moved, the digital chess and card is already in close proximity.
  • the side of the user who is using the current digital chess and card game can maintain a relatively long distance from the portable smart devices of other users, so that the user who is using the current digital chess and card game and the non-users who are not using the current digital chess and card game are connected to the current digital chess and card game.
  • the distance has significant difference, thus improving the accuracy of the attribution problem of the spatial coordinate data collected each time by digital chess and cards.
  • the intelligent trajectory analysis sensor sends current motion data to the analysis unit and/or server.
  • the analysis unit and/or server When the analysis unit and/or server receives the sports data, it first identifies the identifier in the current sports data to identify the data source of the current sports data, and then enters the current sports data into the identity of the current sports data. into the database of the user corresponding to the symbol, thereby solving the problem of attribution of data collected each time for each digital chess and card game.
  • the motion data collected by the intelligent trajectory analysis sensor provided in the digital chess and card can also be sent to the analysis unit and/or server through the portable intelligent device.
  • the initial data can also include one or more of the following: interaction data, language data, eye movement data, pupil data and daily data.
  • the interaction data, language data, eye movement data, and pupil data are collected by the analysis unit during the interaction process of the user using the analysis unit, and the daily data are collected by the intelligent monitoring unit during the user's daily life behavior. get.
  • the digital chess and cards may include: chess and card entities and intelligent trajectory analysis sensors.
  • the intelligent trajectory analysis sensor can at least obtain the spatial coordinate data of the chess and card entity when it is used by the user and the timestamp corresponding to the spatial coordinate data.
  • the user can wear a portable smart device.
  • the portable smart device is configured to at least be able to send an identification code capable of identifying the user's identity information to the analysis unit and/or the server.
  • the analysis unit and/or the server can obtain the identification code carried by the portable smart device that can identify the user's identity information.
  • the portable smart device can also collect or be inputted physiological data of the user.
  • the physiological data may include one or more of heart rate, blood pressure, pulse oximetry, and body temperature.
  • the portable smart device can send the individual characteristic data and/or physiological data to the analysis unit and/or the server.
  • the above physiological data can also be used as initial data.
  • the individual characteristic data may include familiarity with the chess and card entity, education level and disease history.
  • the behavioral data collected by the intelligent trajectory analysis sensor provided in the digital chess and card can also be sent to the analysis unit and/or the server through the portable intelligent device.
  • the analysis unit and/or the method for processing analysis to calculate the digital biomarker that can characterize the cognitive ability of the user is:
  • Characteristic causal inference is performed on the candidate digital biomarkers to obtain the digital biomarkers through computational analysis.
  • the method for performing feature causal inference on the candidate digital biomarkers to deduce effective digital biomarkers is:
  • a causal analysis knowledge base is constructed based on the characteristic data, and causal inference is performed on the candidate digital biomarkers through the established causal analysis knowledge base based on the causal analysis theory, so as to obtain effective digital biomarkers through computational analysis.
  • the present invention also provides a digital biomarker.
  • the formation method of the digital biomarker is: obtaining initial data and performing data fusion on the initial data to obtain fused data; extracting feature data from the fused data and further Candidate digital biomarkers are obtained through calculation and analysis from the characteristic data; causal inference is performed on the candidate digital biomarkers to analyze and infer the effective digital biomarkers.
  • the initial data includes the user's behavioral data, which is collected from the digital chess and cards during the user's use of the digital chess and cards, and/or the initial data includes the user's language data, and the The language data is collected by the analysis unit during the user's use of digital chess and cards.
  • the track recording sensor is disposed or integrated in the chess and card entity, and the track recording sensor can at least be used to obtain the information of the chess and card entity.
  • the user can wear a portable smart wearable device used in conjunction with the digital chess and cards, and the portable smart wearable device is configured to at least send to the track recording sensor a message that can identify the use of the digital chess and cards.
  • said initial data can be sent to a server and/or analysis unit.
  • the analysis unit can determine whether the information input by the user through the analysis unit is within a prescribed numerical range.
  • Figure 1 is a schematic diagram of simplified module connection relationships in a brain health assessment system according to a preferred embodiment of the present invention
  • Figure 2 is a schematic diagram of a preferred embodiment of the digital chess and card game provided by the present invention.
  • Figure 3 is a schematic diagram of another preferred embodiment of the digital chess and card and digital evaluation unit provided by the present invention.
  • Figure 4 is a schematic diagram of a simplified module connection relationship of a cognitive ability evaluation system according to a preferred embodiment of the present invention
  • Figure 5 is a schematic diagram of a simplified module connection relationship of a preferred embodiment of the digital chess and card server provided by the present invention
  • Figure 6 is a model display diagram of the digital chess and card game according to the present invention.
  • Figures 1, 2, 3 and 4 illustrate a brain health evaluation system and method based on digital biomarkers, which can also be called a cognitive ability evaluation system and method based on digital biomarkers.
  • the system at least includes digital chess and cards 1 and server 2.
  • the digital chess card 1 is configured to at least be able to collect movement data of the digital chess card 1 when the user uses the digital chess card 1 .
  • the server 2 is configured to at least obtain motion data, calculate and analyze based on the motion data to derive digital biomarkers corresponding to the reference markers, and evaluate/screen the user's brain health based on the digital biomarkers. .
  • the server 2 can be an ordinary physical server 2.
  • server 2 can also be cloud server 2.
  • brain health includes at least: cognitive ability.
  • brain health may also include: the ability to learn, judge, language and memory, and the ability to perform mental processes.
  • brain health may also include other diseases.
  • the digital chess and card 1 at least includes: a chess and card entity 101, and an intelligent trajectory analysis sensor 102 provided or integrated in the chess and card entity 101.
  • the intelligent trajectory analysis sensor 102 can at least be used to obtain the information that the chess and card entity 101 is used by the user.
  • the evaluation unit is used to collect interaction data, language data, eye movement data and/or pupil data during the use of the chess and card entity 101 by the user.
  • the process in which the above-mentioned chess and card entity 101 is used by the user at least includes: moving the chess and card, and rotating the chess and card.
  • the process of using the chess card by the user may also include: the force size, direction and frequency of pressing the chess card, the force size and frequency of tapping using the chess card, etc.
  • the chess and card entity 101 may be mahjong.
  • the chess and card entity 101 can also be chess, chess and other types of chess and cards.
  • the intelligent trajectory analysis sensor 102 can at least be used to obtain the three-dimensional spatial coordinate data of the digital chess and card 1 .
  • the NFC reader/writer can at least be used to configure parameters for the intelligent trajectory analysis sensor 102 .
  • the wireless gateway is used in conjunction with the intelligent trajectory analysis sensor 102.
  • the wireless charger can at least be used to charge the intelligent trajectory analysis sensor 102 .
  • the intelligent trajectory analysis sensor 102 is provided in or integrated with the chess and card game.
  • the intelligent trajectory analysis sensor 102 is configured to be able to open a data channel for data transmission when needed to connect with other devices such as wireless gateways.
  • strong glue can be used to bond the slot.
  • the data content transmitted by the intelligent trajectory analysis sensor 102 at least includes: three-dimensional spatial coordinate data of the digital chess and card 1 (such as digital mahjong), that is, the coordinates of the digital chess and card 1 corresponding to the x, y, and z axes respectively.
  • the coordinate system in which the digital chess and card 1 is located can be flexibly selected according to the actual application scenario.
  • the NFC reader/writer can support the NFC protocol.
  • an NFC reader can be used to configure parameters for the intelligent trajectory analysis sensor 102 .
  • the NFC reader can be connected to a PC for use.
  • the NFC reader/writer can also be used to read and write the acquired data of the intelligent trajectory analysis sensor 102 .
  • the wireless charger can adopt the Qi standard wireless charging protocol.
  • a wireless charger can be used to charge the smart trajectory analysis sensor 102 .
  • the wireless gateway can adopt a wireless network or the like.
  • the wireless gateway can keep the data receiving channel open and can receive or transmit data sent by the intelligent trajectory analysis sensor 102 at any time.
  • a set of digital chess and cards 1 is equipped with a set of wireless gateways.
  • any digital chess card 1 in a set of digital chess cards 1 only sends data through the wireless gateway corresponding to the set of digital chess cards 1 .
  • the intelligent trajectory analysis sensor 102 may be a three-axis acceleration sensor.
  • the intelligent trajectory analysis sensor 102 does not need to maintain a long connection with the corresponding wireless gateway.
  • the intelligent trajectory analysis sensor 102 can establish a data connection with the wireless gateway to open a data transmission channel.
  • the intelligent trajectory analysis sensor 102 can be used to record the spatial running trajectory data formed by the user grabbing the digital chess card 1 integrated or equipped with the intelligent trajectory analysis sensor 102 in mid-air and/or within a specific platform.
  • the spatial movement trajectory data may include timing signals such as the duration of the pause generated when the user grasps the mahjong, the distance moved by one action, and the movement trajectory of the user's hand from one position to another.
  • one action is a complete action without pause.
  • one action is an action that includes pauses throughout the entire process.
  • the working process of the intelligent trajectory analysis sensor 102 can also be divided into the following steps:
  • the intelligent trajectory analysis sensor 102 When the intelligent trajectory analysis sensor 102 is started (i.e., the first moment), the intelligent trajectory analysis sensor 102 performs clock configuration and serial port configuration through STM32;
  • the 2.4G module sends data to wake up the sensor: if the sensor response signal is received, the intelligent trajectory analysis sensor 102 is released from sleep. If the sensor response signal is not received, the 2.4G module resends data to wake up the sensor;
  • the intelligent trajectory analysis sensor 102 is initialized, that is, the time is reset to read the time and the x/y/z axis data are reset to zero respectively, so that the new coordinate data of the x/y/z axis can be read in the subsequent process;
  • the intelligent trajectory analysis sensor 102 calculates the output acceleration, angular velocity and spatial coordinates of the digital chess and card 1;
  • the intelligent trajectory analysis sensor 102 performs data fusion, that is, obtains the coordinate data of the x/y/z axis for posture judgment and setting posture flags of the digital chess and card 1, and obtains information sent by the wearable device 3 or other devices for representation.
  • the intelligent trajectory analysis sensor 102 packages the above-mentioned data fusion (such as the acceleration, angular velocity and spatial coordinates of the digital chess and card 1, as well as the identification code used to identify the user's identity), wherein the packaged data can be set with a check digit. And sent to the gateway through the 2.4G module, the gateway then forwards the data to the cloud platform (i.e. cloud server 2); the intelligent trajectory analysis sensor 102 can also send the above-mentioned data after data fusion and attitude judgment to the host computer (host computer) through the serial port It can be a personal server 2), and reproduces all the data obtained from the intelligent trajectory analysis sensor 102 through the host computer to draw the spatial trajectory image of the currently used digital chess card 1. Particularly preferably, the above-mentioned spatial trajectory image can also be sent to the server 2 as a possible original data source of digital biomarkers.
  • the server 2 can be a possible original data source of digital biomarkers.
  • the intelligent trajectory analysis sensor 102 is used to collect the user's use of the digital chess and card 1 (such as digital mahjong), the three-dimensional action trajectory data generated by the user's hand drawn in mid-air and/or on a specific platform (such as the desktop of the mahjong table) when taking the digital chess card 1, the user's hand It can fully capture the strength of mahjong and other chess and cards, the frequency and intensity of tremors, the smoothness and coherence of movements, pause time, movement trajectories and other timing signals, and can use existing technologies such as Bluetooth or ZigBee technology, wireless gateways to pass through The user's motion data obtained by the intelligent trajectory analysis sensor 102 integrated in Mahjong is transmitted to the server 2 .
  • the intelligent trajectory analysis sensor 102 integrated in Mahjong is transmitted to the server 2 .
  • the process of the user actually grabbing physical chess and cards such as mahjong is significantly different from just touching the touch screen with the game APP. This is because the touch screen with the game APP can generally only obtain the user's two-dimensional movement data, and the user is The actual process of capturing physical chess and cards such as mahjong will generate three-dimensional spatial motion data, and even more dimensional motion data, thus enriching the data categories; in addition, using digital chess and cards1 to obtain the user's motion data is also easier for users to accept.
  • the intelligent trajectory analysis sensor 102 can send the acquired user's action data to the digital evaluation unit 4 and/or the server 2 on the mobile platform through existing technologies such as wireless networks.
  • the user is able to wear the wearable device 3 .
  • the wearable device is configured to at least be able to send to the user an identifier that can identify the identity information of the user who is currently using the digital chess and card 1 .
  • the intelligent trajectory analysis sensor 102 can obtain the identifier of the wearable device 3 .
  • the intelligent trajectory analysis sensor 102 can obtain the distance intelligent trajectory analysis. The identifier of the wearable device 3 closest to sensor 102.
  • the wearable device 3 may include but is not limited to: a wristband smart device, a head-mounted smart device, etc.
  • the wearable device 3 may be a smart watch/bracelet or a smart helmet.
  • the wearable device 3 can be carried by the user twenty-four hours a day or during a specific time period.
  • the wearable device 3 can be worn on the user's wrist. Preferably, the wearable device 3 can also be worn on other body parts of the user.
  • the wearable device 3 can also collect the user's physiological data.
  • Physiological data includes at least heart rate, blood pressure, pulse oximetry, body temperature, and associated timestamps.
  • the wearable device 3 can send physiological data to the server 2 .
  • the physiological data may also include information related to the user's activities, such as the number of steps the user walks every day.
  • the physiological data may also include the shaking frequency and amplitude of the user's body parts (eg, hands).
  • the shaking frequency and amplitude of the user's body part can be obtained by collecting the accelerometer in the wearable device 3 .
  • an intelligent environment equipment unit 5 can also be included, and the intelligent environment equipment unit 5 can be used to obtain daily data in the user's daily life.
  • the daily data is the user's behavior data collected by the intelligent environment equipment unit 5 all day long or during a specific period of time.
  • the above behavioral data can be behaviors related to user cognitive ability assessment.
  • the above behavioral data can be used to evaluate other behaviors related to the user's physical health.
  • the intelligent environment device unit 5 may include but is not limited to the following devices: water immersion sensor, ultrasonic sensor, voice recorder, microwave sensor, etc.
  • daily data can be behavioral data collected by a water immersion sensor in the toilet (such as monitoring the frequency of forgetting to turn off the faucet) to represent the user's memory; ultrasonic sensors set within the range of the user's daily behavioral activities can achieve distance measurement of the user. Microwave sensors can determine the user's movement, thereby obtaining the user's distance and movement information to mark the user's orientation and visual spatial ability; the voice recorder set within the scope of the user's daily activities records the User voice information can be used to determine the user's attention based on speech frequency, speaking speed, and voice information. It can also evaluate the user's instant or delayed speech memory based on information such as repeated sentences and keywords.
  • daily data may also include risk factors and physiological indicators.
  • risk factors may include but are not limited to: frequency of smoking, frequency of drinking, etc.
  • physiological indicators may include but are not limited to: the health status of the heart, the health status of the brain, etc.
  • daily data may also include other movement data required for cognitive ability assessment.
  • a digital evaluation unit 4 can also be included.
  • the digital evaluation unit 4 is configured to monitor the interaction process between the user and the digital evaluation unit 4 to capture the user's interaction data, language data, eye movement data and/or pupil data.
  • the digital evaluation unit 4 can send interaction data, language data, eye movement data and/or pupil data to the server 2 so that the server 2 can calculate the user's evaluation data based on the interaction process.
  • the digital evaluation unit 4 can recognize and extract speech patterns in used speech commands, words and phrases. Preferably, after performing signal processing on the speech, the digital evaluation unit 4 can extract phonemes and repeated combinations of phonemes. Preferably, the digital evaluation unit 4 can identify and extract features of each speech pattern, including pitch, amplitude and spectrum.
  • the eye movement data is data related to the user's eye movement.
  • the eye movement data may include, but is not limited to: data related to eye movements (or eye movement patterns) such as gaze, saccades, and following.
  • the eye movement data may also include: the user's gaze point position, gaze time and pupil diameter.
  • the above eye movement data can be obtained by monitoring the user's eye behavior with an electrooculogram sensor or other related eye movement sensors.
  • the mobile device provided with or integrated with the digital evaluation unit 4 there are at least two ways of linkage between the digital chess and card 1 and the mobile device provided with or integrated with the digital evaluation unit 4: 1) the mobile device provided with or integrated with the digital evaluation unit 4 and the intelligent trajectory analysis sensor 102, etc. respectively. It is transmitted to the gateway through Bluetooth or wirelessly, and then the gateway transmits it to the server 2 through wired or wirelessly; 2) The data collected by the intelligent trajectory analysis sensor 102 is transmitted to the digital evaluation unit 4 through Bluetooth or wirelessly, and the digital evaluation unit 4 transmits it to the digital evaluation unit 4 through Bluetooth or wirelessly. Transmitted to the gateway, which transmits it to the server 2 through wired or wireless methods.
  • the interaction data, language data and eye movement data are acquired by the digital evaluation unit 4 .
  • the acquisition methods include at least data reception and manual input.
  • the digital evaluation unit 4 can be provided in or integrated into the mobile platform.
  • mobile platforms include but are not limited to mobile phones or tablet computers.
  • the digital evaluation unit 4 can set or integrate corresponding sensors according to actual needs to obtain the user's interaction data, language data, eye movement data and/or pupil data.
  • the user can also link with the digital chess and card 1 through the digital evaluation unit 4 .
  • the digital evaluation unit 4 can issue an evaluation instruction to the user; for example, the evaluation instruction can be "Please name the vacant mahjong in the screen used by the digital evaluation unit 4, and take out the vacant mahjong from the digital chess card 1", and then digitize
  • the evaluation unit 4 collects interaction data and language data during the user's answer process; at the same time, the digital chess and card 1 moved by the user collects its own motion data.
  • the language data is not limited to the user's voice information, but also includes language and text information generated by user interaction.
  • Language and text information is natural language and text information, including at least the languages and characters of multiple countries, numerical symbols, image symbols with linguistic meanings, etc.
  • the digital evaluation unit 4 can wirelessly transmit the user's language data it obtains directly to the server 2 through Bluetooth technology or the like.
  • the digital evaluation unit 4 can obtain the user's motion data collected by the digital chess and card 1, and jointly evaluate the user's cognitive ability based on the above motion data and the language data collected by the digital evaluation unit 4 itself.
  • the digital evaluation unit 4 can evaluate the user's cognitive ability based on the clarity of the user's voice information, the smoothness of the movement trajectory of the chess and card entities recorded by the intelligent trajectory analysis sensor 102, and the length of time to complete an evaluation unit.
  • the digital evaluation unit 4 can evaluate the user's cognitive ability based on whether the information input by the user into the digital evaluation unit 4 meets the information input standard.
  • Typing standards include but are not limited to speech intelligibility, whether the track memory is clear, the length of the test time, whether the typed information exceeds the specified range (for example, numbers between 1 and 9 should be entered, but the actual input exceeds this range), etc.
  • the digital evaluation unit 4 is installed or integrated into a game APP in a mobile device (such as a mobile phone, a tablet, etc.), which can not only obtain the language data or voice information of the interactive Q&A between the user and the game APP, but also can
  • the user's interaction data is obtained through the digital chess and card 1 that is linked or data-connected to the digital evaluation unit 4 .
  • the interaction data at least includes action information, trajectory information, and parameter information related to the action information and trajectory information.
  • Parameter information related to action information and trajectory information is, for example, the interval time between two actions, the degree of continuity of actions, click frequency, etc. For example, if users are asked to draw a clock through the digital evaluation unit 4, some users may have difficulties when they first start drawing.
  • this method uses the screen of the digital evaluation unit 4 and the electronic stylus to capture the following Characteristics: the force with which the electronic stylus touches the touch screen when the user uses the electronic stylus to draw on the touch screen of the digital evaluation unit 4, the speed and/or pause time of the tip of the electronic stylus pen across the touch screen when drawing, the user's usage
  • the type data such as the shape of the handwriting formed by the electronic stylus is saved to itself or sent to the server 2, so as to conduct a preliminary digital assessment of the user's cognitive ability through its own server 2.
  • the evaluation data formed by the digital preliminary evaluation of the user's cognitive ability includes but is not limited to: when the user uses the electronic stylus to draw a picture on the touch screen of the digital evaluation unit 4, the intensity parameter of the electronic stylus touching the touch screen, drawing pictures The speed and/or pause time of the tip of the electronic stylus pen across the touch screen, the shape of the handwriting formed by the user using the electronic stylus pen, the clarity of the user's voice when interacting with the digital evaluation unit 4 during the evaluation process, digital chess and card games 1. Whether the trajectory formed by the movement is smooth, the time required to complete an evaluation, etc.
  • the digital evaluation unit 4 or the server 2 can compare data information such as the gesture trajectory formed when the user uses the digital evaluation unit 4, the intensity of using the electronic stylus, etc., so as to perform horizontal comparison with other people with normal cognitive abilities.
  • the digital evaluation unit 4 or the server 2 can monitor and evaluate the change trend of the elderly's cognitive ability in different periods, thereby avoiding the evaluation of subjects with originally low cognitive abilities as cognitive abilities. Inadequate knowledge and ability.
  • the digital assessment unit 4 focuses on evaluating the user's cognitive abilities in the following aspects: attention, visuospatial ability, abstract ability, executive ability, immediate memory, delayed memory, language ability, and orientation ability.
  • the digital evaluation unit 4 can cut a mahjong tile picture into several pieces, and then shuffle the order, and then let the elderly determine which mahjong tile these fragmented pictures are cut from, thereby selecting from several mahjong pieces. Corresponding mahjong tiles to obtain assessment data corresponding to the visuospatial ability of the elderly.
  • the digital evaluation unit 4 can be digitized or integrated into a game APP on a mobile platform or other evaluation devices.
  • the user's interaction data and/or language data can be obtained through the user's interaction with the digital evaluation unit 4 or the game APP of the mobile platform or other evaluation devices, so as to score or evaluate the user's cognitive ability. .
  • the main evaluation processes of the digital evaluation unit 4 are: data collection, speech recognition, logic verification, rule learning, and scoring.
  • Both the digital evaluation unit 4 and the server 2 may have data processing functions. When the data processing functions of the digital evaluation unit 4 and the server 2 are different, they can be divided into two stages.
  • the first phase of the functionality of the digital evaluation unit 4 includes the following steps:
  • the digital assessment unit 4 can determine whether it is within the prescribed numerical range (such as whether the date of birth corresponds to the ID card, the score of each cognitive assessment ability) Whether it is within the specified value range, whether the input data is clear, timeout, etc.); if the input information is within the specified value range and there is no contradictory information, the logic verification is completed;
  • the second phase includes the following steps:
  • server 2 Based on multi-modal data fusion technology, server 2 performs data fusion on the language data obtained by the voice collection device and the sensor data cognitive ability score data obtained by the intelligent environment collection device;
  • the digital evaluation unit 4 is integrated into the game APP of the mobile platform. Not only can the question and answer information be obtained, but also the user's action information when using the game APP, and the information formed by using the stylus on the electronic screen can be obtained. Trajectory information, etc.
  • the digital evaluation unit 4 by allowing the user to draw a clock on a game APP (such as a tablet), the corresponding sensors on the mobile platform can capture the user's action information, trajectory information, etc. when drawing the clock. For example, some users slowly draw the clock. Difficulties may arise.
  • this method uses corresponding sensors on the mobile platform to capture at least (but not limited to) the following characteristic data of the user: the intensity of using the stylus, the speed of drawing the clock, the length of pause, The accuracy of digital expression, the trajectory formed by the stylus on the touch screen, the dwell time of each stroke and the tremor of the hand, etc., are used to achieve digital evaluation of the user's cognitive ability through the above characteristic data.
  • the digital evaluation can obtain more diverse dimensions of the user's characteristic data, and the collected data categories are more comprehensive, so as to achieve a more comprehensive and objective evaluation of the user's cognitive ability.
  • interactive information such as voice can be established between the digital evaluation unit 4 in the mobile platform and the user to obtain the user's language data (especially when capturing mahjong embedded with the intelligent trajectory analysis sensor 102).
  • the user is asked a question through the digital evaluation unit 4 of the mobile platform: "Please name the mahjong tiles that are vacant on the current page.”
  • the digital evaluation unit 4 performs semantic conversion on the voice information used for the answer to obtain the user's answer;
  • the digital evaluation unit 4 determines whether the answer is within the specified range.
  • the score is determined (whether it is correct and the score-losing items are determined in advance by the digital evaluation unit 4), and is transmitted to the server 2; for input
  • the information is directly judged and then sent to the server 2 (similar to the operation after semantic conversion of the speech information); for the trajectory information, the information needs to be sent to the server 2, and the score is judged based on the machine learning decision algorithm.
  • the digital evaluation unit 4 is also installed or provided with an early warning module and a display module.
  • the early warning module can compare the cognitive ability score of the user corresponding to the current period with the cognitive ability score of the user corresponding to the previous period, and The early warning module can generate the first early warning information when the user's cognitive ability score drops by more than a preset trigger threshold, and the first early warning information can be displayed through the display module.
  • the comparison period in which the evaluation unit regularly compares the user's cognitive ability score corresponding to the current period with the user's cognitive ability score corresponding to the previous period can be set artificially.
  • the preset trigger threshold can be set manually.
  • the preset trigger threshold may be 3% of the user's cognitive evaluation score in the previous period.
  • the early warning module of the evaluation unit may be configured to use at least one of days, weeks, months, quarters and years as a period.
  • the early warning module of the evaluation unit may be configured to compare the cognitive ability scores of the corresponding users on a daily basis.
  • the early warning module of the evaluation unit can be configured to compare the cognitive ability scores of the corresponding users on a daily and weekly basis. That is, the early warning module of the evaluation unit not only compares the cognitive ability scores of corresponding users in two adjacent days, but also compares the cognitive ability scores of corresponding users in two adjacent weeks.
  • the early warning module of the evaluation unit can regularly compare the cognitive ability score of the user corresponding to the current period with the cognitive ability score of the user corresponding to the previous period in at least two comparison periods that are different from each other. Different comparison periods may correspond to different preset trigger thresholds from each other. For example, some users play cards quickly in the early stages of using digital chess and cards 1, but their playing speed gradually slows down as the test time goes by (for example, it shows a slowdown trend for several consecutive months, one or two years) , the early warning module of the evaluation unit may determine that the user has cognitive decline.
  • monitoring and evaluation can be carried out based on the change trend of the user's cognitive ability in different periods, that is, the longitudinal change data of the user's cognitive ability. For example, by comparing user A's data today with the data the user will be testing next week, and the next week, it is possible to monitor the changing trends in evaluation ability, thereby avoiding the need to evaluate users with lower cognitive abilities. Due to insufficient cognitive ability.
  • the digital evaluation unit 4 or the server 2 can respectively obtain the user's movement data when using the digital chess and card 1, the user's language data obtained by the user using the digital evaluation unit 4, and the user's language data obtained by the intelligent environment equipment unit 5.
  • Daily data, the user's subsequent data analysis is used to obtain digital biomarkers that may represent changes in the user's cognitive ability or status.
  • the process of data analysis by the digital evaluation unit 4 or the server 2 is: input the user's motion data when using the digital chess and card 1 into the deep learning network to extract feature data;
  • the language data is converted into feature data through technical means such as Fourier transform and frequency domain analysis, and the feature data is further extracted from the above data through stack auto-encoding; the user's daily data obtained by the intelligent environment equipment unit 5 is learned through ontology way to extract possible feature data from it.
  • the process of data analysis i.e., extraction of digital biomarkers
  • input the user's movement data i.e., sequential signals
  • extract the user's characteristic data or indicators through the deep learning network .
  • target attributes include at least but not limited to: the ability to perform all cognitive processes.
  • target attributes may also include: the ability to perform mental processes.
  • the target attributes may also include: learning and judgment, language and memory abilities.
  • the digital evaluation unit 4 or the server 2 performs feature selection on all the above feature data through a feature selection method to screen out possible digital biomarkers, and then selects the above possible digital biomarkers based on marginal contribution analysis. Rank by importance.
  • the feature selection method may include at least one or more of partial least squares, variational autoencoders, and adversarial network learning.
  • the feature selection method can also adopt other categories of methods.
  • reference markers can be selected based on specific medical screening/assessment needs.
  • the valid digital biomarkers analyzed by the server 2 are: the user's hand movement trajectory, the frequency and/or duration of the user's hand pauses.
  • Server 2 can use the above-mentioned valid digital biomarkers as reference markers for screening brain health.
  • the server 2 can obtain the score of the user's cognitive ability from the digital evaluation unit 4 on the mobile platform in real time to assist in identifying or mining digital creatures that can be used to identify the decline (or change) of the cognitive level of the human user. landmark.
  • possible uses can be extracted from the user's movement data when using the digital chess and card 1, the user's language data recorded when using the digital evaluation unit 4, and the user's daily data obtained by the intelligent environment equipment unit 5.
  • a causal analysis knowledge base is constructed based on the acquired characteristic data, and causal inference is performed on the digital biomarkers through the established causal analysis knowledge base based on the causal analysis theory to mine the user's cognitive ability or ability that can be effectively identified.
  • Digital biomarkers of state changes.
  • Cognitive ability assessment method based on digital chess and card 1.
  • the steps of the method are: obtaining at least the user's movement data through the digital chess and card 1; obtaining at least the user's language data through the digital evaluation unit 4; obtaining the user's daily data through the intelligent environment equipment unit 5 ; Server 2 obtains the above-mentioned sports data, language data and daily data to extract possible digital biomarkers; establishes a causal relationship knowledge base based on the above-mentioned possible digital biomarkers to calculate and analyze to obtain reference markers.
  • reference markers can be selected based on specific medical screening/assessment needs.
  • the reference marker may be a digital biomarker that can effectively identify changes in the user's cognitive ability or status.
  • the main characteristic parameters may include, but are not limited to, possible digital biomarkers obtained by the server 2 in the previous steps.
  • the data set may include but is not limited to voice feature data, behavioral feature data, risk factor data, physiological index data, etc. acquired by the server 2 .
  • the behavioral characteristic data may include movement data of the digital chess and card 1.
  • the behavioral characteristic data may also include other behavioral information of the user collected by the wearable device 3, such as the number of steps the user walks every day, sleep time and other data information.
  • the causal unit can also analyze the direct causal effects between cognitive abilities through data patterns, so that the knowledge unit can form a correspondence between cognitive abilities and the direct causal effects between cognitive abilities based on relevant literature. way to build a knowledge base.
  • the main characteristic parameters used by the causal unit may include but are not limited to: the user's motion data when using the digital chess and card 1, the user's language data recorded when using the digital evaluation unit 4, and the user's language data obtained by the intelligent environment device unit 5 daily data.
  • the document unit is based on the acquisition of a large number of relevant documents covering a variety of cognitive abilities.
  • the document unit classifies relevant documents to form several document units to build the original document database.
  • the relevant documents include medical records, research reports, conference documents, journal documents, books, academic papers and patents.
  • the purpose of document classification is to effectively observe the correlation between cognitive abilities and reduce the load on the system. For example, it can be classified according to digestive tract diseases, cardiovascular diseases, neurological diseases, etc. It can also be classified according to academic fields, such as rehabilitation and psychology, etc.
  • the document classification can adopt Bayesian method, SVM method and k-NN method.
  • the causal unit is tested. For example, a chi-square test of independence can be used to test independence.
  • a chi-square test of independence can be used to test independence.
  • data sources such as the user's motion data when using the digital chess and card 1, the user's language data recorded when using the digital evaluation unit 4, and the user's daily data obtained by the intelligent environment equipment unit 5.
  • depression and habitual sleep inefficiency are both valid/important numerical biomarkers of cognitive decline, but the causal relationship between depression and cognitive decline is stronger than that of habitual sleep inefficiency.
  • Causal links between sleep inefficiency and cognitive decline may be inferred through the causal knowledge base that depression and habitual sleep inefficiency are both valid/important numerical biomarkers of cognitive decline, but the causal relationship between depression and cognitive decline is stronger than that of habitual sleep inefficiency.
  • increased intensity of learning activities and increased frequency of physical exercise are valid/important digital biomarkers of improved cognitive abilities.
  • the frequency of hand shaking when a user uses digital chess and cards 1 increases in a certain period or a short period of time, which is an effective digital biomarker for the decline in the user's cognitive ability.
  • the decision-making time each time shows a significant increase trend.
  • the frequency of a user's hand shaking can have a direct causal relationship with symptoms such as reduced movement, myotonia, tremor, and postural adjustment disorders, and these symptoms can lead to paralysis angitas (Parkinson's disease), which affects the patient's mobility, mobility, and posture. Attention, orientation, and visuospatial ability may all have direct causal effects.
  • symptoms such as reduced movement, myotonia, tremor, and postural adjustment disorders
  • the digital evaluation unit 4 or the wearable device 3 collects that the number of times the user has multiple pauses in speaking in a short time/recent period, often utters consonants such as "um ah ah", has obvious difficulty in expressing, etc. exceeds the normal reference value, It may be a sign of the decline of the user's language nervous system and can be used as an effective digital biomarker of cognitive decline.
  • the normal reference value can be obtained by server 2 or other means with normal cognitive ability or the average/median value of normal people.
  • the server 2 can also generate a causal network model based on the relevant variable characteristics for causal inference to identify the causal relationship between the relevant characteristic variables related to the change in the user's cognitive ability and the change in cognitive ability, as well as the causal relationship.
  • the strength of the relationship can provide medical personnel or medical researchers with an effective way to understand the effective digital biomarkers or pathological causes that cause changes in human individual cognitive abilities.
  • the server 2 can also update the digital assessment unit 4 or the server 2 to further accurately screen/assess brain health based on the mined effective digital biomarkers.
  • the mined effective digital biomarkers can also be used as the basis for subsequent digital assessment unit 4 or server 2 as a brain health (such as Alzheimer's disease) risk assessment platform.
  • the present invention also provides a data processing method based on digital biomarkers.
  • the method is:
  • the digital chess and card 1 collects the movement data of the digital chess and card 1 when the user uses the digital chess and card 1;
  • the server 2 obtains the motion data collected by the digital chess and card 1, calculates and analyzes the motion data to obtain digital biomarkers corresponding to the reference markers, and evaluates/screens the user's brain health based on the digital biomarkers.
  • the server 2 can access the valid reference markers previously recorded or analyzed by the server 2, and compare the current digital biomarkers calculated and generated by the server 2 with the reference markers or previous valid digital biomarkers. differences, and based on the assessment/screening of the user's brain health based on the differences. For example, the server 2 can predict users' changing trends within a specified time range in the future based on users whose cognitive abilities have declined among different groups of people.
  • the reference marker is the pause time and/or pause frequency of the user's hand when moving the digital chess card 1 obtained by the server 2's analysis of the previous raw data.
  • the server 2 can regularly or irregularly compare the occurrence frequency and/or intensity of occurrence of the user's digital biomarkers corresponding to the current period with the occurrence frequency and/or occurrence of the user's digital biomarkers corresponding to the previous period.
  • Strength of. The period can be set manually, such as weekly.
  • the server 2 identifies the frequency of occurrence of digital biomarkers within a certain time period/period (the intensity and/or frequency of the user's hand shaking when using the digital chess and card 1, or the pause time each time during the use of the digital chess and card 1 /decision time) exceeds the preset threshold, the server 2 determines that the user's cognitive ability is likely to decline at this time.
  • the preset threshold can be inferred by server 2 through the causal relationship knowledge base.
  • server 2 identifies the occurrence of a certain digital biomarker within a certain time period/period (the user has multiple pauses in speaking in a short period of time/recently, often utters consonants such as "um ah ah", obvious difficulty in expression, etc.) When the frequency exceeds the preset threshold, the server 2 determines that the user's cognitive ability is likely to decline at this time.
  • the reference marker is obtained by the following steps:
  • the original data is obtained by the following steps:
  • the digital chess and card 1 collects the movement data of the digital chess and card 1 when the user uses the digital chess and card 1;
  • Wearable device 3 collects users’ daily physiological data
  • the digital evaluation unit 4 obtains the user's evaluation data, language data and eye movement data
  • the intelligent environment equipment unit 5 collects daily data in the user's daily life
  • the server 2 obtains one or more of motion data, other physiological data, assessment data, language data, eye movement data and daily data as original data.
  • Embodiment 1 is a further improvement of Embodiment 1, and the repeated content will not be described again.
  • This embodiment provides a digital biomarker.
  • the initial data of the digital biomarker is at least composed of collecting behavioral data of various operations of the user using digital chess and cards 1 such as mahjong, poker, chess, Go, etc. as initial data; the collected initial data will be sent to the analysis unit 103 and/or Server 2, the initial data will be analyzed and calculated by the analysis unit 103 and/or the artificial intelligence program on the server 2 to obtain a series of digital biomarkers that can represent the user's cognitive abilities, such as perceptual cognition, sensory cognition, thinking cognition, etc.
  • the obtained digital biomarkers can be marked against the real-time data collected when users use digital chess and cards 1, and the user's cognitive ability can be evaluated through marking.
  • the movement data of the user moving the digital chess and card 1 and the touch data of the user's contact with the digital chess and card 1 can be used as the user's behavior data towards the digital chess and card 1 .
  • the movement data of the user using the digital chess card 1 may include the user's operations of moving the digital chess card 1 and rotating the digital chess card 1 .
  • the movement data of the user using the digital chess and card 1 may also include at least the operations of the user lifting the digital chess and card 1 and dropping the digital chess and card 1, the user touching the digital chess and card 1 to cause the digital chess and card 1 to tip over, and the user righting the tilted digital chess and card 1. Operation, such as digital chess and card 1 shown in Figure 5.
  • the touch data of the user's contact with the digital chess and card 1 may include the magnitude, direction and frequency of the force exerted by the user on the digital chess and card 1 , including the magnitude, direction and frequency of the force generated by the user's use of the digital chess and card 1 .
  • the digital biomarkers obtained after analysis can characterize user attributes, and user attributes can include but are not limited to: cognitive status/ability, action status/ability.
  • user attributes may also include, but are not limited to, learning and analysis abilities, expression abilities, memory abilities, and mental endurance.
  • ordinary physical servers and/or cloud servers can serve as the server 2.
  • the server 2 can analyze the collected initial data including but not limited to behavioral data and extract digital biomarkers, and use the digital biomarkers to Assess the user's health status.
  • the health state may include: cognitive abilities, such as observation, attention, and imagination.
  • the health status may also include: learning ability, analytical ability, expression ability, memory ability and mental endurance.
  • health status may also include whether one suffers from other diseases.
  • the digital chess and card 1 can be various chess and card entities, such as mahjong, chess, chess, Go, poker, etc.
  • the digital chess and card 1 may include a chess and card entity 101 and an intelligent trajectory analysis sensor 102 .
  • the number of digital chess and cards 1 is determined by the rules of various chess and cards.
  • the chess and card entity 101 of the digital chess and card 1 is equipped with an intelligent trajectory analysis sensor 102, or the intelligent trajectory analysis sensor 102 is integrated into the chess and card entity 101, so that the chess and card entity 101 of the digital chess and card 1 and There is no obvious difference in appearance between ordinary chess and cards.
  • the intelligent trajectory analysis sensor 102 can at least be used to obtain the spatial coordinates of the chess and card entity 101 during use by the user and the timestamp associated with the spatial coordinates, and send the above information to the analysis unit 103 and the server 2 .
  • the process of the user using the chess and card entity 101 may include: moving the chess and card entity 101, rotating the chess and card entity 101, lifting the chess and card entity 101, dropping the chess and card entity 101, falling over the chess and card entity 101, and the user righting the chess and card entity 101.
  • the process of the user using the chess and card entity 101 may also include: the magnitude, direction and frequency of the force exerted on the chess and card entity 101 when touching the chess and card entity 101, and the use of the force generated by the chess and card entity 101 on other objects such as the chess and card platform. size, direction and frequency.
  • the chess entity 101 can be a chess piece, especially a chess piece with a three-dimensional space volume.
  • the chess and card entity 101 can also be other types of chess and cards such as mahjong, Go, chess, poker, etc.
  • the intelligent trajectory analysis sensor 102 integrated or arranged inside the chess and card entity 101 can be used to obtain the three-dimensional spatial coordinate data of the digital chess and card 1 and can transmit the acquired three-dimensional spatial data to the server 2 and/or the analysis unit 103 .
  • the intelligent trajectory analysis sensor 102 integrated or arranged inside the chess and card entity 101 can capture the spatial trajectory data of the user's mobile digital chess and card 1 in a three-dimensional space, such as the sky above the chess and card platform, and/or a two-dimensional plane, such as the chess and card platform, the user's movement of the digital chess and card 101.
  • the spatial trajectory data of touching the digital chess piece 1 causes the digital chess piece 1 to tip over and righting the fallen digital chess piece 1 .
  • the interval time for the user to grab the chess and card entity 101 the pause duration during the user's grabbing of the chess and card entity 101, the distance the user moves the chess and card entity 101 under the corresponding time length, the user's movement of the chess and card entity 101 relative to the normal placement.
  • the angular changes of the chess and card entity 101 and the initial and final positions of the chess and card entity 101 relative to the user's palm can be used as the spatial running trajectory data that the intelligent trajectory analysis sensor 102 needs to capture.
  • the user can wear a portable smart device 105 that matches the digital chess card 1 and is configured with an identifier that can send the user's identity information to the smart trajectory analysis sensor 102 .
  • the intelligent trajectory analysis sensor 102 in the digital chess and card 1 can be connected wirelessly with the portable intelligent device 105 through Bluetooth, LAN, etc., and the intelligent trajectory analysis sensor 102 obtains the identity information identifier configured on the portable intelligent device 105 through this connection method. symbol.
  • Portable smart wearable devices 105 may include: smart watches, smart bracelets, smart rings, smart necklaces, smart helmets, etc.
  • the portable smart device 105 can be worn on the user's wrist, such as a smart watch or a smart bracelet.
  • the portable smart device 105 can also be worn on other body parts of the user, for example, a smart ring can be worn on the user's finger, a smart necklace can be worn on the user's neck, and a helmet can be worn on the user's head.
  • the user can wear a portable smart device 105 that matches the digital chess card 1 and is configured with an identifier that can send the user's identity information to the smart trajectory analysis sensor 102 .
  • the smart trajectory analysis sensor 102 can search through the overlay.
  • the surrounding portable smart devices 105 establish a connection with the portable smart device 105 closest to the smart trajectory analysis sensor 102 in a straight line and obtain the identity information identifier configured on the portable smart device 105 .
  • the portable smart device 105 can autonomously collect and summarize the user's physiological data and/or the user's individual characteristic data, or can also manually input and have the portable smart device 105 summarize and analyze the user's physiological data and/or the user's individual characteristics.
  • Feature data The user's physiological data may include heart rate, blood pressure, blood oxygen saturation, body temperature, and time stamps corresponding to each feature.
  • the physiological data is collected and analyzed by the portable smart device 105 and then sent to the server 2.
  • the server 2 uploads the data based on the portable smart device 105.
  • Physiological data determines the user's current physiological state.
  • the user's individual characteristic data may include familiarity with the chess and card entity 101 that the user is using, the user's ability to think independently (IQ), and disease history information that may have an impact on cognitive ability.
  • the portable smart device 105 is equipped with an acceleration measurement device inside.
  • the acceleration measurement device can enable the portable smart device 105 to obtain information related to user activities, such as daily exercise status.
  • the motion status may include the user's daily walking steps, the user's running distance, the user's jumping frequency and corresponding timestamps.
  • the motion state may also include the motion of the user's body parts, such as the shaking frequency, shaking amplitude and corresponding timestamp of the user's body parts.
  • the user's body parts may include hands, legs, waist, shoulders and neck.
  • user activity-related information obtained through the acceleration measurement device configured in the portable smart device 105 can be autonomously collected by the portable smart device 105 and incorporated into physiological data.
  • it also includes an intelligent monitoring unit 104 capable of acquiring daily data in the user's daily life and sending the daily data to the server 2 that remains connected to the intelligent monitoring unit 104 .
  • the intelligent monitoring unit 104 can be selected by the user to collect the user's behavior information for twenty-four hours a day or a specific period as daily data.
  • the daily data can be sent to the server 2 by the intelligent monitoring unit 104, and the server 2 determines the user's real-time status based on the user's daily data uploaded by the intelligent monitoring unit 104.
  • the real-time status may include the current user's cognitive ability status, such as observation, attention, and imagination.
  • the real-time status may also include the user's current physical health status.
  • the intelligent monitoring unit 104 may include but is not limited to the following devices: water immersion sensor, ultrasonic sensor, microwave sensor, voice recorder, etc.
  • daily data may include user behavior information collected by water immersion sensors installed in user toilets, kitchens and/or other areas connected to water sources, such as the frequency of forgetting to turn off the faucet and the length of time between turning on the faucet and turning off the faucet, etc.
  • this kind of daily data can be used as a basis for evaluating the user's memory status; the user's position coordinates can be determined through ultrasonic sensors, and the user's movement can be determined through microwave sensors, thus through ultrasonic sensors and microwave sensors set within the range of the user's daily activities.
  • daily data may also include risk factors and physiological indicators.
  • risk factors may include but are not limited to: frequency of smoking, frequency of drinking, etc.
  • physiological indicators may include but are not limited to: the health status of the heart, the health status of the brain, etc.
  • daily data may also include other movement data required for cognitive ability assessment.
  • an analysis unit 103 installed or integrated inside a mobile intelligent platform such as a smart phone or a tablet.
  • the analysis unit 103 can record and analyze the interaction process between the user and the analysis unit 103, and sort out and summarize the interaction data between the user and the analysis unit 103, the user's language data, the user's eye movement data, and the user's pupil data, and through analysis
  • the unit 103 sends the above data to the server 2.
  • the server 2 analyzes and evaluates the interaction process between the user and the analysis unit 103, and stores the evaluation data obtained from the backup analysis.
  • the interaction data is generated when the user manipulates the chess and card entity 101 of the digital chess and card 1 or interacts with the analysis unit 103, such as click data, frequency data, action continuous data, trajectory data, information content selection data, and sliding selection. Data, content changes, logical relationship data and other data.
  • the analysis unit 103 provided or integrated in the mobile smart device can identify, analyze, and extract language data used to analyze and determine the user's emotional state, emotional information, and other information.
  • the language data may include voice information and image and text information generated by the user's interaction with others or with the smart device through the mobile smart device.
  • the graphic and text information generated by the user collected by the analysis unit 103 in the mobile smart device may include graphic and text information for communication, language and/or pictures or texts expressing language, such as emoticons that are now commonly used. , emoticons and other graphic information that can express the user's emotions more vividly.
  • the analysis unit 103 can connect to the network to identify and analyze image and text information, thereby directly or indirectly obtaining the user's real-time emotional state and emotional information, etc., thereby facilitating the judgment of the user's real-time cognition. The state of the ability.
  • the voice information is the voice when the user interacts with the analysis unit 103 set or integrated in the mobile smart device.
  • the user usually communicates with others through the mobile smart device or directly communicates with the mobile smart device.
  • the voice information generated by the communication includes voice. Commands, word speech, phrase speech, etc.
  • the analysis unit 103 can perform low-level processing on speech and extract different phonemes and repeated combinations of phonemes in the speech, thereby identifying and extracting the characteristics of each speech, including volume level, pitch conversion, audio pitch, etc., the analysis unit 103 Such data collected and analyzed can be sent to server 2.
  • the eye movement data can be acquired by an electrooculogram sensor or other related eye movement sensors provided in the analysis unit 103 .
  • the electrooculogram sensor or other related eye movement sensors in the analysis unit 103 can capture the user's eye movement status through the camera of the mobile smart device.
  • the eye movement data may include but is not limited to: data related to the movement of the user's eyeballs during gaze, saccade, and following.
  • the eye movement data may also include: the coordinates of the fixation point when the user's eyeballs fixate, the duration of stay at the fixation point, pupil data of the fixation point, etc.
  • the eye movement data acquired by the analysis unit 103 through the electrooculogram sensor or other related eye movement sensors may be sent to the server 2 through the analysis unit 103 .
  • the user's pupil data captured by the electrooculogram sensor or other related eye movement sensors in the analysis unit 103 through the camera of the mobile smart device includes but is not limited to the user touching the chess and card entity 101, hearing or A series of changes in the pupil when seeing the chess and card entity 101 played by others, such as changes in pupil size, change speed, and focus frequency.
  • the analysis unit 103 set or integrated inside the mobile smart device can autonomously collect interaction data, language data, eye movement data and pupil data with the user through the mobile smart device.
  • the analysis unit 103 can also manually collect data from the mobile smart device.
  • the input terminal is input and summarized by the analysis unit 103.
  • the analysis unit 103 can be disposed or integrated in a mobile smart device such as a mobile phone or a tablet computer.
  • the analysis unit 103 provided or integrated inside the mobile smart device can independently analyze the operating status of the electrooculogram sensor or other related eye movement sensors inside the unit 103 on the mobile smart device according to the actual needs of the user to obtain the response that the user needs. interaction data, language data and/or eye movement data.
  • the analysis unit 103 provided or integrated inside the mobile smart device can obtain input information generated when interacting with the mobile smart device carrying the analysis unit 103 and compare the input information with the analysis unit 103 and/or the server. 2. Compare the information input standards established to evaluate the user's cognitive ability.
  • the information input standards formulated by the analysis unit 103 and/or the server 2 may include: semantic clarity, whether the trace memory is clear, the length of the test time, whether the input information exceeds the specified range (for example, a number in the range of 1 to 9 should be input, but the actual input is beyond this range) etc.
  • the analysis unit 103 in a leisure APP in a mobile smart device can be obtained through the mobile smart device.
  • the analysis unit 103 provided or integrated inside the mobile smart device can also capture action information and trajectory information that occur when the user interacts with the chess and card entity 101 by interacting with the intelligent trajectory analysis sensor 102 in the digital chess and card 101 .
  • the user is allowed to draw a clock or draw the appearance of the current chess and card entity 101 on a leisure APP that is set up or integrated inside the mobile smart device or integrates the analysis unit 103.
  • the user's operation data on the APP may include, but is not limited to: the intensity with which the user touches the touch screen with the electronic stylus when drawing on the touch screen of the analysis unit 103, the movement of the nose of the electronic stylus on the touch screen when drawing.
  • the above operation data is saved to the analysis unit 103 itself or sent to the server 2 through the mobile smart device, so that the analysis unit 103 itself or the server 2 can digitize the user's cognitive ability and initially evaluate the health status.
  • the evaluation data formed by the analysis unit 103 and/or the server 2's preliminary evaluation of the digitalization and health status of the user's cognitive ability include but are not limited to: when the user uses an electronic stylus to draw on the touch screen of the analysis unit 103 The force with which the electronic stylus touches the touch screen, the speed and/or pause time of the tip of the electronic stylus across the touch screen when drawing, the shape of the handwriting formed by the user using the electronic stylus, the pressure exerted by the user on the electronic stylus The pressure on the body, the humidity when the user holds the body of the electronic stylus, the clarity of the user's voice when interacting with the analysis unit 103 during the evaluation process, whether the trajectory formed by the movement of the digital chess and card 1 is smooth, the time required to complete an evaluation Time etc.
  • the analysis unit 103 or the server 2 preliminarily determines the digitalization and health status of the user's cognitive ability by comparing the obtained evaluation data with the normal data generated by people in the same operation under normal conditions collected from big data
  • the analysis unit 103 or the server 2 can compare the gesture trajectories formed on the screen of the mobile smart device when the user uses the mobile smart device with internal settings or integrated with the analysis unit 103, but also the use of gestures corresponding to the mobile smart device.
  • Data information such as the strength of the electronic stylus is compared horizontally with data information collected by other people with normal cognitive abilities who perform the same operation through the analysis unit 103 or server 2 set or integrated inside the mobile smart device.
  • the physical health status can be preliminarily judged based on the user's pressure on the stylus body and whether the palm of the hand is sweating when holding the stylus, and more importantly, the analysis unit 103 or the server 2 can based on the user's different Monitor and evaluate the changing trends of their cognitive abilities and physical status over time, thereby avoiding evaluating test subjects with originally low cognitive abilities as having insufficient cognitive abilities and simply predicting the user's physical status.
  • a picture of a chess and card entity 101 such as a mahjong tile can be randomly cut into several pieces, and then the order is disrupted, and then the user can Determine which mahjong tiles these fragmented pictures are cut from, and then select the corresponding mahjong tiles from a number of mahjong to obtain evaluation data corresponding to the user's visual spatial ability and thinking ability. For example, let the user convert simple mahjong tiles into The randomly cut fragments are completely spliced together to analyze the user's memory and visuospatial ability.
  • the user's operation, voice and other data can be obtained through the user's interaction with a mobile smart device with an internal or integrated analysis unit 103 or a leisure APP or other evaluation device on the mobile smart device, thereby identifying the user.
  • Score or evaluate cognitive abilities such as visuospatial ability, thinking ability or memory.
  • Both the analysis unit 103 and the server 2 may have data processing functions.
  • the data processing function of the analysis unit may be set to the first data processing stage, and the data processing function of the server 2 may be set to the second data processing stage.
  • the first data processing stage of the analysis unit 103 provided inside the mobile smart device is generally divided into the following steps: questions raised by the user or user feedback can be submitted to the analysis unit 103 inside the mobile smart device, and the analysis unit 103 will pass the user A preliminary judgment is made on the information submitted by the input terminal of the mobile smart device.
  • the preliminary judgment can include whether the date of birth corresponds to the ID card, whether the scores of each cognitive assessment ability are within the specified numerical range, whether the input data is clear, and the numerical range of timeout, etc.
  • the analysis unit 103 will perform a logical check to determine whether the information submitted by the user is inconsistent; after passing the logical check, the analysis unit 103 will compare the information submitted by the user with the cognitive ability score of the user's corresponding cognitive domain. Sent to the server 2, the server 2 sums the scores of each analysis unit 103 and compares the total score with the cognitive assessment level corresponding to the set score interval to realize the assessment of the subject's cognition.
  • the portable smart device 105 It is always connected to the portable smart device 105, the smart trajectory analysis sensor 2 inside the chess and card entity 101 of the digital chess and card 1, the analysis unit 103 set or integrated inside the mobile smart device, and the smart monitoring unit 104 distributed in the user's daily life space.
  • the second data processing stage of the server 2 may include the following steps: Based on multi-modal data fusion technology, the server 2 sends the user's motion data captured by the intelligent trajectory analysis sensor 102 inside the chess and card entity 101 of the digital chess and card 1 to the analysis unit 103 The interaction data, language data, eye movement data, pupil data, sensor data cognitive ability score data collected by the sensor of the intelligent monitoring unit 104, and the movement data of the user's body captured by the portable smart device 105 are used for data fusion; Server 2 After data fusion is completed, machine learning-based feature extraction methods (such as partial least squares, autoencoder algorithms and their derivative algorithms, adversarial network learning algorithms and their derivative algorithms, etc.) based on the fused data may be used to characterize user cognition.
  • machine learning-based feature extraction methods such as partial least squares, autoencoder algorithms and their derivative algorithms, adversarial network learning algorithms and their derivative algorithms, etc.
  • the analysis unit 103 is set or integrated on the leisure APP on the mobile smart device. Not only can the user communicate with the user through voice communication to obtain the question and answer information between the user and the analysis unit 103, but also through the mobile smart device.
  • the screen captures data information such as the user's action information when interacting with a mobile smart device or a casual APP on the mobile smart device, the intensity of using the electronic stylus corresponding to the mobile smart device, etc.
  • data information such as the user's action information when interacting with a mobile smart device or a casual APP on the mobile smart device, the intensity of using the electronic stylus corresponding to the mobile smart device, etc.
  • the user is allowed to draw a clock or draw the appearance of the current chess and card entity 101 on a leisure APP that is set up or integrated inside the mobile smart device or integrates the analysis unit 103.
  • this method More evaluation information can be obtained by evaluating the drawing clock, and user operations can be captured through sensors connected to the analysis unit 103 provided on the screen of the mobile smart device internally or integrated with the analysis unit 103 and on the electronic stylus that interacts with the mobile smart device. data.
  • the user's operation data on the APP may include but is not limited to: the strength of the user's electronic stylus touching the touch screen when drawing on the touch screen of the mobile smart device with built-in analysis unit 103, the intensity of the electronic stylus pen touching the touch screen when drawing, The speed and/or pause time of the tip of the nose crossing the touch screen, the shape of the handwriting formed by the user using the electronic stylus, the pressure the user exerts on the body of the electronic stylus, and the movement of the user when holding the body of the electronic stylus. humidity and other data, and save the above operation data to the analysis unit 103 itself or send it to the server 2 through the mobile smart device.
  • the acquisition of the above operation data is more comprehensive and multi-dimensional, and the extracted feature data is also more comprehensive and multi-dimensional.
  • the analysis unit 103 itself or the server 2 can also make a more comprehensive and objective assessment of the user's digital cognitive ability and health status through more comprehensive and multi-dimensional feature data.
  • the analysis unit 103 integrated inside the mobile smart device can establish voice, image and physical contact such as touch and other interactive means between the user and the user to obtain the information when the user grabs the digital chess and card 1 (especially when the user grabs the internal settings (When there is a chess and card entity 101 of the intelligent trajectory analysis sensor 102) language data, image data, etc.
  • the user can be asked questions related to the rules of the chess and card entity 101 of the current digital chess and card 1 through the analysis unit 103 set or integrated inside the mobile smart device.
  • the analysis unit 103 set or integrated inside the mobile smart device 103 can send voice questions or display text information to the user through the mobile smart device: "Please explain how many moves there are for this chess piece next.” If the user makes a voice answer, the analysis unit is set or integrated in the mobile smart device. 103 collects the voice through the earpiece device of the mobile smart device, and semantically converts the voice into information identifiable by the analysis unit 103; if the user types in the answer through the mobile smart device, the analysis unit 103 set or integrated inside the mobile smart device passes Get answers from inputs on mobile smart devices and convert them into self-recognizable information.
  • the analysis unit 103 inside the mobile smart device judges the answer information and scores the user's answer based on the judgment result, and stores the result locally and uploads it to the server 2.
  • the scores are also judged and scored, stored locally and uploaded to Server 2.
  • Server 2 can judge the scores through machine learning algorithms.
  • the user's movement data when using the digital chess and card 1 interaction data generated when the user interacts with the analysis unit 103, language data, eye movement data, pupil data, and the user's daily data obtained by the intelligent monitoring unit 104 can be obtained by the analysis unit 103 and the server 2 respectively.
  • the various types of data obtained above are stored in the analysis unit 103 and/or the server 2, and used for subsequent data analysis by the server 2 and/or the analysis unit 103 through artificial intelligence algorithms to calculate Develop digital biomarkers that can characterize cognitive impairment and other diseases in users.
  • the analysis unit 103 and/or the server 2 after the analysis unit 103 and/or the server 2 completes the data processing, the analysis unit 103 set or integrated in the mobile intelligent platform or the server 2 that stores data from various components performs a third step on the data passing through the server 2.
  • the data after the second-stage data processing is analyzed. The process is as follows: using the intelligent trajectory analysis sensor 102 integrated or set inside the chess and card entity 101 to capture the user's movement of the digital chess and card 1 in a three-dimensional space, such as above and above the chess and card platform.
  • a two-dimensional plane such as the spatial trajectory data of the chess and card platform where it is located, the spatial trajectory data of the user touching the digital chess and card 1 to tip the digital chess and card 1, and the spatial trajectory data of righting the toppled digital chess and card 1, as well as the corresponding time stamps, and inputting the motion data into the deep learning network.
  • To extract feature data related to characterizing the user's cognitive ability convert the user's language data obtained by the user usage analysis unit 103 into feature data through technical means such as Fourier transform and frequency domain analysis, and perform the above-mentioned processing through stack auto-encoding.
  • the data further extracts feature data; possible feature data is extracted from the user's daily data acquired by the intelligent monitoring unit 104 through ontology learning.
  • the server 2 or the analysis unit 103 obtains the user's motion data when using the digital chess and card 1
  • Daily data is analyzed (i.e., the extraction of digital biomarkers).
  • the data analysis relies on artificial intelligence algorithms.
  • the main steps are as follows: The user is captured by the intelligent trajectory analysis sensor 102 integrated or set inside the chess and card entity 101.
  • the motion data formed by the data and corresponding time stamps is input into the deep learning network to extract feature data related to characterizing the user's cognitive ability or motion status; the user's daily data acquired by the intelligent monitoring unit 104 wirelessly connected to the server 2 ( For example, the water immersion, ultrasound, and pressure data obtained when the user goes to the toilet) are extracted from the feature data that can represent the user's attributes through ontology learning.
  • user attributes may include but are not limited to: cognitive status/ability, action status/ability.
  • user attributes may also include, but are not limited to, learning and analysis abilities, expression abilities, memory abilities, and mental endurance.
  • the above-mentioned feature data may not have the characteristics of becoming a digital biomarker due to some reasons. Therefore, the analysis unit 103 and/or the server 2 need to screen the above-mentioned extracted features to generate a data that can accurately represent the user's identity. For digital biomarkers of intellectual ability or health status, after obtaining accurate digital biomarkers, it is still necessary to further rank the importance of the digital biomarkers. The sorting method can optionally rely on marginal contribution analysis to complete.
  • the analysis unit 103 and/or the server 2 of the data collected by the analysis unit 103 integrated inside the mobile smart device rely on the least squares method for the extracted feature data that can be used to roughly characterize the user's cognitive ability status and physical health status. Carry out accurate and effective feature data screening to generate digital biomarkers that can accurately and effectively characterize the user's cognitive ability or health status. After obtaining accurate and effective digital biomarkers, it is still necessary to further rank the importance of the digital biomarkers.
  • the sorting method is optional and relies on marginal contribution analysis.
  • the portable smart device 105 the smart trajectory analysis sensor 102 inside the chess and card entity 101 of the digital chess and card 1, the analysis unit 103 set or integrated inside the mobile smart device, and the smart monitoring unit 104 distributed in the user's daily life space
  • the server 2 that remains connected at all times can obtain in real time the preliminary scores of the user's cognitive ability from each analysis unit 103 set or integrated on the mobile smart device.
  • the preliminary scores of the analysis unit 103 can assist in identifying or mining information that can accurately characterize the human user's cognitive abilities.
  • Digital biomarkers of cognitive ability changes including decline and improvement).
  • This setting can improve the possibility of filtering and extracting from the user's movement data when using the digital chess and card 1, interaction data when interacting with the analysis unit 103, language data, eye movement data, pupil data, and the user's daily data obtained by the intelligent monitoring unit 104. Success rate in accurately characterizing digital biomarkers of cognitive changes (including decline, improvement) or other diseases in human users.
  • All feature data proposed by the user's own motion data provided by the smart device 105 constructs a user causal analysis knowledge base.
  • the establishment of the causal analysis knowledge base helps to conduct causal analysis of digital biomarkers based on the causal analysis theory, thereby effectively judging the The accuracy of digital biomarkers in characterizing changes in human users' cognitive abilities (including decline and improvement), and helping to discover digital biomarkers that can effectively represent changes in users' cognitive abilities (including decline and improvement) and physical health status .
  • the behavioral data of the user's operation of the digital chess and card 1 can also be used as the basis for the evaluation of the user's cognitive ability. This is called a cognitive ability assessment method based on the digital chess and card 1. This method uses the motion data generated by the user's operation of the digital chess and card 1.
  • the interaction data, language data, eye movement data, and pupil data generated when the user interacts with the analysis unit 103 are obtained through the analysis unit 103, and the daily data within the scope of the user's daily activities are obtained through the intelligent monitoring unit 104;
  • the server 2 analyzes the sensor through intelligent trajectory 102 obtains the above-mentioned motion data, obtains interaction data, language data, eye movement data, pupil data through the analysis unit 103 and obtains the user's daily data through the intelligent monitoring unit 104 to extract possible digital biomarkers; based on all acquired feature data Construct a user causal analysis knowledge base.
  • this causal analysis knowledge base helps to conduct causal analysis of digital biomarkers based on the causal analysis theory, thereby effectively judging that the digital biomarkers represent changes in the cognitive ability of human users (including decline and improvement). ), and helps to mine digital biomarkers that can effectively characterize changes in users' cognitive abilities (including decline and improvement) and physical health status.
  • the document unit can retrieve and collect numerous relevant documents with multiple cognitive abilities and classify them through machine learning algorithms to form several document units to build an original document database, so that the data unit can obtain based on the document units.
  • the data provided by the intelligent trajectory analysis sensor 102 in the chess and card entity 101 of the digital chess and card 1, the portable smart device 105, the analysis unit 103 set or integrated in the mobile smart device, and the smart monitoring unit 104 are provided by the server 2 that is always connected to each component. Analyze the main feature parameters extracted from the provided data and construct a data set based on the main feature parameters, thereby reducing the interference of the huge feature parameters formed by massive relevant literature on the causal relationship between cognitive ability and disease and improving the quality of the original literature database. Utilization value and utilization efficiency.
  • the data provided by the intelligent trajectory analysis sensor 102 in the chess and card entity 101 of the digital chess and card 1, the portable smart device 105, the analysis unit 103 set or integrated in the mobile smart device, and the smart monitoring unit 104 are combined with each component at all times.
  • the connected server 2 analyzes the provided data and extracts the main feature parameters and data sets through the causal unit to construct a Bayesian network that can obtain the average causal effect between cognitive abilities through data pattern analysis, thereby enabling the knowledge unit to
  • the knowledge base is constructed based on relevant literature in a manner that forms correspondences between cognitive abilities and average causal effects between cognitive abilities. For example, the average causal effect between cognitive abilities can reflect whether the cognitive abilities constitute complications and comorbidities.
  • the main characteristic parameters may be data provided by the intelligent trajectory analysis sensor 102 in the chess and card entity 101 of the digital chess and card 1, the portable smart device 105, the analysis unit 103 set or integrated in the mobile smart device, and the smart monitoring unit 104.
  • the server 2 which is connected to each component at all times, analyzes the provided data and extracts digital biomarkers that can represent changes in the cognitive ability and physical health status of the human user.
  • the data set used to build the knowledge base may include, but is not limited to, the voice feature data acquired by the server 2 through the analysis unit 103, the behavioral feature data acquired through the intelligent trajectory analysis sensor 102 and the portable smart device 105, the physiological index data and the Risk factor data obtained by the intelligent monitoring unit 104.
  • the behavioral characteristic data may include motion data of the digital chess and card 1 and human body motion data.
  • the behavior characteristic data may also include other user behavior information collected by the portable smart device 105, such as the number of steps the user walks every day, sleep time and other data information.

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Abstract

本发明涉及一种数字生物标志物及基于其的脑健康评判系统，所述数字生物标志物的初始数据至少来自用户使用数字化棋牌(1)时用户的行为数据；所述初始数据能够被发送至服务器(2)和/或分析单元(103)，并且经所述服务器(2)和/或分析单元(103)处理分析以计算得出能够表征所述用户的认知能力的所述数字生物标志物。本发明得到的数字生物标志物可以针对实时采集到的用户使用数字道具时的实时数据进行标记，通过标记可以评估用户的认知能力。

Description

一种数字生物标志物的形成系统、形成方法及基于数字生物标志物的脑健康评判系统 技术领域
本发明涉及数字医疗技术领域，尤其涉及一种数字生物标志物及基于其的脑健康评判系统。
背景技术
认知是大脑接收处理外界信息从而能动地认识世界的过程。认知功能涉及记忆、注意、语言、执行、推理、计算和定向力等多种区域。认知障碍指上述区域中的一项或多项功能受损，它在不同程度影响患者的社会功能和生活质量，严重时甚至导致患者死亡。认知障碍不仅是单纯的医学问题，也是严峻的社会问题。神经系统退行性疾病、心脑血管疾病、营养代谢障碍(特别是糖尿病)、感染、外伤、肿瘤、药物滥用等多种原因均可导致认知障碍。目前针对人类个体的认知能力的评估主要依靠认知障碍筛查量表，通过工作人员对人类个体进行评测，进而实现对人类个体的认知能力评估。当前的数字化评估表(例如savonix数字量表)以及国内对上述量化评估表的本地化版本等通常是将相应的评估软件嵌入至移动设备的APP中，并通过游戏过程中对用户与应用程序的交互进行评估。而发明人经过对现有与认知评估相关的现有技术的认真钻研与评估后发现其仍存在如下技术不足：a.传统量表以及传统认知评估耗时耗力、数据采集难、测试的经济成本较高，并且老人(特别是教育水平较低的老人)对该种测试形式的接受度低或者使用困难度较大；b.传统认知评估依赖于老人自主求医，因而评估方式较为被动；此外，传统量表监测数据的类别少(例如，传统量表评估仅能判断被测试者画时钟是否正确、指针的位置以及钟的轮廓是否正确和数字表达是否正确等上述几项评分而已)；c.传统数据分析仅注重关联关系挖掘。
例如，公开号为CN106327049A的中国专利文献公开了一种认知评估系统，包括信息模块、测试模块和分析模块；信息模块用于根据对象的资料，获得与测试模块相匹配的医疗信息，建立完整的认知评估数据库；测试模块通过测试得到对象的认知测试数据，包含以下五个子模块：注意力和执行功能测试模块、记忆测试模块、数学和计算能力测试模块、语言测试模块、动作与行为的控制和计划测试模块；分析模块根据信息模块获取的医疗信息和测试模块获取的认知测试数据，确定对象的认知评估结果。但是该发明仍然需要工作人员进行测量实现认知评估，对工作人员要求高，难以实现自动化。而且，人类个体的认知能力下降是一个缓慢而难以觉察的过程，通过一两次认知障碍筛查量表评估得到的认知能力评分不能够很好地反应人类个体的认知能力的变化，而如前面提到的，由于认知障碍筛查量表评估过程存在耗时长、对工作人员要求高、受教育水平低的人类个体难以完成大部分评测内容等问题，频繁地通过认知障碍筛查量表评估得到的认知能力变化是很难操作的。因此，有必要对现有技术进行改进。
近年来，智能手环、苹果手表、智能床垫、袖珍心电图和其他健康医疗数字设备如雨后春笋般涌现，有不少已飞入寻常百姓家。上述这些设备除了帮助人们更便捷地了解自己的健康状况外，他们持续收集的健康数据，通过移动互联网汇集为天文数字级的健康医疗数据资源，配以合适的分析手段，可产生新洞察来揭示群体的，尤其是个体的身心健康当前状态和发展趋势。由此而生的数字生物标志物有望成为深入了解人类自身健康和疾病的有效手段。简单来说，数字生物标志物(英文为digital biomarker)是用户/消费者通过数字健康互联设备收集的有关个体生理和行为的客观数据，用以解释、影响和预测健康结局。而传统生物标志物一般指通过生化检验获取的指标，用于标志器官等组织结构或功能发生的改变。如传统医院里的血液测试可以产生富有洞察的数据，但因是通过生化检验得到，而非通过互联的数字健康设备得到，因此不是数字生物标志物。一方面，数字生物标志物现阶段的发展目标是传统生物标志物的有效补充，而不是取代后者；另一方面，数字生物标志物可有力推动健康医疗模式从被动应对向主动预防的转变。通过使用数字生物标志物，研究人员不仅能够更好地解释疾病，而且还可利用日益庞大的健康数据分析正常且健康的个体状态所代表的意义，更重要的是预测未来的健康结局。因此，各方面对数字生物标志物研究的兴趣有望在未来几年内飙升。
此外，一方面由于对本领域技术人员的理解存在差异；另一方面由于申请人做出本发明时研究了大量文献和专利，但篇幅所限并未详细罗列所有的细节与内容，然而这绝非本发明不具备这些现有技术的特征，相反本发明已经具备现有技术的所有特征，而且申请人保留在背景技术中增加相关现有技术之权利。
发明内容
针对现有技术之不足，本发明提供了一种基于数字生物标志物的脑健康评判系统。所述用于人群筛查脑健康的系统至少包括：
数字化棋牌至少能够采集用户使用所述数字化棋牌时所述数字化棋牌的运动数据。优选地，运动数据至少包括数字化棋牌被用户使用/移动过程中的空间坐标数据。优选地，运动数据还可以包括数字化棋牌被用户使用/移动过程中的加速度大小/方向、沿各坐标轴的旋转角速度/角加速度等。
服务器至少能够获取所述运动数据，并基于所述运动数据计算分析得出与参考标志物相对应的数字生物标志物，并基于所述数字生物标志物对所述用户的脑健康进行评估/筛查。
而当本系统被用于某一社区棋牌娱乐场所时，棋牌娱乐场所很可能存在多个使用本数字化棋牌的用户，并且同时使用本数字化棋牌，即避免智能轨迹分析传感器被用户停顿于半空时智能轨迹分析传感器将一次完整的摸牌动作分解为多个子部分来记录而容易丢失半空停顿/滞留时间和完整的摸牌轨迹等可能重要的数字生物标志物。
优选地，第二时刻能够由智能轨迹分析传感器确定。优选地，当智能轨迹分析传感器所测量的智能轨迹分析传感器和/或棋牌实体的振动值从非零的数值变为数值零时，智能轨迹分析传感器判定此时为第二时刻。
特别优选地，当且仅当所述数字化棋牌内的智能轨迹分析传感器处于所述第二时刻时开始搜索所述智能轨迹分析传感器周围的所述可穿戴设备时，所述智能轨迹分析传感器获取距离所述智能轨迹分析传感器最近的所述可穿戴设备的标识符。
通过该配置方式，可以避免在第一时刻或第一时刻与第二时刻之间来测量智能轨迹分析传感器与其周围的可穿戴设备之间的距离而发生一次数字化棋牌的移动/运动过程对应多个用户/可穿戴设备。这是由于用户使用数字化棋牌的过程中，尤其是在使用/移动完某个数字化棋牌的时刻，该数字化棋牌位于靠近使用当次数字化棋牌的用户的一侧，进而能够与其他用户的可穿戴设备保持相对较远的距离，从而避免发生一次智能轨迹分析传感器识别出与多个用户的可穿戴设备的距离为相同的最小距离，即避免发生一次数字化棋牌的移动/运动过程对应多个用户/可穿戴设备的情况，例如在属于同一组的多个佩戴有可穿戴设备的用户同时靠近/移动某个/某几个数字化棋牌时某个/某几个数字化棋牌或出现测量其与周围多个可穿戴设备的最小距离为相同的数值。而本技术方案中智能轨迹分析传感器选择在第二时刻测量该智能轨迹分析传感器与其周围的可穿戴设备之间的距离，即在使用/移动完某个数字化棋牌的时刻，该数字化棋牌已经位于靠近使用当次数字化棋牌的用户的一侧，进而能够与其他用户的可穿戴设备保持相对较远的距离，从而使得使用当次数字化棋牌的用户、非使用当次数字化棋牌的用户与当次数字化棋牌的距离具有显著的差异性，从而提升了数字化棋牌每次所采集的空间坐标数据的归属问题的准确性。
例如，智能轨迹分析传感器开始测量其与周围的可穿戴设备之间的距离，并向距离该数字化棋牌最近的可穿戴设备发送出身份请求。之后距离该数字化棋牌最近的可穿戴设备向该数字化棋牌内的智能轨迹分析传感器发送用于标识佩戴所述可穿戴设备的用户的身份信息的标识符。
当智能轨迹分析传感器接收到与该数字化棋牌距离最近的可穿戴设备的标识符后，智能轨迹分析传感器将标识符插入该智能轨迹分析传感器当次向数字化评估单元和/或服务器的运动数据之中，以标识出当次采集的运动数据来源于哪一个用户。
特别优选地，智能轨迹分析传感器仅能够测量距离在距离阈值范围内的可穿戴设备的距离。优选地，距离阈值可以根据实际场景人为地设定，例如距离阈值可以为十五厘米。通过该配置方式，可以显著地减少智能轨迹分析传感器的测距需求，从而显著地降低智能轨迹分析传感器的数据采集量，进而使得智能轨迹分析传感器能够更快速地识别出距离当次智能轨迹分析传感器最近的可穿戴设备，最终获取所述可穿戴设备相关联的用户的身份信息。
优选地，标识符可以由字符、数字等构成。例如，标识符可以为A1、A2、A3、A4，其中，一组用户使用同一套数字化棋牌，字母A可以代表属于哪一组用户，数字可以标识属于相应用户组的第一个用户。
优选地，当智能轨迹分析传感器通过无线网关完成向数字化评估单元和/或云服务器当次运动数据之后，该智能轨迹分析传感器自动地将所记录的标识符清除，以便于该智能轨迹分析传感器在下一次被用户使用/移动的过程中接收来自所述可穿戴设备用于标识下一次用户的身份信息的标识符。
智能轨迹分析传感器向数字化评估单元和/或服务器发送当次运动数据。
当数字化评估单元和/或服务器接收到所述运动数据，先识别该当次运动数据中的标识符以识别当次运动数据的数据源归属，然后再将当次运动数据录入与该运动数据中的标识符所对应的用户的数据库内，从而解决每个数字化棋牌每次采集的数据归属问题。
优选地，设置于所述数字化棋牌内的智能轨迹分析传感器所采的所述运动数据还能够发送至数字化评估单元和/或服务器，或者通过所述可穿戴设备发送至数字化评估单元和/或服务器。
通过该配置方式，基于数字化棋牌对用户脑健康进行筛查/评估的方式更易为老年人群所接受，从而使得本系统能够在多个社区中投入使用，以作为老年人用户脑健康评估的初筛工具，从而显著地降低对用户脑健康进行筛查/评估的经济成本、以及社会成本，还可以避免医疗资源的浪费；相比仅通过APP进行认知测试评估，通过数字化棋牌实体和数字化评估单元所能获得用户的数据维度更多，例如APP仅能得到用户的二维数据，而通过数字化棋牌内的智能轨迹分析传感器得到的是三维甚至更多维度的数据；通过本发明可以辅助实现脑健康诊断关口的前移，即可以作为一种更简单、客观的阿尔茨海默病的风险评估工具，而不是作为认知障碍的诊断工具。
特别优选地，智能轨迹分析传感器还能够测量其自身和/或棋牌实体的振动值。优选地，智能轨迹分析传感器能够在智能轨迹分析传感器或棋牌实体发生振动/运动时开始记录智能轨迹分析传感器或棋牌实体的空间坐标数据等。优选地，智能轨迹分析传感器能够在智能轨迹分析传感器或棋牌实体停止振动/运动时停止记录智能轨迹分析传感器或棋牌实体的空间坐标数据等。特别优选地，智能轨迹分析传感器能够在智能轨迹分析传感器或棋牌发生振动/运动且所述智能轨迹分析传感器并非处于第一高度时开始记录智能轨迹分析传感器或棋牌实体的空间坐标数据等，其中，所述第一高度为用户使用数字化棋牌的棋牌平台的高度。比如，棋牌平台可以为麻将桌等。通过该配置方式，可以避免数字化棋牌内的智能轨迹分析传感器在数字化棋牌被洗牌等非用户独立使用时采集数字化棋牌运动过程中的数据，而此类数据不能作为用户个人独立使用行为的原始数据，从而进一步地减少无效的原始数据的采集，进而提升原始数据的质量、降低了服务器的数据处理量。优选地，智能轨迹分析传感器所采集的空间坐标数据精度最高可以至少达到五毫米。优选地，智能轨迹分析传感器所采集的空间坐标数据精度的记录时长最大可以为十秒。
根据一个优选实施方式，所述数字化棋牌至少包括棋牌实体和设置于或集成于所述棋牌实体内的智能轨迹分析传感器。所述智能轨迹分析传感器至少能够用于获取所述棋牌实体被所述用户使用的过程中的空间坐标和/或与所述空间坐标相关联的时间戳。
根据一个优选实施方式，所述用户能够穿戴可穿戴设备。所述穿戴设备被配置为至少能够向所述用户发送能够标识所述用户的身份信息的标识符。所述智能轨迹分析传感器能够获取所述可穿戴设备的标识符。优选地，可穿戴设备能够被佩戴于用户的手腕部。优选地，可穿戴设备也能够被佩戴于用户的其他身体部位。
根据一个优选实施方式，所述可穿戴设备还能够采集或被输入所述用户的个体特征数据和/或生理数据，所述生理数据至少包括心率、血压、脉搏血氧饱和度、体温以及相关联的时间戳。个体特征数据至少包括对棋牌的熟悉程度、受教育程度、和疾病史。
其中，所述可穿戴设备能够将个体特征数据和/或生理数据发送至数字化评估单元或所述服务器。优选地，所述生理数据也可以包括与用户活动相关的信息，例如用户每日步行的步数。优选地，所述生理数据还可以包括用户身体部位的抖动频率和幅度。例如，用户身体部位的抖动频率和幅度可以通过可穿戴设备内的加速度计采集获得。
特别优选地，可穿戴设备至少能够采集穿戴可穿戴设备的用户的手部的抖动数据。通过该配置方式，服务器可以将可穿戴设备所采集的数据可以与数字化棋牌的运动数据相结合/融合，以共同评估多个用户的认知能力和/或其他疾病
根据一个优选实施方式，还能够包括数字化评估单元。所述数字化评估单元被配置为能够对用户与所述数字化评估单元之间的交互过程进行监测，以捕获用户的交互数据、语言数据、眼动数据和/或瞳孔数据。所述数字化评估单元能够将上述交互过程的交互数据、语言数据、眼动数据和/或瞳孔数据发送至所述服务器，以使得所述服务器能够基于所述交互过程的交互数据、语言数据、眼动数据和/或瞳孔数据计算得出用户的评估数据。
优选地，交互数据为用户在与数字化评估单元1交互的过程中产生的数据，例如包括但不限于点击数据、频率数据、动作连续数据、轨迹数据、信息内容选择数据、滑动选择数据、内容变化逻辑关系数据等等。
优选地，数字化评估单元可以识别和提取使用的语言数据中的语音信息和图文信息。通过语言数据的提取，能够识别用户的情绪信息、情感信息等等。
图文信息是指用于交流的图文信息、语言文字和/或表达语言的图片或文字，例如普通用户在交流中普遍使用表情包、表情图像来表达语言和情感。通过图文信息也能够直接或者间接提取用户的情绪信息、情感信息等等。
语音信息例如是语音命令、单词和短语中的语音模式。优选地，数字化评估单元在对语音进行信号处理之后，可以提取音素和音素的重复组合。优选地，数字化评估单元可以识别和提取每个语音模式的特征，包括音高、幅度和频谱。
优选地，眼动数据为与用户眼球运动相关的数据。优选地，眼动数据可以包括但不限于：与注视、眼跳和追随等眼球运动等相关的数据。优选地，眼动数据也可以包括：用户的注视点位置、注视时间和瞳孔直径。优选地，上述眼动数据能够由眼电图传感器或其他相关的眼动传感器监测用户的眼部行为获得。瞳孔数据包括但不限于瞳孔的大小数据、瞳孔的变化数据等。
根据一个优选实施方式，所述数字化棋牌能够将所述运动数据发送至所述数字化评估单元和/或所述服务器。所述数字化评估单元能够获取所述运动数据，并能够将所述运动数据发送至所述服务器。
根据一个优选实施方式，还能够包括智能环境设备单元。所述智能环境设备单元能够用于获取所述用户日常生活中的日常数据。
根据一个优选实施方式，所述服务器至少能够基于所述参考标志物调整所述数字化棋牌、可穿戴设备、数字化评估单元和智能环境设备单元所采集的原始数据的种类/类型。
优选地，参考标志物为能够有效表示用户脑健康的目标属性的数字生物标志物。优选地，目标属性至少包括但不限于：执行所有认知过程的能力。优选地，目标属性也可以包括：执行心理过程的能力。优选地，目标属性还可以包括：学习和判断、语言和记忆的能力。例如，参考标志物可以为能够有效表示用户认知能力或状态变化的数字生物标志物。优选地，参考标志物也可以为有效表示用户其他身体健康水平的数字生物标志物。
数字生物标志物来源不局限于单一的数据类别，能够来源于交互数据、语言数据、眼动数据、眼动数据以及日常数据中的一种或几种。
例如，若服务器分析得出认知能力下降的参考标志物为用户移动数字化棋牌时手部的停顿时间、停顿频率，则服务器能够基于该参考标志物调整数字化棋牌采集用户手部的运动数据的种类/类型，即智能轨迹分析传感器仅需测量/采集数字化棋牌在第一时刻与第二时刻之间时自身振动值以及相关联的时间戳以用于计算得出数字生物标志物，而无需测量/采集用户手部移动数字化棋牌的空间坐标数据，或者智能轨迹分析传感器也可以采集数字化棋牌的空间坐标数据但智能轨迹分析传感器可以不将所采集的空间坐标数据发送至数字化评估单元或服务器。
再例如，在用于筛查人群的脑健康时若服务器分析得出认知能力下降的参考标志物还包括用户使用数字化棋牌时的心率时，可穿戴设备仅需重点测量用户在使用数字化棋牌时的心率数据。
再例如，在用于筛查人群的脑健康时若服务器分析得出认知能力下降的参考标志物还包括用户发生遗忘行为的频率时，智能环境设备单元仅需或者至少需要水浸传感器以监测用户忘记关闭水龙头的频率。通过该配置方式，可以通过服务器基于因果分析理论所推断出的有效的数字生物标志物来调整数字化棋牌、数字化评估单元、可穿戴设备、智能环境设备单元等所采集原始数据的种类和/或数量，从而达到降低服务器的数据处理量，以提高服务器对基于各传感器/设备所采集的健康数据进行实时处理的速度，进而可以更快地通过服务器分析和反馈给医生、消费者、研究者和其他相关机构人员相关的结论，最终使得数字化棋牌、数字化评估单元、可穿戴设备、智能环境设备单元等所采集的健康数据能够产生更大的价值，例如在城市或社区进行大规模人群的脑健康筛查，从而及早地发现人群中脑健康出现异常的用户，并将发现的异常病理及时地反馈给相关的医疗人员等。
本发明还提供一种基于数字生物标志物的数据处理方法。所述方法为：
数字化棋牌采集用户使用所述数字化棋牌时所述数字化棋牌的运动数据；
服务器获取所述数字化棋牌所采集的所述运动数据，并基于所述运动数据计算分析得出与参考标志物的数据类型一致的数字生物标志物，并基于所述数字生物标志物对所述用户的脑健康进行评估/筛查。
例如，服务器能够访问服务器之前记录或分析得出的有效的参考标志物，并将当次服务器计算生成的数字生物标志物与参考标志物或先前的有效数字生物标志物之间的差异，并基于根据所述差异对所述用户的脑健康进行评估/筛查。例如，服务器能够基于根据所述差异人群中的认知能力出现下降的用户或者预测用户在未来的指定时间范围内的变化趋势。
根据一个优选实施方式，所述参考标志物由以下步骤得到：获取原始数据；基于机器学习从原始数据中提取特征数据，并进一步从所述特征数据中筛选出候选数字生物标志物；基于因果学习从所述候选数字生物标志物进行因果推断，以计算分析得出所述参考标志物。
优选地，所述原始数据由以下步骤得到：数字化棋牌采集用户使用所述数字化棋牌时的所述数字化棋牌的运动数据；可穿戴设备采集用户的其他生理数据；数字化评估单元获取用户的评估数据、语言数据和眼动数据；智能环境设备单元采集所述用户日常生活中的日常数据；服务器获取所述运动数据、其他生理数据、评估数据、语言数据和眼动数据和日常数据中的一种或多种以作为服务器分析的原始数据。
针对现有技术之不足，本发明提供了一种基于数字生物标志物的认知能力评判系统。所述认知能力评判系统至少包括：
数字化评估单元，被配置为：
能够对用户与所述数字化评估单元之间的交互过程进行监测，以得出交互数据、语言数据、眼动数据和/或瞳孔数据，
能够基于所述交互过程信息以计算得出评估数据；
基于所述交互数据、语言数据、眼动数据和/或瞳孔数据和评估数据对所述用户的脑健康进行评判；
或者，与所述数字化评估单元建立连接的服务器能够获取所述交互过程信息以计算得出评估数据，
其中，所述服务器被配置为至少能够获取所述交互数据、语言数据、眼动数据和/或瞳孔数据，并且基于所述交互数据、语言数据、眼动数据和/或瞳孔数据和评估数据对所述用户的脑健康进行评判。所述认知能力评判系统还能够包括数字化棋牌。所述数字化棋牌被配置为至少能够捕获用户使用所述数字化棋牌时所述数字化棋牌的运动数据。
根据一个优选实施方式，所述数字化评估单元或所述服务器设置有预警模块和显示模块。所述预警模块能够比较当前周期对应的用户的认知能力评分与上一周期对应的用户的认知能力评分，并且所述预警模块能够在用户的认知能力评分环比下降超过预设触发阈值时生成第一预警信息。所述第一预警信息能够通过所述显示模块进行显示。
根据一个优选实施方式，所述数字化棋牌能够将所述运动数据发送至所述数字化评估单元和/或所述服务器。所述数字化评估单元能够获取所述运动数据，并能够将所述运动数据发送至所述服务器。
根据一个优选实施方式，还能够包括智能环境设备单元。所述智能环境设备单元能够用于获取所述用户日常生活中的日常数据。
一种数据处理方法，所述方法为：
数字化评估单元对用户与所述数字化评估单元之间的交互过程进行监测，以得出交互数据、语言数据、眼动数据和/或瞳孔数据；
数字化棋牌采集用户移动所述数字化棋牌时所述数字化棋牌的运动数据；
所述数字化评估单元和/或服务器基于所述交互过程信息以计算得出评估数据，并且至少能够基于所述语言数据、眼动数据、评估数据、运动数据和参考标志物对所述用户的脑健康进行评判。
本发明提供了一种数字生物标志物。所述数字生物标记物的初始数据至少通过采集用户使用数字化棋牌例如麻将、扑克牌、象棋、围棋等的各种操作的行为数据构成；采集到的初始数据会发送至分析单元和/或服务器，该初始数据会经过分析单元和/或服务器上的人工智能程序分析计算得出一系列能够表征用户认知能力例如知觉认知、感觉认知、思维认知等的数字生物标志物，得到的数字生物标志物可以针对实时采集到的用户使用数字化棋牌时的实时数据进行标记，通过标记可以评估用户的认知能力。根据本发明，在采用智能穿戴设备时，多名用户分别穿戴各自的智能穿戴设备。
在根据本发明提取数字生物标志物时，分别针对各个穿戴有智能穿戴设备的用户分析其行为数据，即，根据本发明而采集分析的初始数据包括用户的行为数据，所述行为数据由所述用户使用数字化棋牌的过程中由所述数字化棋牌采集得到。优选地，一套数字化棋牌可以对应多套智能穿戴设备，其中，所述数字化棋牌中的至少一个道具可以具有至少一个传感器，而各个智能穿戴设备可以分别与带有该传感器的该道具预先建立数字链路，用以采集行为数据。
通过该配置方式，基于数字化棋牌对用户健康状态进行筛查/评估的方式更易为普通用户所接受，即一方面在长期的使用本数字化棋牌的过程中不会影响用户的正常生活，另一方面由于本数字化棋牌能够引起普通用户持续的兴趣而能够使得用户能够主动地、长期地使用本数字化棋牌，而本数字化棋牌所采集的用户的行为数据能够作为所述初始数据之一并被发送至服务器后再经所述服务器处理分析以计算得出能够表征所述用户的认知能力的所述数字生物标志物；此外也可以显著地降低对用户健康状态进行筛查/评估的经济成本、以及社会成本以及避免医疗资源的浪费。
特别优选地，当且仅当所述数字化棋牌内的智能轨迹分析传感器处于所述第二时刻时开始搜索所述智能轨迹分析传感器周围的所述便携式智能设备时，所述智能轨迹分析传感器获取距离所述智能轨迹分析传感器最近的所述便携式智能设备的标识符。
通过该配置方式，可以避免发生如下情况：在第一时刻或第一时刻与第二时刻之间来测量智能轨迹分析传感器与其周围的最近距离的便携式智能设备时，用户移动一次数字化棋牌或者一次数字化棋牌运动的过程中，该用户的智能轨迹分析传感器搜索到多个其他用户或者其他用户的便携式智能设备。这是由于用户使用数字化棋牌的过程中，尤其是在使用或移动完某个数字化棋牌的时刻，该数字化棋牌位于靠近使用当次数字化棋牌的用户的一侧，进而能够与其他用户的便携式智能设备保持相对较远的距离，从而避免发生一次智能轨迹分析传感器识别出与多个用户的便携式智能设备的距离为相同的最小距离，即避免发生用户移动一次数字化棋牌或者一次数字化棋牌运动的过程中，该用户的轨迹记录传感器搜索到多个其他用户或者其他用户的便携式智能穿戴设备的情况。而本技术方案中智能轨迹分析传感器选择在第二时刻测量该智能轨迹分析传感器与其周围的便携式智能设备之间的距离，即在使用或移动完某个数字化棋牌的时刻，该数字化棋牌已经位于靠近使用当次数字化棋牌的用户的一侧，进而能够与其他用户的便携式智能设备保持相对较远的距离，从而使得使用当次数字化棋牌的用户、非使用当次数字化棋牌的用户与当次数字化棋牌的距离具有显著的差异性，从而提升了数字化棋牌每次所采集的空间坐标数据的归属问题的准确性。
智能轨迹分析传感器向分析单元和/或服务器发送当次运动数据。
当分析单元和/或服务器接收到所述运动数据，先识别该当次运动数据中的标识符以识别当次运动数据的数据源归属，然后再将当次运动数据录入与该运动数据中的标识符所对应的用户的数据库内，从而解决每个数字化棋牌每次采集的数据归属问题。
优选地，设置于所述数字化棋牌内的智能轨迹分析传感器所采的所述运动数据还能够通过所述便携式智能设备发送至分析单元和/或服务器。
根据一个优选实施方式，所述初始数据还能够包括以下一种或多种：交互数据、语言数据、眼动数据、瞳孔数据和日常数据。所述交互数据、语言数据、眼动数据、瞳孔数据由分析单元在所述用户使用所述分析单元的交互过程中采集得到，所述日常数据由智能监测单元在所述用户日常生活行为中采集得到。
根据一个优选实施方式，所述数字化棋牌可以包括：棋牌实体和智能轨迹分析传感器。所述智能轨迹分析传感器至少能够获取所述棋牌实体被所述用户使用过程中的空间坐标数据以及所述空间坐标数据相对应的时间戳。
根据一个优选实施方式，所述用户能够佩戴便携式智能设备。所述便携式智能设备被配置为至少能够向所述分析单元和/或所述服务器发送能够标识所述用户的身份信息的识别码。所述分析单元和/或所述服务器能够获取所述便携式智能设备所携带的能够标识所述用户身份信息的识别码。
根据一个优选实施方式，所述便携式智能设备还能够采集或被输入所述用户的生理数据。所述生理数据可以包括心率、血压、脉搏血氧饱和度、体温中的一个或者多个。所述便携式智能设备能够将所述个体特征数据和/或生理数据发送至所述分析单元和/或所述服务器。优选地，上述生理数据也能够作为初始数据。所述个体特征数据可以包括对所述棋牌实体的熟悉程度、受教育程度和疾病史
根据一个优选实施方式，设置于所述数字化棋牌内的所述智能轨迹分析传感器所采集的所述行为数据还能够通过所述便携式智能设备发送至所述分析单元和/或所述服务器。
根据一个优选实施方式，所述分析单元和/或处理分析以计算得出能够表征所述用户的认知能力的所述数字生物标志物的方法为：
从所述初始数据提取特征数据并进一步从所述特征数据中筛选出候选数字生物标志物；
对所述候选数字生物标志物进行特征因果推断，以计算分析得出所述数字生物标志物。
根据一个优选实施方式，对所述候选数字生物标志物进行特征因果推断，以推断出有效的数字生物标志物的方法为：
基于所述特征数据构建因果分析知识库，并基于因果分析理论通过所建立的因果分析知识库对所述候选数字生物标志物进行因果推断，以计算分析得出有效的数字生物标志物。
本发明还提供一种数字生物标志物，所述数字生物标志物的形成方法为：获取初始数据，并对所述初始数据进行数据融合以得到融合数据；对所述融合数据提取特征数据并进一步从所述特征数据中计算分析得出候选数字生物标志物；对所述候选数字生物标志物进行因果推断，以分析推断出有效的所述数字生物标志物。
优选地，所述初始数据包括用户的行为数据，所述行为数据在所述用户使用数字化棋牌的过程中由所述数字化棋牌采集得到，和/或所述初始数据包括用户的语言数据，所述语言数据是在所述用户使用数字化棋牌的过程中由分析单元采集得到。
优选地，在所述数字化棋牌包括棋牌实体和轨迹记录传感器的情况下，所述轨迹记录传感器设置于或集成于所述棋牌实体内，所述轨迹记录传感器至少能够用于获取所述棋牌实体在被所述用户使用的过程中的空间坐标和/或与所述空间坐标相关联的时间戳；在所述数字化棋牌集成于移动设备的情况下，所述分析单元能够用于获取所述棋牌实体在被所述用户使用的过程中的交互数据、语言数据、眼动数据和/或瞳孔数据。
优选地，所述用户能够穿戴与所述数字化棋牌相配合使用的便携式智能穿戴设备，所述便携式智能穿戴设备被配置为至少能够向所述轨迹记录传感器发送能够标识所述使用所述数字化棋牌的用户的身份信息的识别码，其中，所述轨迹记录传感器能够获取所述便携式智能穿戴设备的识别码。
优选地，所述初始数据能够被发送至服务器和/或分析单元。
优选地，所述分析单元能够判断所述用户通过所述分析单元所输入的信息是否在规定数值范围区间内。
附图说明
图1是本发明提供的一种优选实施方式的脑健康评判系统放入简化模块连接关系示意图；
图2是本发明提供的数字化棋牌的一种优选实施方式的示意图；
图3是本发明提供的数字化棋牌和数字化评估单元的另一种优选实施方式的示意图；
图4是本发明提供的一种优选实施方式的认知能力评判系统的简化模块连接关系示意图；
图5是本发明提供的数字化棋牌和服务器的一种优选实施方式的简化模块连接关系示意图；
图6是本发明数字化棋牌的其中一种模型展示图。
附图标记列表
1：数字化棋牌；2：服务器；3：可穿戴设备；4：数字化评估单元；5：智能环境设备单元；101：棋牌实体；
102：智能轨迹分析传感器；103：分析单元；104：智能监测单元；105：便携式智能设备。
具体实施方式
下面结合附图进行详细说明。
实施例1
图1、图2、图3和图4示出一种基于数字生物标志物的脑健康评判系统及方法，也可以称为一种基于数字生物标志物的认知能力评判系统及方法。
该系统至少包括数字化棋牌1和服务器2。优选地，数字化棋牌1被配置为至少能够采集用户使用数字化棋牌1时数字化棋牌1的运动数据。优选地，服务器2被配置为至少能够获取运动数据，并基于运动数据计算分析得出与参考标志物相对应的数字生物标志物，并基于数字生物标志物对用户的脑健康进行评估/筛查。
优选地，服务器2可以为普通的物理服务器2。优选地，服务器2也可以为云服务器2。
优选地，脑健康至少包括：认知能力。优选地，脑健康也可以包括：学习、判断、语言和记忆的能力，以及执行心理过程的能力。优选地，脑健康还可以包括其他疾病。
根据一个优选实施方式，数字化棋牌1至少包括：棋牌实体101，以及设置于或集成于棋牌实体101内的智能轨迹分析传感器102，智能轨迹分析传感器102至少能够用于获取棋牌实体101被用户使用的过程中的空间坐标和/或与空间坐标相关联的时间戳。
当数字化棋牌1集成于移动设备时，评估单元用于采集棋牌实体101被用户使用的过程中的交互数据、语言数据、眼动数据和/或瞳孔数据。
优选地，上述棋牌实体101被用户使用的过程至少包括：移动棋牌、转动棋牌。优选地，上述棋牌被用户使用的过程还可以包括：按压棋牌的作用力大小和方向、频率，使用棋牌敲击的作用力大小和频率等。
特别优选地，棋牌实体101可以是麻将。优选地，棋牌实体101也可以是象棋、国际象棋等其他类别的棋牌。
优选地，智能轨迹分析传感器102至少能够用于获取数字化棋牌1的三维空间坐标数据。优选地，NFC读写器至少能够用于为智能轨迹分析传感器102配置参数。优选地，无线网关用于与智能轨迹分析传感器102配套使用。优选地，无线充电器至少能够用于为智能轨迹分析传感器102充电。
优选地，智能轨迹分析传感器102被设置于或集成与棋牌内。优选地，智能轨迹分析传感器102被配置为能够在需要时与无线网关等其他设备数据连接时再打开数据通道进行数据传输。优选地，将智能轨迹分析传感器102安装入麻将的开槽内后可以采用强力胶水进行对开槽进行粘合。
优选地，智能轨迹分析传感器102所传输的数据内容至少包括：数字化棋牌1(例如数字化麻将)的三维空间坐标数据，即数字化棋牌1分别对应于x、y、z轴的坐标。优选地，数字化棋牌1所在的坐标系可以根据实际应用场景灵活地选定。
优选地，NFC读写器可以支持NFC协议。优选地，NFC读写器可以用于为智能轨迹分析传感器102配置参数使用。优选地，NFC读写器可以连接至PC使用。优选地，NFC读写器也可以用于对所获取智能轨迹分析传感器102的数据进行读写。
优选地，无线充电器可以采用Qi标准无线充电协议。优选地，无线充电器可以用于为智能轨迹分析传感器102充电。
优选地，无线网关可以采用无线网络等。优选地，无线网关可以保持数据接收通道一直打开，任意时刻可以接收或传输智能轨迹分析传感器102所发送的数据。优选地，一套数字化棋牌1配备一套无线网关。优选地，一套数字化棋牌1中的任意一个数字化棋牌1仅通过与该套数字化棋牌1相对应的无线网关发送数据。
优选地，智能轨迹分析传感器102可以为三轴加速度传感器。
特别优选地，智能轨迹分析传感器102与相对应的无线网关不用保持长连接，可以在需要发送数据时智能轨迹分析传感器102再与无线网关建立数据连接以打开数据传输通道。
优选地，智能轨迹分析传感器102可以用于记录用户抓取集成或者设置有智能轨迹分析传感器102的数字化棋牌1而在半空中和/或在特定的平台内所形成的空间运行轨迹数据。优选地，空间运行轨迹数据可以包括用户抓取麻将过程中所产生的停顿的时长、一次动作移动的距离、从用户手部从一个位置移动到另一个位置的移动轨迹等时序信号。优选地，一次动作为全程无停顿的动作。优选地，一次动作为全程中包含停顿的动作。
优选地，智能轨迹分析传感器102的工作过程也可以分为以下步骤：
当智能轨迹分析传感器102被启动(即第一时刻)之后，智能轨迹分析传感器102先后通过STM32进行时钟配置和串口配置；
通过2.4G模块设置透传模式；
2.4G模块发送数据唤醒传感器：若收到传感器应答信号，则智能轨迹分析传感器102解休眠，若未收到传感器应答信号，则2.4G模块重新发送数据唤醒传感器；
智能轨迹分析传感器102初始化，即时间重置以读取时间、x/y/z轴数据分别归零，以便于后续过程中读取x/y/z轴新的坐标数据；
在用户使用设置有或集成有智能轨迹分析传感器102的数字化棋牌1时，智能轨迹分析传感器102计算该数字化棋牌1的输出加速度、角速度以及空间坐标；
智能轨迹分析传感器102进行数据融合，即获取x/y/z轴的坐标数据，以用于数字化棋牌1的姿态判断和设置姿态标志位，以及获取可穿戴设备3或其他设备发送的用于表示当前使用该数字化棋牌1的用户身份的标识码。
智能轨迹分析传感器102将上述经数据融合的数据(例如该数字化棋牌1的出加速度、角速度以及空间坐标，以及用于标识用户身份的标识码)打包，其中，打包后的数据可以设置校验位并通过2.4G模块发送至网关，网关再转发数据至云平台(即云服务器2)；智能轨迹分析传感器102也可以将上述经数据融合和姿态判断后的数据经串口发送至上位机(上位机可以是个人服务器2)，并经由上位机对所获取智能轨迹分析传感器102的所有数据进行复现，以绘制当前被使用的数字化棋牌1的空间轨迹图像。特别优选地，上述空间轨迹图像也可以被发送至服务器2，以作为一种可能的数字生物标志物的原始数据来源。
通过该配置方式，可以通过数字化棋牌1相比仅从移动平台的APP获取更多维度的能够表示用户决策过程和/或行为习惯的数据，即通过智能轨迹分析传感器102采集用户在使用数字化棋牌1(例如数字化麻将)过程中，在拿取数字化棋牌1时所产生的用户手部在半空和/或在特定的平台(例如麻将桌的桌面)内中所划出的三维动作轨迹数据、用户手部抓取麻将等棋牌的力度、颤抖的频率和强度、动作的流畅性和连贯性、停顿时间、移动轨迹等时序信号，并可以通过蓝牙或紫蜂技术、无线网关等现有技术以将通过集成于麻将中的智能轨迹分析传感器102所获得的用户的运动数据传输至服务器2。用户实际抓取麻将等实体棋牌过程中与仅仅通过触摸带有游戏APP的触摸屏是显著不同的，这是由于通过带有游戏APP的触摸屏一般仅能获取用户的二维动作运动数据，而用户在实际抓取麻将等实体棋牌过程中会产生三维空间运动数据，甚至更多维度的运动数据，进而数据类别更丰富；此外，采用数字化棋牌1的方式获取用户的运动数据也更易于用户所接受。
优选地，智能轨迹分析传感器102可以通过无线网络等现有技术将所获取的用户的动作数据等发送至移动平台上的数字化评估单元4和/或服务器2。
根据一个优选实施方式，用户能够穿戴可穿戴设备3。穿戴设备被配置为至少能够向用户发送能够标识当次使用数字化棋牌1的用户的身份信息的标识符。智能轨迹分析传感器102能够获取可穿戴设备3的标识符。优选地，当且仅当数字化棋牌1内的智能轨迹分析传感器102处于第二时刻时开始搜索(智能轨迹分析传感器102周围的)可穿戴设备3时，智能轨迹分析传感器102才能获取距离智能轨迹分析传感器102最近的可穿戴设备3的标识符。
优选地，可穿戴设备3可以包括但不限于：腕带智能设备、头戴智能设备等。例如可穿戴设备3可以为智能手表/手环、智能头盔。优选地，可穿戴设备3可以由用户全天二十四小时或特定的时间段随身携带。
优选地，可穿戴设备3能够被佩戴于用户的手腕部。优选地，可穿戴设备3也能够被佩戴于用户的其他身体部位。
根据一个优选实施方式，可穿戴设备3还能够采集用户的生理数据。生理数据至少包括心率、血压、脉搏血氧饱和度、体温以及相关联的时间戳。可穿戴设备3能够将生理数据发送至服务器2。
优选地，生理数据也可以包括与用户活动相关的信息，例如用户每日步行的步数。
优选地，生理数据还可以包括用户身体部位(例如手部)的抖动频率和幅度。例如，用户身体部位(例如手部)的抖动频率和幅度可以通过可穿戴设备3内的加速度计采集获得。
根据一个优选实施方式，还能够包括智能环境设备单元5，智能环境设备单元5能够用于获取用户日常生活中的日常数据。优选地，日常数据为智能环境设备单元5采集用户全天候或者特定时段的行为数据。优选地，上述行为数据可以为用户认知能力评估相关的行为。优选地，上述行为数据可以为用户其他身体健康评估相关的行为。优选地，智能环境设备单元5可以包括但不限于以下设备：水浸传感器、超声波传感器、语音记录器和微波传感器等。例如，日常数据可以为厕所里的水浸传感器(如监测忘记关闭水龙头的频率)所采集的行为数据，以表示用户的记忆力情况；设置于用户日常行为活动范围内的超声波传感器可实现对用户距离的判断、微波传感器可实现对用户移动的判定，从而获取用户的距离及移动信息，以用于标志用户的定向力、视空间能力；设置于用户日常行为活动范围内的语音记录器所记录的用户语音信息可以用于根据其语频、语速、语音信息判定用户的注意力，还可以根据重句和关键字等信息评估用户的言语瞬时或延迟记忆力。优选地，日常数据也可以包括危险因素和生理指标。优选地，危险因素可以包括但不限于：吸烟的频率、饮酒的频率等。优选地，生理指标可以包括但不限于：心脏的健康状态、脑部的健康状况等。优选地，日常数据还可以包括认知能力评估所需的其他运动数据。
根据一个优选实施方式，还能够包括数字化评估单元4。数字化评估单元4被配置为能够对用户与数字化评估单元4之间的交互过程进行监测，以捕获用户的交互数据、语言数据、眼动数据和/或瞳孔数据。数字化评估单元4能够将交互数据、语言数据、眼动数据和/或瞳孔数据发送至服务器2，以使得服务器2能够基于交互过程计算得出用户的评估数据。
优选地，数字化评估单元4可以识别和提取使用的语音命令、单词和短语中的语音模式。优选地，数字化评估单元4在对语音进行信号处理之后，可以提取音素和音素的重复组合。优选地，数字化评估单元4可以识别和提取每个语音模式的特征，包括音高、幅度和频谱。
优选地，眼动数据为与用户眼球运动相关的数据。优选地，眼动数据可以包括但不限于：与注视、眼跳和追随等眼球运动(或者眼球运动模式)等相关的数据。优选地，眼动数据也可以包括：用户的注视点位置、注视时间和瞳孔直径。优选地，上述眼动数据能够由眼电图传感器或其他相关的眼动传感器监测用户的眼部行为获得。
优选地，数字化棋牌1与设置有或集成有数字化评估单元4的移动设备的联动方式至少有以下两种：1)设置有或集成有数字化评估单元4的移动设备与智能轨迹分析传感器102等分别通过蓝牙或无线传送到网关，再由网关通过有线或无线传送到服务器2；2)智能轨迹分析传感器102所采集的数据通过蓝牙或无线传送到数字化评估单元4，数字化评估单元4通过蓝牙或无线传送到网关，网关通过有线或无线传送到服务器2。
优选地，交互数据、语言数据和眼动数据是由数字化评估单元4获取的。获取方式至少包括数据接收和人工输入。优选地，数字化评估单元4可以设置于或集成于移动平台内。优选地，移动平台包括但不限于手机或平板电脑等。优选地，数字化评估单元4可以根据实际需求而设置或集成相应的传感器以获取用户的交互数据、语言数据、眼动数据和/或瞳孔数据。
优选地，用户还能够通过数字化评估单元4与数字化棋牌1进行联动。例如，数字化评估单元4可以向用户发出评估指令；比如，评估指令可以是“请说出数字化评估单元4所使用屏幕中空缺的麻将，并从数字化棋牌1中取出该空缺的麻将”，之后数字化评估单元4采集用户回答过程中的交互数据和语言数据；与此同时，被用户移动的数字化棋牌1采集其自身的运动数据。
优选地，语言数据不限于用户的语音信息，还包括用户交互产生的语言文字信息。语言文字信息为自然语言文字信息，至少包括多种国家的语言文字、数字符号、具有语言含义的图像符号等等。
例如，数字化评估单元4可以将其获取的用户的语言数据直接通过蓝牙技术等无线传输至服务器2。
再例如，数字化评估单元4能够获取数字化棋牌1所采集用户的运动数据，并基于上述运动数据以及数字化评估单元4本身所采集的语言数据共同对用户的认知能力进行评估。例如，数字化评估单元4可以根据用户语音信息的清晰度、智能轨迹分析传感器102所记录的棋牌实体移动轨迹的流畅程度和完成一个评估单元的时间的长度等对用户的认知能力进行评估。
再例如，数字化评估单元4可以针对用户输入数字化评估单元4的信息是否符合信息键入标准以对用户的认知能力进行评估。键入标准包括但不限于语音清晰度、轨迹记忆是否明确、测试时间的长度、键入信息是否超过规定范围(如应当输入1至9范围内数字，但是实际输入超出这一范围)等。通过该配置方式，即将数字化评估单元4设置于或集成于移动设备(例如手机、平板电脑等)中的游戏APP，不仅仅可以获取用户与游戏APP交互问答的语言数据或语音信息，还有能够通过与数字化评估单元4相联动或者数据连接的数字化棋牌1获取用户的交互数据。交互数据至少包括动作信息、轨迹信息以及与动作信息、轨迹信息相关的参数信息。与动作信息、轨迹信息相关的参数信息例如是两个动作的间隔时间、动作连续程度、点击频率等等。例如，让用户通过数字化评估单元4画一个时钟，部分用户起初开始画时可能就出现困难，此方式相比传统量表评估画时钟，通过数字化评估单元4的屏幕以及电子触控笔抓取下列特征：用户使用电子触控笔在数字化评估单元4的触摸屏上画图时使用电子触控笔接触触摸屏的力度、画图时电子触控笔鼻尖在触摸屏上划过的速度和/或停顿时间、用户使用电子触控笔所形成的笔迹的形状等类别数据，并将上述数据保存至自身或者发送至服务器2，以通过自身服务器2对用户认知能力的数字化初步评估。
优选地，对用户认知能力的数字化初步评估所形成的评估数据包括但不限于：用户使用电子触控笔在数字化评估单元4的触摸屏上画图时使用电子触控笔接触触摸屏的力度参数、画图时电子触控笔鼻尖在触摸屏上划过的速度和/或停顿时间、用户使用电子触控笔所形成的笔迹的形状、评估过程中用户与数字化评估单元4交互时语音的清晰度、数字化棋牌1被移动所形成的轨迹是否流畅、完成一次评估所需的时间等。通过该配置方式，不仅仅能够通过数字化评估单元4或者服务器2比较用户使用数字化评估单元4时所形成的手势轨迹、使用电子触控笔的力度等数据信息以与其他正常认知能力人群进行横向对比，而且更重要的是数字化评估单元4或者服务器2能够基于该老人的不同时期其认知能力的变化趋势进行监测和评估，从而可以避免将原本认知能力较低的被测试者评估为认知能力不足。
优选地，数字化评估单元4着重评估用户如下方面的认知能力：注意力、视空间能力、抽象能力、执行能力、即时记忆、延迟记忆、语言能力、定向能力。例如，数字化评估单元4可以将一块麻将牌图片切割成几个碎片，然后将顺序打乱，再通过让老年人判定这些碎片化图片是由哪个麻将牌切割而成的，从而从若干麻将中选取对应的麻将牌，以获取与老年人的视空间能力对应的评估数据。
优选地，能够将数字化评估单元4数字化或者整合于移动平台的游戏APP或者其他评估装置中。通过该配置方式，可以通过用户与数字化评估单元4或移动平台的游戏APP或者其他评估装置的交互以获取用户的交互数据和/或语言数据等数据，从而对用户的认知能力进行评分或者评估。
优选地，数字化评估单元4的主要评估过程：数据采集、语音识别、逻辑校验、规则学习、评分。
数字化评估单元4和服务器2均可能具有对数据的处理功能。当数字化评估单元4和服务器2的数据处理功能有区别时，可分为两个阶段。
其中，数字化评估单元4的功能的第一阶段包括以下步骤：
(1)针对用户对数字化评估单元4所提出的问题的输入/反馈，数字化评估单元4能够判断其是否在规定数值范围区间内(如其出生年月与身份证是否对应、各个认知评估能力得分是否在规定数值范围内、输入数据是否清晰、超时等)；若信息的输入均在规定数值范围内，且不存在矛盾信息，则完成逻辑校验；
(2)将信息的输入、及对应认知域认知能力得分传输至服务器2，获取各数字化评估单元4得分总和，将总分与设定的得分区间对应认知评估水平进行比对，实现对受试者认知的评估。
第二阶段包括以下步骤：
(1)基于多模态的数据融合技术，服务器2对语音采集设备获取的语言数据、智能环境采集设备获取的传感器数据认知能力得分数据进行数据融合；
(2)针对融合数据进行基于机器学习的特征提取方法(例如偏最小二乘法、自编码器算法及其衍生算法、对抗网络学习算法及其衍生算法等)提取可能用于表征用户认知状态的特征；
(3)针对提取的特征与认知域进行关联，以获取能够表征用户认知状态的相关数字生物标志物，根据各数字生物标志物在特征提取算法中的权重进行重要性排序，实现认知筛查，助力认知障碍早发现。
通过该配置方式，即将数字化评估单元4集成于移动平台的游戏APP上，不仅仅可以获得问答的信息，还能够获取用户使用该游戏APP时的动作信息、使用触控笔在电子屏幕所形成的轨迹信息等。例如，数字化评估单元4里通过让用户在游戏APP(如平板电脑)上画一个时钟，移动平台上的相应传感器能够捕捉用户画时钟时的动作信息、轨迹信息等，比如部分用户慢慢画时可能就出现困难，此方式相比传统量表评估画时钟，通过移动平台上的相应传感器至少(但不限于)捕获用户下列特征数据：使用触控笔的力度、画时钟的速度、停顿时长、数字表达的准确性、触控笔在触摸屏上形成的轨迹、画每一笔的停留时间以及手部的颤动等，进而通过上述特征数据实现对用户认知能力的数字化评估。通过该配置方式，即设置数字化评估单元4，数字化评估可以获取用户的特征数据的维度更多元，所采集的数据类别更全面，以实现对用户认知能力更全面、更客观的评估。
特别优选地，移动平台内的数字化评估单元4与用户之间可以建立语音等交互信息，以获取用户(特别是抓取植入有智能轨迹分析传感器102的麻将时)的语言数据等。例如，通过移动平台的数字化评估单元4向用户提问：“请说出当前页面中所空缺的麻将牌”，然后数字化评估单元4针对用于回答的语音信息进行语义转化，以获取用户答案；紧接着由数字化评估单元4判断该答案是否在规定区间，如在规定区域则判断其得分(是否正确、失分项目判定由数字化评估单元4预先设定)，并传至服务器2；针对用于输入的信息则直接判断后传至服务器2(类似语音信息语义转化后操作)；针对轨迹信息，则需要将信息传至服务器2，基于机器学习的判定算法来判断得分情况。
根据一个优选实施方式，数字化评估单元4还安装有或设置有预警模块和显示模块，预警模块能够比较当前周期对应的用户的认知能力评分与上一周期对应的用户的认知能力评分，并且预警模块能够在用户的认知能力评分环比下降超过预设触发阈值时生成第一预警信息，第一预警信息能够通过显示模块进行显示。
优选地，评估单元定期比较当前周期对应的用户的认知能力评分与上一周期对应的用户的认知能力评分的比较周期可以是人为设定的。优选地，预设触发阈值可以人为设定。例如，预设触发阈值例如可以是上一周期该用户认知评估得分的3％。比如，评估单元的预警模块可以被配置为以天、周、月、季度和年中的至少一个为周期。比如，评估单元的预警模块可以被配置为以天为周期来比较对应的用户的认知能力评分。又比如，评估单元的预警模块可以被配置为以天和周为周期来比较对应的用户的认知能力评分。即，评估单元的预警模块既比较相邻两天中对应的用户的认知能力评分，也比较相邻两周中对应的用户的认知能力评分。
优选地，评估单元的预警模块可以以彼此不同的至少两个比较周期定期比较当前周期对应的用户的认知能力评分与上一周期对应的用户的认知能力评分。不同的比较周期可以对应于彼此不同的预设触发阈值。例如，一些用户在使用数字化棋牌1的前期出牌比较快，但是当其出牌的速度随着被测试时间的推移逐渐变慢(例如，连续几个月、一两年均呈变慢趋势)，评估单元的预警模块则可能判断出该用户出现认知能力下降的问题。通过该配置方式，可以基于该用户的不同时期其认知能力的变化趋势进行监测和评估，即用户认知能力的纵向变化数据。例如甲用户今天的数据和该用户下个星期在做测试的数据的对比，以及再下个星期，有可能从中监测出评估能力的变化趋势，从而可以避免将原本认知能力较低的用户评估为认知能力不足。
根据一个优选实施方式，数字化评估单元4或服务器2能够分别获取用户使用数字化棋牌1时的运动数据、用户使用数字化评估单元4所获取的用户的语言数据、智能环境设备单元5所获取的用户的日常数据，以用户后续的数据分析而获取可能表示用户认知能力或状态变化的数字生物标志物。
根据一个优选实施方式，数字化评估单元4或服务器2进行数据分析的过程为：将用户使用数字化棋牌1时的运动数据输入深度学习网络以提取特征数据；将用户使用数字化评估单元4所获取的用户的语言数据通过傅里叶变换以及频域分析等技术手段转换为特征数据，并通过堆栈自编码对上述数据进一步地提取特征数据；将智能环境设备单元5所获取的用户的日常数据通过本体学习的方式从中提取可能的特征数据。
当服务器2或者云平台获取用户使用数字化棋牌1时的动作运动数据、使用数字化评估单元4时所记录的用户的交互数据、语言数据、眼动数据以及智能环境设备单元5所获取的用户的日常数据进行数据分析(即数字生物标志物的提取)的过程为：将用户使用数字化棋牌1时的动作运动数据(即时序信号)输入深度学习网络，以通过深度学习网络提取用户的特征数据或指标。将用户使用数字化评估单元4所获取的用户的语言数据(即时序信号)等数据通过傅里叶变换以及频域分析等技术手段转换为特征数据，然后通过堆栈自编码对上述数据进一步地提取特征数据。将智能环境设备单元5所获取的用户的日常数据(例如用户如厕时获取的水浸、超声、压力数据)通过本体学习的方式从中提取能够表征目标属性的特征数据。优选地，目标属性至少包括但不限于：执行所有认知过程的能力。优选地，目标属性也可以包括：执行心理过程的能力。优选地，目标属性还可以包括：学习和判断、语言和记忆的能力。
根据一个优选实施方式，数字化评估单元4或服务器2对所有上述特征数据通过特征选择方法进行特征选择，以筛选出可能的数字生物标志物，然后再根据边际贡献分析对上述可能的数字生物标志物进行重要性排序。优选地，特征选择方法可以至少包括偏最小二乘法、变分自编码器、对抗网络学习中的一种或多种。优选地，特征选择方法也可以采用其他类别的方法。当服务器2初步提取上述特征数据之后，服务器2对以上所有所提取的特征可以通过偏最小二乘法进行特征选择：Y＝X*(XTS(TTXXTS)-1TTY)+Re   (1)，其中S为自变量X映射的向量，T为因变量Y映射的向量，Re为相应的残基构成的矩阵。服务器2对以上所有所提取的特征数据通过偏最小二乘法进行特征选择以筛选出可能的数字生物标志物，然后再根据边际贡献分析对上述可能的数字生物标志物进行重要性排序。
优选地，参考标志物可以根据具体的医疗筛查/评估需求而选定。例如，服务器2所分析出有效的数字生物标志物(重要性由高至低)：用户手部移动轨迹、用户手部停顿的频率和/或时长。服务器2可以将上述有效的数字生物标志物作为筛查脑健康的参考标志物。
与此同时，服务器2能够实时地获取移动平台上的数字化评估单元4对用户认知能力的评分，以用于辅助识别或者挖掘能够用于标识人类用户认知水平下降(或者变化)的数字生物标志物。通过该配置方式，可以从用户使用数字化棋牌1时的动作运动数据、使用数字化评估单元4时所记录的用户的语言数据以及智能环境设备单元5所获取的用户的日常数据中筛选提取可能用于标识人类用户认知水平下降或者变化的数字生物标志物。
根据一个优选实施方式，基于所获取的特征数据构建因果分析知识库，并基于因果分析理论通过所建立的因果分析知识库对数字生物标志物进行因果推断，以挖掘能够有效标识用户认知能力或状态变化的数字生物标志物。
基于数字化棋牌1的认知能力评估方法，方法的步骤为：通过数字化棋牌1至少获取用户的运动数据；通过数字化评估单元4至少获取用户的语言数据；通过智能环境设备单元5获取用户的日常数据；服务器2获取上述运动数据、语言数据和日常数据以提取可能的数字生物标志物；基于上述可能的数字生物标志物建立因果关系知识库以计算分析得出参考标志物。
优选地，参考标志物可以根据具体的医疗筛查/评估需求而选定。例如，参考标志物可以为能够有效标识用户认知能力或状态变化的数字生物标志物。
优选地，主要特征参数可以包括不限于服务器2前述步骤中所获取的可能的数字生物标志物。
优选地，数据集可以包括但不限于服务器2所获取的语音特征数据、行为特征数据、危险因素数据、生理指标数据等。优选地，行为特征数据可以包括数字化棋牌1的运动数据。优选地，行为特征数据也可以包括可穿戴设备3所采集的用户其他行为信息，例如用户每天所行走的步数、睡眠时间等数据信息。
优选地，因果单元也可以通过数据模式分析认知能力之间的直接因果效应，从而使得知识单元能够基于相关文献以形成认知能力之间及认知能力之间的直接因果效应的对应关系的方式构建知识库。
优选地，因果单元所采用的主要特征参数可以包括但不限于：用户使用数字化棋牌1时的运动数据、使用数字化评估单元4时所记录的用户的语言数据以及智能环境设备单元5所获取的用户的日常数据。
优选地，文献单元基于获取的众多含有多种认知能力的相关文献。文献单元对相关文献进行分类形成若干文献单元体以构建原始文献库。该相关文献包括就诊病历、研究报告、会议文献、期刊文献、书籍、学术论文和专利。在如此大量的文献的情况下，其需要按照一定的方法进行分类。进行文献分类是为了能够有效的观察认知能力之间的关联以及减小系统的负荷。例如可以按照消化道疾病、心血管疾病和神经科疾病等进行分类。也可以按照学术领域进行分类，例如康复学和心理学等等进行分类。不过，在大量文献的严峻形式下，其准确高效的分类会直接影响并发症和合并症的区别。优选地，文献分类可以采用贝叶斯法、SVM方法和k-NN法。
为了能够在不影响认知能力对之间的因果关系的情况下，因果单元通过独立性检验简化无向图约束。例如，独立性检验可以采用卡方独立性检验。通过该配置方式，可以从用户使用数字化棋牌1时的运动数据、使用数字化评估单元4时所记录的用户的语言数据以及智能环境设备单元5所获取的用户的日常数据等数据来源中筛选提取用于标识人类用户认知水平变化或者其他疾病的有效/重要的数字生物标志物。
例如，通过因果关系知识库可能推断出：抑郁症与习惯性睡眠效率低均是认知能力下降的有效/重要的数字生物标志物，但抑郁症与认知能力下降的因果关系强于习惯性睡眠效率低与认知能力下降的因果关系。
又例如，学习活动强度提高与体育锻炼频率提高是认知能力提高的有效/重要的数字生物标志物。
再例如，通过因果关系知识库可能推断出：用户使用数字化棋牌1时手部抖动的频率在一定时期或短时期内的加剧是该用户认知能力出现下降的一个有效数字生物标志物。
再例如，用户一定时期或短时期内使用数字化棋牌1的过程中每次的决策时间呈显著增长的趋势。
再例如，用户手部抖动的频率可与运动减少、肌强直、震颤和姿势调节障碍等症状产生直接因果关系，而这些症状会导致震颤麻痹(paralysis angitas)即帕金森，对患者的行动力、注意力、定向力、视空间能力均有可能产生直接因果效应的影响。
再例如，若数字化评估单元4或可穿戴设备3采集到用户短时间/近期内说话出现多处停顿、经常发出“嗯哎啊”等辅音、明显的表达困难等的次数超过正常参考值时，则可能是用户语言神经系统下降的迹象，可以作为认知能力下降的有效数字生物标志物。正常参考值可以由服务器2或其他方式获取正常认知能力或正常人的平均值/中位数。
通过该配置方式，服务器2还可以基于相关变量特征产生因果关系网络模型进行因果推理，以识别出与用户认知能力发生变化有关的相关特征变量与认知能力变化之间的因果关系以及该因果关系的强度，从而可以为医务人员或者医学研究人员提供一种认识引起人类个体认知能力变化的有效数字生物标志物或者病理起因的有效途径。
通过该配置方式，服务器2还可以基于所挖掘的有效数字生物标志物更新数字化评估单元4或者服务器2进一步准确地筛查/评估脑健康的方式。与此同时，所挖掘的有效数字生物标志物也可以作为后续数字化评估单元4或者服务器2作为脑健康(比如阿尔茨海默病)风险评估平台的依据。
本发明还提供一种基于数字生物标志物的数据处理方法。方法为：
数字化棋牌1采集用户使用数字化棋牌1时数字化棋牌1的运动数据；
服务器2获取数字化棋牌1所采集的运动数据，并基于运动数据计算分析得出与参考标志物相对应的数字生物标志物，并基于数字生物标志物对用户的脑健康进行评估/筛查。
优选地，服务器2能够访问服务器2之前记录或分析得出的有效的参考标志物，并将当次服务器2计算生成的数字生物标志物与参考标志物或先前的有效数字生物标志物之间的差异，并基于根据差异对用户的脑健康进行评估/筛查。例如，服务器2能够基于根据差异人群中的认知能力出现下降的用户或者预测用户在未来的指定时间范围内的变化趋势。
例如，参考标志物为服务器2对在先的原始数据分析得出的用户移动数字化棋牌1时手部的停顿时间和/或停顿频率。
优选地，服务器2能够定期或不定期地比较当前周期对应的用户的数字生物标志物的发生频率和/或发生的强度与上一周期对应的用户的数字生物标志物的发生频率和/或发生的强度。周期可以是人为设定的，例如每周。
例如，服务器2识别出某一时间段/周期内数字生物标志物的发生频率(用户使用数字化棋牌1时手部抖动的强度和/或频率，或者使用数字化棋牌1的过程中每次的停顿时间/决策时间)超过预设阈值时，服务器2判定此时的用户的认知能力下降的可能性较大。其中，预设阈值能够由服务器2通过因果关系知识库推断得出。
再例如，服务器2识别出某一时间段/周期内某数字生物标志物(用户短时间/近期内说话出现多处停顿、经常发出“嗯哎啊”等辅音、明显的表达困难等)的发生频率超过预设阈值时，服务器2判定此时的用户的认知能力下降的可能性较大。
根据一个优选实施方式，参考标志物由以下步骤得到：
获取原始数据；
基于机器学习从原始数据中提取特征数据，并进一步从特征数据中筛选出候选数字生物标志物；
基于因果学习从候选数字生物标志物进行因果推断，以计算分析得出参考标志物。
优选地，原始数据由以下步骤得到：
数字化棋牌1采集用户使用数字化棋牌1时的数字化棋牌1的运动数据；
可穿戴设备3采集用户的日常生理数据；
数字化评估单元4获取用户的评估数据、语言数据和眼动数据；
智能环境设备单元5采集用户日常生活中的日常数据；
服务器2获取运动数据、其他生理数据、评估数据、语言数据和眼动数据和日常数据中的一种或多种以作为原始数据。
实施例2
本实施例是对实施例1的进一步改进，重复的内容不再赘述。
本实施例提供一种数字生物标志物。
数字生物标记的初始数据至少通过采集用户使用数字化棋牌1例如麻将、扑克牌、象棋、围棋等的各种操作的行为数据作为初始数据构成；采集到的初始数据会发送至分析单元103和/或服务器2，该初始数据会经过分析单元103和/或服务器2上的人工智能程序分析计算得出一系列能够表征用户认知能力例如知觉认知、感觉认知、思维认知等的数字生物标志物，得到的数字生物标志物可以针对实时采集到的用户使用数字化棋牌1时的实时数据进行标记，通过标记可以评估用户的认知能力。
优选地，至少用户移动数字化棋牌1的运动数据和用户接触数字化棋牌1的触碰数据可作为用户对数字化棋牌1的行为数据。优选地，用户使用数字化棋牌1的运动数据可以包括用户对数字化棋牌1进行的移动数字化棋牌1、转动数字化棋牌1的操作。优选地，用户使用数字化棋牌1的运动数据至少还可以包括用户抬起数字化棋牌1、落下数字化棋牌1的操作、用户碰触数字化棋牌1使得数字化棋牌1倾倒以及用户使倾倒的数字化棋牌1扶正的操作，例如图5所示的数字化棋牌1。优选地，用户接触数字化棋牌1的触碰数据可以包括用户施加于数字化棋牌1的作用力的大小、方向以及频率，包括用户使用数字化棋牌1产生的作用力的大小、方向以及频率。优选地，经过分析得到的数字生物标志物可以表征用户属性，用户属性可以包括但不限于：认知状态/能力、行动状态/能力。优选地，用户属性还可以包括但不限于学习和分析能力、表达能力、记忆能力以及心理承受能力。
优选地，普通的物理服务器和/或云服务器都可以作为服务器2，服务器2可以对采集到的包括但不限于行为数据的初始数据进行分析并提取数字生物标志物，并通过数字生物标志物对用户的健康状态进行评估。优选地，健康状态可以包括：认知能力，例如观察力、注意力、想象力。优选地，健康状态也可以包括：学习能力、分析能力、表达能力和记忆能力以及心理承受能力。优选地，健康状态还可以包括是否患有其他疾病。
根据一种优选实施方式，如图5和图6所示，数字化棋牌1可以是各种棋牌类的实体，例如麻将、国际象棋、象棋、围棋、扑克牌等。数字化棋牌1可以包括棋牌实体101和智能轨迹分析传感器102。数字化棋牌1的数量由各种棋牌的规则确定，数字化棋牌1的棋牌实体101内部设置智能轨迹分析传感器102，或智能轨迹分析传感器102集成于棋牌实体101内，使数字化棋牌1的棋牌实体101与普通的棋牌外观上无明显差别。智能轨迹分析传感器102至少能够用于获取棋牌实体101被用户使用的过程中的空间坐标以及与空间坐标相关联的时间戳，并将上述信息发送到分析单元103和服务器2上。
优选地，用户使用棋牌实体101的过程可以包括：移动棋牌实体101、转动棋牌实体101、抬起棋牌实体101、落下棋牌实体101、棋牌实体101发生倾倒以及用户扶正棋牌实体101。优选地，用户使用棋牌实体101的过程还可以包括：触摸棋牌实体101时产生的对棋牌实体101的作用力的大小、方向以及频率，使用棋牌实体101对其他物体例如棋牌平台产生的作用力的大小、方向以及频率。特别优选地，棋牌实体101可以是国际象棋，尤其是具有立体空间体积的国际象棋棋子。优选地，棋牌实体101也可以是麻将、围棋、象棋、扑克牌等其他类别的棋牌。优选地，集成或者设置于棋牌实体101内部的智能轨迹分析传感器102可以能够用于获取数字化棋牌1的三维空间坐标数据并且能够将获取的三维空间数据传输至服务器2和/或分析单元103。优选地，集成或者设置于棋牌实体101内部的智能轨迹分析传感器102可以抓取用户移动数字化棋牌1在三维空间例如所在棋牌平台的上空和/或二维平面例如所在棋牌平台的空间轨迹数据、用户触碰数字化棋牌1使数字化棋牌1倾倒以及将倾倒的数字化棋牌1扶正的空间轨迹数据。优选地，用户抓取棋牌实体101的间隔时长、用户抓取棋牌实体101过程中的停顿时长、用户使棋牌实体101在相应时长下的移动的距离、用户使棋牌实体101相对于正常放置下的棋牌实体101发生的角度变化以及棋牌实体101相对于用户手掌抓取时的初始位置与终止位置等可以作为智能轨迹分析传感器102需要捕捉的空间运行轨迹数据。
根据一种优选实施方式，用户可以穿戴与数字化棋牌1相匹配的配置有可以向智能轨迹分析传感器102发送标志用户身份信息的标识符的便携式智能设备105。优选地，数字化棋牌1内的智能轨迹分析传感器102可与便携式智能设备105进行无线连接例如蓝牙、局域网等方式，智能轨迹分析传感器102通过该连接方式获取配置于便携式智能设备105上的身份信息标识符。
优选地，可由用户自主选择全天二十四小时或特定时间段穿戴的便携式智能设备105。便携式智能穿戴设备105可以包括：智能手表、智能手环、智能戒指、智能项链、智能头盔等。优选地，便携式智能设备105能够佩戴与用户的手腕部，例如智能手表、智能手环。优选地，便携式智能设备105也能够佩戴于用户的其他身体部位，例如智能戒指佩戴于用户手指处、智能项链佩戴于用户颈部以及头盔可佩戴于用户头部。
优选地，用户能够穿戴与数字化棋牌1相匹配的配置有可以向智能轨迹分析传感器102发送标志用户身份信息的标识符的便携式智能设备105。当且仅当数字化棋牌1内的智能轨迹分析传感器102处于智能轨迹分析传感器102和/或棋牌实体101处于相对于便携式智能设备105静止的第二时刻时，智能轨迹分析传感器102可通过覆盖式搜索周围的便携式智能设备105，并与智能轨迹分析传感器102直线距离最近的便携式智能设备105建立连接并获取配置于便携式智能设备105上的身份信息标识符。
根据一种优选实施方式，便携式智能设备105能够自主收集归纳用户的生理数据和/或用户的个体特征数据，也能够人为输入并由便携式智能设备105归纳分析用户的生理数据和/或用户的个体特征数据。用户的生理数据可以包括心率、血压、血氧饱和度、体温以及和各项特征对应的时间戳，生理数据被便携式智能设备105采集分析之后发送给服务器2，服务器2依据便携式智能设备105上传的生理数据判断用户当前的生理状态。用户的个体特征数据可以包括对用户正在使用的棋牌实体101的熟悉程度、用户自主思考的能力(智商)以及可能对认知能力存在影响的疾病历史信息。
优选地，便携式智能设备105的内部配置有加速度测量设备，加速度测量设备可以使得便携式智能设备105获取用户活动相关的信息，例如每日的运动状态。优选地，运动状态可以包括用户每日步行步数、用户跑动距离、用户跳跃频次以及相应的时间戳。优选地，运动状态还可以包括用户身体部位发生的运动，例如用户身体部位的抖动频率、抖动幅度以及相应的时间戳。优选地，用户身体部位可以包括手部、腿部、腰部、肩部和颈部。优选地，通过便携式智能设备105内配置的加速度测量装置获得的用户活动相关信息可以由便携式智能设备105自主采集并纳入生理数据。
根据一种优选实施方式，还包括能够获取用户日常生活中的日常数据并将日常数据发送到与智能监测单元104保持连接的服务器2上的智能监测单元104。
优选地，智能监测单元104可由用户自主选择采集全天二十四小时或者特定时段的用户的行为信息作为日常数据。优选地，日常数据可由智能监测单元104发送至服务器2，服务器2依据智能监测单元104上传的用户日常数据判断用户实时状态。优选地，实时状态可以包括当前用户认知能力状态，例如观察力、注意力、想象力。优选地，实时状态还可以包括用户当前的身体健康状态。优选地，智能监测单元104可以包括但不限于以下设备：水浸传感器、超声波传感器、微波传感器、语音记录器等。
例如，日常数据可以包括设置在用户厕所、厨房和/或其他接通水源的区域里的水浸传感器所采集到的用户的行为信息，例如忘记关闭水龙头的频次和打开水龙头至关闭水龙头的时长等，此类日常数据可以作为评估用户记忆力状态的依据；可通过超声波传感器对用户位置坐标进行判定，通过微波传感器对用户运动进行判定，由此通过设置在用户日常活动范围内的超声波传感器和微波传感器获取可以判定用户观察力、注意力和行动力的用户移动的方向、速度和距离等信息；可通过设置于用户日常行为活动范围内的语音记录器所记录的用户语音信息并根据其语频、语速、语音信息判定用户的注意力，还可以根据重句和关键字等信息评估用户的言语瞬时或延迟记忆力。优选地，日常数据也可以包括危险因素和生理指标。优选地，危险因素可以包括但不限于：吸烟的频率、饮酒的频率等。优选地，生理指标可以包括但不限于：心脏的健康状态、脑部的健康状况等。优选地，日常数据还可以包括认知能力评估所需的其他运动数据。根据一种优选实施方式，还具有设置或集成于移动智能平台例如智能手机、平板电脑内部的分析单元103。分析单元103可以对用户与分析单元103之间的交互过程进行记录分析，并整理归纳用户与分析单元103的交互数据、用户的语言数据、用户的眼动数据以及用户的瞳孔数据，并通过分析单元103将上述数据发送至服务器2，服务器2对用户与分析单元103的交互过程进行分析评估，并存储备份分析得到的评估数据。
优选地，交互数据是用户在操纵数字化棋牌1的棋牌实体101时或者与分析单元103进行交互的过程中产生的例如点击数据、频率数据、动作连续数据、轨迹数据、信息内容选择数据、滑动选择数据、内容变化逻辑关系数据等数据。优选地，通过移动智能设备内设置或者集成的分析单元103可以识别和分析并提取用于分析判断用户的情绪状态、情感信息等信息的语言数据。优选地，语言数据可以包括用户平时通过移动智能设备与他人或与智能设备交互产生的语音信息和图文信息。
优选地，移动智能设备内的分析单元103收集的用户产生的图文信息可以包括用于交流的图文信息、语言文字和/或表达语言的图片或文字等信息，例如现在普遍使用的表情包、颜文字等可以更生动形象地表达使用者情绪的图文信息。通过设置在或集成于移动智能设备内部，分析单元103可以连接网络以此来识别分析图文信息，从而直接或者间接获取用户的实时的情绪状态和情感信息等，从而便于判断用户的实时认知能力处于何种状态。
优选地，语音信息是用户与设置或集成于移动智能设备内的分析单元103交互时的语音，用户通常会通过移动智能设备与他人交流或者直接与移动智能设备交流，交流产生的语音信息包括语音命令、单词语音、短语语音等。优选地，分析单元103能够对语音进行底层处理并通过提取语音里不同的音素与音素的重复组合，从而识别和提取每条语音的特征，包括音量高低、音调转化和音频高低等，分析单元103可将采集分析得到的此类数据发送至服务器2。
优选地，眼动数据能够由设置在分析单元103内的眼电图传感器或者其他相关的眼动传感器获取。分析单元103内的眼电图传感器或者其他相关的眼动传感器可以通过移动智能设备的摄像头捕获到用户的眼珠运动状态。优选地，眼动数据可以包括但不限于：用户眼球注视、眼跳和追随时眼球的运动相关数据。优选地，眼动数据还可以包括：用户眼球注视时注视点的坐标、在此注视点停留的时长、在此注视点瞳孔数据等。分析单元103通过眼电图传感器或其他相关的眼动传感器获取的眼动数据可通过分析单元103发送至服务器2。
优选地，分析单元103内的眼电图传感器或其他相关的眼动传感器通过移动智能设备的摄像头捕获的用户的瞳孔数据包括但不限于用户在操作数字化棋牌1过程中触摸棋牌实体101、听见或看见其他人打出的棋牌实体101时的瞳孔发生的一系列变化，例如瞳孔的大小变化、变化速度和聚焦频次等。
优选地，设置或集成于移动智能设备内部的分析单元103可通过移动智能设备自主采集与用户的交互数据、语言数据、眼动数据和瞳孔数据，分析单元103也可通过人为地从移动智能设备的输入端输入并由分析单元103整理归纳。优选地，分析单元103可以设置于或集成于例如手机或平板电脑等移动智能设备内。优选地，设置于或集成于移动智能设备内部的分析单元103可由用户实际需求在移动智能设备上自主分析单元103内部的眼电图传感器或其他相关眼动传感器的运作状态以获取自身需要的相应的交互数据、语言数据和/或眼动数据。
例如，设置于或集成于移动智能设备内部的分析单元103可以通过获取用于与承载有分析单元103的移动智能设备交互时产生的输入信息，并将该输入信息与分析单元103和/或服务器2制定的信息键入标准进行对比，从而对用户的认知能力进行评估。分析单元103和/或服务器2制定的信息键入标准可以包括：语义清晰度、轨迹记忆是否明确、测试时间的长度、键入信息是否超过规定范围(如应当输入1至9范围内数字，但是实际输入超出这一范围)等。再例如，通过将分析单元103设置于或集成于移动智能设备(例如手机、平板电脑)中的休闲APP内，并通过移动智能设备可以获取用户休闲时与相关APP产生的交互数据、语言数据等。设置于或集成于移动智能设备内部的分析单元103还能够通过与数字化棋牌1内的智能轨迹分析传感器102捕获用户与棋牌实体101交互时发生的动作信息、轨迹信息。例如，让用户在设置于或集成于移动智能设备内部的内部设置或集成了分析单元103的某个休闲APP上画一个时钟或者画一个当前棋牌实体101的外形等，此方式相比传统量表评估画时钟可以获得更多的评判信息，通过内部设置或集成了分析单元103的移动智能设备的屏幕以及与移动智能设备交互的电子触控笔上设置与分析单元103连接的传感器抓取用户操作数据。用户在该APP上的操作数据可以包括但不限于：用户使用电子触控笔在分析单元103的触摸屏上画图时使用电子触控笔接触触摸屏的力度、画图时电子触控笔鼻尖在触摸屏上划过的速度和/或停顿时间、用户使用电子触控笔所形成的笔迹的形状、用户施加在电子触控笔笔身的压力以及用户握住电子触控笔笔身时的湿度等数据，并将上述操作数据通过移动智能设备保存至分析单元103自身或者发送至服务器2，以通过分析单元103自身或者服务器2对用户认知能力的数字化和健康状态初步评估。
优选地，分析单元103和/或服务器2对用户认知能力的数字化和健康状态的初步评估所形成的评估数据包括但不限于：用户使用电子触控笔在分析单元103的触摸屏上画图时使用电子触控笔接触触摸屏的力度、画图时电子触控笔鼻尖在触摸屏上划过的速度和/或停顿时间、用户使用电子触控笔所形成的笔迹的形状、用户施加在电子触控笔笔身的压力、用户握住电子触控笔笔身时的湿度、评估过程中用户与分析单元103交互时语音的清晰度、数字化棋牌1被移动所形成的轨迹是否流畅、完成一次评估所需的时间等。分析单元103或者服务器2通过将获取的评估数据与大数据收集到的正常状态下人在相同操作时产生的正常数据作对比，以此初步判断用户认知能力的数字化和健康状态。
通过该配置方式，不仅仅能够通过分析单元103或者服务器2比较用户使用内部设置或集成了分析单元103的移动智能设备时在移动智能设备屏幕上所形成的手势轨迹、使用与移动智能设备对应的电子触控笔的力度等数据信息将这类信息与通过设置或集成于移动智能设备内部的分析单元103或服务器2收集到的其他正常认知能力人群做出相同操作是的数据信息进行横向对比，并且可以通过用户对于施加在触控笔笔身的压力和握住触控笔时手心是否出汗等信息初步判断身体健康状态，而且更重要的是分析单元103或者服务器2能够基于用户的不同时期其认知能力的变化趋势和身体状态的变化趋势进行监测和评估，从而可以避免将原本认知能力较低的被测试者评估为认知能力不足和简单预测用户的身体状态。例如，在移动智能设备上的一款设置或集成有分析单元103的休闲APP上，可以将一块棋牌实体101例如麻将牌的图片随机切割成几个碎片，然后将顺序打乱，再通过让用户判定这些碎片化图片是由哪个麻将牌切割而成的，从而从若干麻将中选取对应的麻将牌，以获取与用户的视空间能力对应和思维能力的评估数据，再例如让用户将简单的几个随机切割的碎片完整的拼接完全，从而分析用户的记忆力和视空间能力。通过该配置方式，可以通过用户与内部设置或集成了分析单元103的移动智能设备或移动智能设备上的休闲APP或者其他评估装置的交互以获取用户的操作、语音等数据，从而对用户的认知能力例如视空间能力、思维能力或者记忆力等进行评分或者评估。
分析单元103和服务器2均可能具有数据处理功能，可将分析单元对于数据处理功能设置为第一数据处理阶段，将服务器2的数据处理功能设置为第二数据处理阶段。
设置在移动智能设备内部的分析单元103的第一数据处理阶段一般分为以下步骤：针对用户提出的问题或者用户的反馈可提交至移动智能设备内部的分析单元103，分析单元103会对用户通过移动智能设备的输入端提交的信息进行初步判断，初步判断可以包括其出生年月与身份证是否对应、各个认知评估能力得分是否在规定数值范围内、输入数据是否清晰、超时等的数值范围区间判断；完成初步判断之后，分析单元103会进行逻辑校验，判断用户提交的信息是否矛盾；通过逻辑校验之后，分析单元103将用户提交的信息和对用户对应认知域认知能力得分发送至服务器2，服务器2对各个分析单元103的得分进行总和，将总分与设定的得分区间对应认知评估水平进行比对，实现对受试者认知的评估。
与便携式智能设备105、数字化棋牌1的棋牌实体101内部的智能轨迹分析传感器2、设置或集成于移动智能设备内部的分析单元103以及分布在用户日常生活空间内的智能监测单元104时刻保持连接的服务器2的第二数据处理阶段可以包括一下步骤：基于多模态的数据融合技术，服务器2对于数字化棋牌1的棋牌实体101内部的智能轨迹分析传感器102捕捉的用户的运动数据、分析单元103发送的交互数据、语言数据、眼动数据、瞳孔数据、智能监测单元104的传感器采集到的传感器数据认知能力得分数据、以及便携式智能设备105捕捉到的用户身体的运动数据进行数据融合；服务器2完成数据融合之后，针对融合数据进行基于机器学习的特征提取方法(例如偏最小二乘法、自编码器算法及其衍生算法、对抗网络学习算法及其衍生算法等)提取可能用于表征用户认知状态的数字生物标志物；完成数字生物标志物的提取之后，针对提取的数字生物标志物与认知域进行关联，以获取能够表征用户认知状态的相关数字生物标志物，根据各数字生物标志物在特征提取算法中的权重进行重要性排序，实现认知筛查全面可靠，助力认知障碍早发现。通过该配置方式，即将分析单元103设置或集成于移动智能设备上的休闲APP上，不仅可以通过移动智能设备和用户进行语音交流获取用户与分析单元103的问答信息，还能够通过移动智能设备的屏幕捕捉用户与移动智能设备或移动智能设备上的休闲APP互动时的动作信息、使用与移动智能设备对应的电子触控笔的力度等数据信息等。例如，让用户在设置于或集成于移动智能设备内部的内部设置或集成了分析单元103的某个休闲APP上画一个时钟或者画一个当前棋牌实体101的外形等，此方式相比传统量表评估画时钟可以获得更多的评判信息，通过内部设置或集成了分析单元103的移动智能设备的屏幕以及与移动智能设备交互的电子触控笔上设置与分析单元103连接的传感器抓取用户操作数据。用户在该APP上的操作数据可以包括但不限于：用户使用电子触控笔在内置分析单元103的移动智能设备的触摸屏上画图时使用电子触控笔接触触摸屏的力度、画图时电子触控笔鼻尖在触摸屏上划过的速度和/或停顿时间、用户使用电子触控笔所形成的笔迹的形状、用户施加在电子触控笔笔身的压力以及用户握住电子触控笔笔身时的湿度等数据，并将上述操作数据通过移动智能设备保存至分析单元103自身或者发送至服务器2，上述操作数据的获取更加全面及多维化，提取到的特征数据也更加全面与多维化。分析单元103自身或者服务器2通过更加全面和多维化的特征数据对用户认知能力的数字化和健康状态的评估也能够更全面、更客观。
特别优选地，设置集成于移动智能设备内部的分析单元103与用户之间可以建立语音、图像以及物理接触例如触摸等交互手段，以获取用户抓取数字化棋牌1时(特别是用户抓取内部设置有智能轨迹分析传感器102的棋牌实体101时)的语言数据、图像数据等。例如，可通过设置或集成于移动智能设备内部的分析单元103向用户提出与目前数字化棋牌1的棋牌实体101规则相关的问题，以国际象棋为例，设置或集成于移动智能设备内部的分析单元103可以通过移动智能设备向用户发出语音提问或者显示文字信息提问：“请阐述该国际象棋棋子下一步存在几种走法”，若用户作出语音回答，设置或集成于移动智能设备内的分析单元103通过移动智能设备的听筒装置收录语音，并将语音进行语义转化，转化为分析单元103可识别的信息；若用户通过移动智能设备键入答案，设置或集成于移动智能设备内部的分析单元103通过获取移动智能设备输入端的答案，并将其转化为自身可识别的信息。针对用户给出的答案信息，移动智能设备内部的分析单元103对答案信息进行判断并根据判断结果为用户此次答题进行评分，并将结果存放在本地和上传至服务器2，针对键入的答案信息同样进行判断评分，存放本地并上传至服务器2，服务器2可通过机器学习算法判断得分情况。
根据一种优选实施方式，用户使用数字化棋牌1时的运动数据、用户与分析单元103交互时产生的交互数据、语言数据、眼动数据、瞳孔数据、智能监测单元104所获取的用户的日常数据能够分别被分析单元103和服务器2获取，上述获取的各类数据被存放至分析单元103和/或服务器2，并用于后续服务器2和/或分析单元103通过人工智能算法进行数据分析从而计算得出可以表征用户认知能力障碍和其他疾病的数字生物标志物。
根据一种优选实施方式，在分析单元103和/或服务器2完成数据处理后，设置或集成于移动智能平台内的分析单元103或存储有来自各个组件的数据的服务器2对经过服务器2进行第二阶段数据处理后的数据进行分析，其过程为：将用户借助于集成或者设置于棋牌实体101内部的智能轨迹分析传感器102抓取的用户移动数字化棋牌1在三维空间例如所在棋牌平台的上空和/或二维平面例如所在棋牌平台的空间轨迹数据、用户触碰数字化棋牌1使数字化棋牌1倾倒、将倾倒的数字化棋牌1扶正的空间轨迹数据以及相应的时间戳形成的运动数据输入深度学习网络以提取与表征用户认知能力有关的特征数据；将用户使用分析单元103所获取的用户的语言数据通过傅里叶变换以及频域分析等技术手段转换为特征数据，并通过堆栈自编码对上述数据进一步地提取特征数据；将智能监测单元104所获取的用户的日常数据通过本体学习的方式从中提取可能的特征数据。当服务器2或者分析单元103获取用户使用数字化棋牌1时的运动数据、使用分析单元103时所记录的用户的交互数据、语言数据、眼动数据、瞳孔数据以及智能监测单元104所获取的用户的日常数据进行数据分析(即数字生物标志物的提取)，数据分析依靠人工智能算法完成，主要步骤如下：将用户借助于集成或者设置于棋牌实体101内部的智能轨迹分析传感器102抓取的通过用户移动数字化棋牌1在三维空间例如所在棋牌平台的上空和/或二维平面例如所在棋牌平台的空间轨迹数据、用户触碰数字化棋牌1使数字化棋牌1倾倒、将倾倒的数字化棋牌1扶正的空间轨迹数据以及相应的时间戳形成的运动数据输入深度学习网络以提取与表征用户认知能力或运动状态有关的特征数据；将与服务器2进行无线连接的智能监测单元104所获取的用户的日常数据(例如用户如厕时获取的水浸、超声、压力数据)通过本体学习的方式从中提取能够表征用户属性的特征数据。优选地，用户属性可以包括但不限于：认知状态/能力、行动状态/能力。优选地，用户属性还可以包括但不限于学习和分析能力、表达能力、记忆能力以及心理承受能力。
根据一种优选实时方式，上述特征数据可能由于一些原因不具备成为数字生物标志物的特性，因此需要分析单元103和/或服务器2对上述提取到的特征进行筛选，从而生成能够准确表征用户认知能力或者健康状况的数字生物标志物，获得准确的数字生物标志物之后，仍需进一步对数字生物标志物进行重要性排序，其排序方式可选依靠边际贡献分析来完成。接收来自于数字化棋牌1的棋牌实体101内部的智能轨迹分析传感器102、分布在用户的活动空间内的智能监测单元104、捕捉用户身体运动状态的便携式智能设备105传来的各类数据以及设置或集成于移动智能设备内部的分析单元103自身采集到的数据的分析单元103和/或服务器2对上述提取到的可以用于大致表征用户认知能力状态和身体健康状态的特征数据依靠最小二乘法进行准确有效的特征数据筛选，从而生成能够准确有效地表征用户认知能力或者健康状况的数字生物标志物，获得准确有效的数字生物标志物之后，仍需进一步对数字生物标志物进行重要性排序，其排序方式可选依靠边际贡献分析来完成。与此同时，与便携式智能设备105、数字化棋牌1的棋牌实体101内部的智能轨迹分析传感器102、设置或集成于移动智能设备内部的分析单元103以及分布在用户日常生活空间内的智能监测单元104时刻保持连接的服务器2能够实时的获取设置于或者集成于移动智能设备上的各个分析单元103对于用户认知能力的初步评分，分析单元103的初步评分可辅助识别或者挖掘能够准确表征人类用户认知能力变化(包括下降、提升)的数字生物标志物。该设置可提升从用户使用数字化棋牌1的运动数据、与分析单元103交互时的交互数据、语言数据、眼动数据、瞳孔数据以及智能监测单元104所获取的用户的日常数据中筛选提取出可能准确表征人类用户认知能力变化(包括下降、提升)或其他疾病的数字生物标志物的成功率。
根据一种优选实施方式，基于用户操作数字化棋牌1的棋牌实体101时棋牌实体101内部的智能轨迹分析传感器102提供的用户操作运动数据、设置或集成于移动智能设备内部的分析单元103提供的用户与移动智能设备交互产生的交互数据、语言数据、眼动数据、瞳孔数据、与服务器2实时连接且分布在用户活动空间内的智能监测单元104提供的日常数据、穿戴在用户身体部位上的便携式智能设备105提供的用户自身的运动数据提出出来的所有特征数据构建用户因果分析知识库，该因果分析知识库的建立有助于基于因果分析理论对数字生物标记物进行因果分析，从而有效判断该数字生物标志物表征人类用户认知能力变化(包括下降、提升)的准确度，并且有助于挖掘能有有效表征用户认知能力变化(包括下降、提升)和身体健康状态的数字生物标志物。
用户对数字化棋牌1进行操作的行为数据也可作为用户认知能力的评估依据，将其称为基于数字化棋牌1的认知能力评估方法，该方法通过用户对数字化棋牌1的操作产生的运动数据，通过分析单元103获取用户与分析单元103交互时产生的交互数据、语言数据、眼动数据、瞳孔数据，通过智能监测单元104获取用户日常活动范围内的日常数据；服务器2通过智能轨迹分析传感器102获取上述运动数据、通过分析单元103获取交互数据、语言数据、眼动数据、瞳孔数据和通过智能监测单元104获取用户的日常数据以提取可能的数字生物标志物；基于所获取的所有特征数据构建用户因果分析知识库，该因果分析知识库的建立有助于基于因果分析理论对数字生物标记物进行因果分析，从而有效判断该数字生物标志物表征人类用户认知能力变化(包括下降、提升)的准确度，并且有助于挖掘能有有效表征用户认知能力变化(包括下降、提升)和身体健康状态的数字生物标志物。
优选地，文献单元能够检索并收集众多含有多种认知能力的相关文献并通过机器学习算法对其进行分类形成若干文献单元体以构建原始文献库，以使得数据单元能够基于文献单元体获取由数字化棋牌1的棋牌实体101内的智能轨迹分析传感器102、便携式智能设备105、设置或集成于移动智能设备内的分析单元103、智能监测单元104提供的数据并由与各组件时刻连接的服务器2对提供的数据进行分析提取出的主要特征参数并基于主要特征参数构建数据集，从而降低海量相关文献形成的庞大的特征参数对于认知能力与病症之间因果关系的干扰并提高原始文献库的利用价值和利用效率。
优选地，由数字化棋牌1的棋牌实体101内的智能轨迹分析传感器102、便携式智能设备105、设置或集成于移动智能设备内的分析单元103、智能监测单元104提供的数据并由与各组件时刻连接的服务器2对提供的数据进行分析提取出的主要特征参数和数据集通过因果单元构建能够通过数据模式分析得出认知能力之间的平均因果效应的贝叶斯网络，从而使得知识单元能够基于相关文献以形成认知能力之间及认知能力之间的平均因果效应的对应关系的方式构建知识库。例如，认知能力之间的平均因果效应能够反映出认知能力之间是否是构成并发症和合并症。
优选地，主要特征参数可以是由数字化棋牌1的棋牌实体101内的智能轨迹分析传感器102、便携式智能设备105、设置或集成于移动智能设备内的分析单元103、智能监测单元104提供的数据并由与各组件时刻连接的服务器2对提供的数据进行分析提取出的能够表征人类用户认知能力变化和身体健康状态的数字生物标志物。优选地，用于构建知识库的数据集可以包括但不限于服务器2通过分析单元103获取的语音特征数据、通过智能轨迹分析传感器102和便携式智能设备105获取的行为特征数据、生理指标数据和通过智能监测单元104获取的危险因素数据。优选地，行为特征数据可以包括数字化棋牌1的运动数据、人体运动数据。优选地，行为特征数据也可以包括便携式智能设备105所采集的用户其他行为信息，例如用户每天所行走的步数、睡眠时间等数据信息。
需要注意的是，上述具体实施例是示例性的，本领域技术人员可以在本发明公开内容的启发下想出各种解决方案，而这些解决方案也都属于本发明的公开范围并落入本发明的保护范围之内。本领域技术人员应该明白，本发明说明书及其附图均为说明性而并非构成对权利要求的限制。本发明的保护范围由权利要求及其等同物限定。本发明说明书包含多项发明构思，诸如“优选地”、“根据一个优选实施方式”或“可选地”均表示相应段落公开了一个独立的构思，申请人保留根据每项发明构思提出分案申请的权利。

Claims (15)

  1. 一种数字生物标志物，其特征在于，所述数字生物标志物的初始数据至少来自用户使用数字化棋牌(1)时用户的行为数据；所述初始数据能够被发送至服务器(2)和/或分析单元(103)，并且经所述服务器(2)和/或分析单元(103)处理分析以计算得出能够表征所述用户的认知能力的所述数字生物标志物。
  2. 根据权利要求1所述的数字生物标志物，其特征在于，所述数字化棋牌(1)包括：棋牌实体(101)，以及设置于或集成于所述棋牌实体(101)内的智能轨迹分析传感器(102)，其中，所述智能轨迹分析传感器(102)至少能够获取所述棋牌实体(101)被所述用户使用过程中的空间坐标数据和/或所述空间坐标数据相对应的时间戳。
  3. 根据权利要求1或2所述的数字生物标志物，其特征在于，在用户能够佩戴便携式智能设备(105)的情况下，所述便携式智能设备(105)被配置为至少能够向所述分析单元(103)和/或所述服务器(2)发送能够标识所述用户的身份信息的识别码，其中，所述分析单元(103)和/或服务器(2)能够获取所述便携式智能设备(105)所携带的能够标识所述用户身份信息的识别码。
  4. 根据权利要求1～3任一项所述的数字生物标志物，其特征在于，设置于所述数字化棋牌(1)内的所述智能轨迹分析传感器(102)所采集的所述行为数据还能够通过所述便携式智能设备(105)发送至所述分析单元(103)和/或所述服务器(2)。
  5. 根据权利要求1～4任一项所述的数字生物标志物，其特征在于，所述初始数据还能够包括以下一种或多种：交互数据、语言数据、眼动数据、瞳孔数据和日常数据，其中，所述交互数据、语言数据、眼动数据、瞳孔数据由分析单元(103)在所述用户使用所述分析单元(103)的交互过程中采集得到，所述日常数据由智能监测单元(104)在所述用户日常生活行为中采集得到。
  6. 一种数字生物标志物，其特征在于，所述数字生物标志物的形成方法为：
    获取初始数据，并对所述初始数据进行数据融合以得到融合数据；
    对所述融合数据提取特征数据并进一步从所述特征数据中计算分析得出候选数字生物标志物；
    对所述候选数字生物标志物进行因果推断，以分析推断出有效的所述数字生物标志物。
  7. 根据权利要求6所述的数字生物标志物，其特征在于，
    在所述数字化棋牌(1)包括棋牌实体(101)和轨迹记录传感器(102)的情况下，所述轨迹记录传感器(102)设置于或集成于所述棋牌实体(101)内，所述轨迹记录传感器(102)至少能够用于获取所述棋牌实体(101)在被所述用户使用的过程中的空间坐标和/或与所述空间坐标相关联的时间戳；
    在所述数字化棋牌(1)集成于移动设备的情况下，所述分析单元(103)能够用于获取所述棋牌实体(101)在被所述用户使用的过程中的交互数据、语言数据、眼动数据和/或瞳孔数据。
  8. 根据权利要求6或7所述的数字生物标志物，其特征在于，所述初始数据包括用户的行为数据，所述行为数据在所述用户使用数字化棋牌(1)的过程中由所述数字化棋牌(1)采集得到，和/或
    所述初始数据包括用户的语言数据，所述语言数据是在所述用户使用数字化棋牌(1)的过程中由分析单元(103)采集得到。
  9. 一种基于数字生物标志物的脑健康评判系统，其特征在于，至少包括：
    数字化棋牌(1)，被配置为至少能够采集用户使用所述数字化棋牌(1)时所述数字化棋牌(1)的运动数据；
    服务器(2)和/或数字化评估单元(4)，被配置为至少能够获取所述运动数据，并基于所述运动数据计算分析得出与参考标志物相对应的数字生物标志物，并基于所述数字生物标志物对所述用户的脑健康进行评估/筛查。
  10. 根据权利要求9所述的基于数字生物标志物的脑健康评判系统，其特征在于，所述数字化棋牌(1)至少包括棋牌实体(101)和智能轨迹分析传感器(102)，所述智能轨迹分析传感器(102)设置于或集成于所述棋牌实体(101)内，所述智能轨迹分析传感器(102)至少能够用于获取所述棋牌(101)被所述用户使用的过程中的空间坐标和/或与所述空间坐标相关联的时间戳。
  11. 根据权利要求9或10所述的基于数字生物标志物的脑健康评判系统，其特征在于，还能够包括数字化评估单元(4)，所述数字化评估单元(4)被配置为能够对用户与所述数字化评估单元(4)之间的交互过程进行监测，以捕获用户的交互数据、语言数据、眼动数据和/或瞳孔数据；
    其中，所述数字化评估单元(4)能够将交互数据、语言数据、眼动数据和/或瞳孔数据发送至所述服务器(2)，以使得所述服务器(2)能够基于所述交互过程计算得出用户的评估数据。
  12. 根据权利要求9～11任一项所述的基于数字生物标志物的脑健康评判系统，其特征在于，
    所述数字化棋牌(1)能够将所述运动数据发送至所述数字化评估单元(4)和/或所述服务器(2)，所述数字化评估单元(4)能够获取所述运动数据，并且能够将所述运动数据发送至所述服务器(2)。
  13. 一种基于数字生物标志物的认知能力评判系统，其特征在于，至少包括：
    数字化评估单元(4)，被配置为：
    能够对用户与所述数字化评估单元(4)之间的交互过程进行监测，以得出交互数据、语言数据、眼动数据和/或瞳孔数据，
    能够基于所述交互过程信息以计算得出评估数据；
    基于所述交互数据、语言数据、眼动数据和/或瞳孔数据和评估数据对所述用户的脑健康进行评判；
    或者，与所述数字化评估单元(4)建立连接的服务器(2)能够获取所述交互过程信息以计算得出评估数据，
    其中，所述服务器(2)被配置为至少能够获取所述交互数据、语言数据、眼动数据和/或瞳孔数据，并且基于所述交互数据、语言数据、眼动数据和/或瞳孔数据和评估数据对所述用户的脑健康进行评判。
  14. 根据权利要求13所述的基于数字生物标志物的认知能力评判系统，其特征在于，所述数字化评估单元(4)或所述服务器(2)设置有预警模块和显示模块，所述预警模块能够比较当前周期对应的用户的认知能力评分与上一周期对应的用户的认知能力评分，并且所述预警模块能够在用户的认知能力评分环比下降超过预设触发阈值时生成第一预警信息，所述第一预警信息能够通过所述显示模块进行显示。
  15. 根据权利要求13或14所述的基于数字生物标志物的认知能力评判系统，其特征在于，还能够包括数字化棋牌(1)，所述数字化棋牌(1)被配置为至少能够捕获用户使用所述数字化棋牌(1)时所述数字化棋牌(1)的运动数据。
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