Nervous system function evaluation method and device, computer equipment and medium

Abstract

The invention discloses a nervous system function evaluation method and device, computer equipment and a medium, and relates to the technical field of intelligent medical treatment. The specific implementation mode comprises the following steps: acquiring sensor data of a plurality of body parts of daily activities of a user, wherein the sensor data is acquired through a sensor arranged in a home environment; obtaining a plurality of motion function evaluation parameters according to the sensor data; obtaining a nervous system function evaluation result based on the whole-age group normal population data model according to the multiple motion function evaluation parameters; and outputting a neurological function risk index and a risk index of the user according to a nervous system function evaluation result. According to the scheme, the nervous system function is evaluated by obtaining multiple evaluation parameters in daily life, and the accuracy and reliability of the evaluation result and the use flexibility of the evaluation method are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of smart medical technology, and in particular to methods, devices, computer equipment and media for evaluating nervous system function. Background Art

[0002] With the aging population and the increase in chronic diseases, the importance of neurological function assessment is becoming increasingly prominent. Traditional neurological assessments usually need to be conducted in medical institutions, which are limited by professional equipment and medical personnel. This not only increases the cost of the assessment, but also limits the frequency and flexibility of the assessment, making it difficult for users to obtain timely health monitoring and assessment. However, the popularity of home neurological function assessment equipment has brought more convenient health monitoring methods, but it often requires users to operate under specific conditions, such as wearing the equipment, performing specific tasks, etc.; and existing equipment often requires users to perform specific operations in an unnatural state, resulting in limited accuracy of the assessment results. For example, in a designated assessment area or using specific equipment, this will affect the user's natural state and behavior, limiting the accuracy of the assessment results. In addition, the data model lacks universality. The data models used by many existing assessment systems lack broad applicability and fail to fully consider the differences in different age groups, genders and physical conditions, resulting in the reliability and accuracy of the assessment results being affected. Summary of the Invention

[0003] Aiming at the problems that existing nervous system function assessment methods have low flexibility in use and the accuracy and reliability of assessment results are easily affected, a nervous system function assessment method, device, electronic device and storage medium are provided.

[0004] According to a first aspect, a method for evaluating nervous system function is provided, comprising:

[0005] Acquire sensor data of multiple body parts of a user's daily activities, wherein the sensor data is obtained through sensors arranged in a home environment;

[0006] obtaining a plurality of motor function assessment parameters according to the sensor data;

[0007] Obtaining a nervous system function assessment result based on the multiple motor function assessment parameters and a normal population data model of all age groups;

[0008] Output the user's neurological function risk indicator and risk index according to the neurological function assessment result.

[0009] According to a second aspect, there is provided a nervous system function assessment device comprising:

[0010] a first acquiring unit, configured to acquire sensor data of multiple body parts of a user's daily activities, wherein the sensor data is obtained by sensors arranged in a home environment;

[0011] a second acquiring unit, configured to acquire a plurality of motor function assessment parameters according to the sensor data;

[0012] An evaluation unit, configured to obtain a nervous system function evaluation result based on the plurality of motor function evaluation parameters and a data model of normal people of all age groups;

[0013] An output unit is used to output the user's neurological function risk indicator and risk index based on the neurological function assessment result.

[0014] According to a third aspect, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method as in any embodiment of the method for evaluating nervous system function.

[0015] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method of any embodiment of the method for evaluating nervous system function is implemented.

[0016] According to the solution of the present application, sensor data obtained from the user's daily activities is used to assess nervous system function. The sensors used are placed in the home environment and can implicitly collect data during daily activities, capturing the user's natural state and behavior. This assessment method can more accurately reflect the user's actual health status and reduce the interference of human factors on the assessment results. The implicit assessment method of the present application uses non-contact, non-wearable intelligent interactive sensors that only need to be placed in the home environment. There is no need for the user to perform additional operations or wear equipment, thereby reducing the user's burden and improving the convenience and user acceptance of the assessment. By continuously collecting the user's health data in the home environment, real-time monitoring and dynamic assessment of the user's health status can be achieved, increasing the frequency and flexibility of the assessment, and enabling users to obtain timely health feedback and guidance. At the same time, health assessment based on data models of normal people of all age groups can better consider differences in different age groups, genders and physical conditions, improve the reliability and accuracy of assessment results, help users identify potential health risks, and provide data basis for formulating personalized health advice and rehabilitation training programs. By collecting data from multiple sensors, the user's nervous system function can be comprehensively analyzed from multiple dimensions, improving the comprehensiveness, reliability and accuracy of the nervous system function assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0018] Figure 1 is an exemplary system architecture diagram to which some embodiments of the present application may be applied;

[0019] Figure 2 is a flow chart of an embodiment of a method for evaluating nervous system function according to the present application;

[0020] Figure 3 is a schematic diagram of an application scenario of the nervous system function assessment method according to the present application;

[0021] Figure 4 is a schematic structural diagram of an embodiment of a nervous system function assessment device according to the present application;

[0022] Figure 5 4 is a block diagram of an electronic device used to implement the nervous system function evaluation method according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following description of exemplary embodiments of the present application is made in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the nervous system function assessment method or nervous system function assessment apparatus of the present application can be applied.

[0026] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0027] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as video applications, live broadcast applications, instant messaging tools, email clients, social platform software, etc.

[0028] The terminal devices 101, 102, and 103 here can be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with display screens, including but not limited to smartphones, tablet computers, e-book readers, laptop computers, and desktop computers, etc. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, multiple software or software modules for providing distributed services), or they can be implemented as a single software or software module. No specific limitation is made here.

[0029] The server 105 may be a server that provides various services, such as a backend server that provides support to the terminal devices 101, 102, and 103. The backend server may analyze and process received sensor data and other data, and feed back the processing results (such as neurological function risk indicators and risk indexes) to the terminal device.

[0030] It should be noted that the nervous system function assessment method provided in the embodiment of the present application can be executed by the server 105 or the terminal devices 101, 102, 103, and accordingly, the nervous system function assessment device can be set in the server 105 or the terminal devices 101, 102, 103.

[0031] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0032] Continue to refer Figure 2 , shows a process 200 of an embodiment of a method for evaluating nervous system function according to the present application. The method for evaluating nervous system function comprises the following steps:

[0033] Step 201: Acquire sensor data of multiple body parts of a user's daily activities, wherein the sensor data is obtained through sensors arranged in a home environment;

[0034] Step 202: obtaining a plurality of motor function assessment parameters according to the sensor data;

[0035] Step 203: Obtain a nervous system function assessment result based on the multiple motor function assessment parameters and a normal population data model of all age groups;

[0036] Step 204: Output the user's neurological function risk indicator and risk index according to the neurological function assessment result.

[0037] In this embodiment, sensor data obtained from the user's daily activities is used to evaluate the function of the nervous system. The sensors used are arranged in a home environment and can implicitly collect data during daily activities to capture the user's behavior in a natural state. This evaluation method can more accurately reflect the user's actual health status and reduce the interference of human factors on the evaluation results. The implicit evaluation method of this embodiment uses non-contact, non-wearable smart interactive sensors, which only need to be arranged in a home environment. The user does not need to perform additional operations or wear equipment, thereby reducing the burden on the user and improving the convenience and user acceptance of the evaluation. By continuously collecting the user's health data in the home environment, real-time monitoring and dynamic evaluation of the user's health status can be achieved, the frequency and flexibility of the evaluation are improved, and the user can obtain health feedback and guidance in a timely manner.

[0038] The data model for normal people of all age groups in this embodiment can be applied to the national population norm database of quantitative parameters of neurological function. This database provides a reference for the normal value range for clinical quantitative evaluation of neurological function and establishes the normal value range of quantitative parameters for two core neurological functions: movement and cognition. Therefore, health assessment based on the data model of normal people of all age groups can better consider the differences between different age groups, genders, and physical conditions, improve the reliability and accuracy of assessment results, help users identify potential health risks, and provide data basis for formulating personalized health advice and rehabilitation training programs. By collecting data from multiple sensors, the user's nervous system function can be comprehensively analyzed from multiple dimensions, improving the comprehensiveness, reliability, and accuracy of the results of the nervous system function assessment.

[0039] In one embodiment, obtaining sensor data of multiple body parts of a user's daily activities includes:

[0040] Acquire plantar pressure data of the user when standing up from a sitting position, eye movement data when searching for preset items, hand movement data when performing preset hand tasks, and three-dimensional movement trajectory of the user when walking;

[0041] Among them, the plantar pressure data is obtained through a plantar pressure sensor arranged at the user's position, the eye movement data is obtained through an eye movement capture sensor, the hand movement data is obtained through a hand movement capture sensor, and the three-dimensional motion trajectory is obtained through a three-dimensional point cloud data capture device.

[0042] In this embodiment, pressure sensors, eye movement capture sensors, hand movement capture sensors and three-dimensional point cloud data capture devices arranged in a home environment are used to collect data on the movement of various parts of the body. A non-contact, non-wearable data collection method is adopted, which reduces the burden on users and improves the convenience and user acceptance of the assessment. In addition, this embodiment integrates the user's standing movements, eye movements, hand movements and walking movements, comprehensively analyzes the user's nervous system functions from multiple dimensions, and provides more comprehensive and detailed health assessment results.

[0043] In one embodiment, obtaining a plurality of motor function assessment parameters according to the sensor data includes:

[0044] Standing action evaluation parameters are obtained based on the plantar pressure data, visual search function evaluation parameters are obtained based on the eye movement data, hand movement function evaluation parameters are obtained based on the hand movement data, and gait function evaluation parameters are obtained based on the three-dimensional movement trajectory.

[0045] In this embodiment, the relevant pressure data obtained when the user stands up from a sitting position can be analyzed to obtain standing movement assessment parameters, which can be used to detect the user's lower limb strength and balance ability; the acquired eye movement data can be analyzed to obtain visual search function assessment parameters, which can be used to evaluate the user's visual search ability and cognitive function; the acquired hand movement data can be analyzed to obtain hand movement function assessment parameters, which can be used to evaluate the movement accuracy and coordination of hand movements; and the acquired three-dimensional movement trajectory can be analyzed to obtain gait function assessment parameters, which can be used to detect the user's walking ability and lower limb coordination. In this embodiment, by obtaining corresponding assessment parameters through sensor data and evaluating nervous system function through multiple assessment parameters, the user's health status can be analyzed in real time, providing accurate nervous system function assessment results.

[0046] In one embodiment, obtaining standing-up action assessment parameters based on the plantar pressure data includes:

[0047] Extracting, based on the plantar pressure data, the start time and end time of the user's standing-up action, as well as the left foot pressure value and the right foot pressure value at each data point at different standing heights during the standing-up process;

[0048] Obtaining a standing-up time from a sitting position to a standing position according to the standing-up action start time and the standing-up action end time;

[0049] Obtaining a standing-up speed according to the standing-up time and the standing-up height;

[0050] Obtaining standing stability based on the difference between the left foot pressure value and the right foot pressure value at each data point;

[0051] The standing-up duration, the standing-up speed, and the standing-up stability are used as standing-up action evaluation parameters.

[0052] In specific implementation, the standing action assessment can be carried out by placing pressure sensors on the user's usual seat to obtain pressure data. The following example uses the addition of pressure sensors on sofas and carpets to evaluate the speed and stability of the user's movement from sitting to standing, so as to accurately detect the user's lower limb strength and balance ability. Assessment principle: When the user stands up from a sitting position, the pressure sensor records the changes in the pressure on the soles of the feet during the user's standing up process. By analyzing this data, the speed and stability of the user's standing action are assessed. Specifically, the following steps are included:

[0053] The user sits down on a sofa or carpet;

[0054] The pressure sensor records the user's process from sitting to standing, and extracts the user's standing up action start time, standing up action end time, and left foot pressure value and right foot pressure value at each data point from the sensor data;

[0055] Based on the systematic analysis of the extracted sensor data, the standing-up duration, standing-up speed and standing-up stability are calculated.

[0056] The calculation formula for standing time is:

[0057] t stand =t end1 -t start1

[0058] Among them, t stand is the standing time (unit: seconds), t start1 is the starting time of standing up (unit: seconds), t end1 The time it takes to stand up (unit: seconds);

[0059] The formula for calculating the standing speed is:

[0060]

[0061] Among them, v stand is the standing speed (unit: m / s), h stand is the standing height (unit: meter);

[0062] The formula for calculating the stability of standing up is:

[0063]

[0064] Among them, s stand is the standing stability, n is the total number of collected data points, p L,iis the left foot pressure value of the i-th pressure data point, p R,i is the right foot pressure value of the i-th pressure data point.

[0065] The correlation between the various standing movement assessment parameters calculated above and the results of the nervous system function assessment is as follows:

[0066] Time to stand up: A shorter time indicates better lower limb strength; a longer time may indicate insufficient lower limb strength or movement disorders;

[0067] Getting up speed: The faster the speed, the stronger the user's ability to get up; the slower the speed, the worse the lower limb strength or coordination.

[0068] Stability when standing up: A lower stability value indicates that the user has better balance when standing up; a higher value may indicate that the user has balance issues.

[0069] In one embodiment, obtaining a visual search function evaluation parameter based on the eye movement data includes:

[0070] Extracting, based on the eye movement data, the scanning angle distance, scanning duration, gaze start time when gazing at the preset object, gaze end time when gazing at the preset object, and actual position angle of the gaze point when the user searches for the preset object;

[0071] Obtaining a scanning speed according to the scanning angle distance and the scanning duration;

[0072] Obtaining a gaze duration according to the gaze start time and the gaze end time;

[0073] Obtaining a search path deviation according to the actual position angle of the gaze point and the preset position angle of the gaze point;

[0074] The scanning speed, the fixation duration and the search path deviation are used as the visual search function evaluation parameters.

[0075] In this embodiment, by capturing eye movement data through sensors and analyzing multiple aspects of eye data, parameter data that can accurately evaluate eye movement function can be obtained, thereby providing effective parameter data for nervous system function evaluation and improving the accuracy of evaluation results.

[0076] In specific implementation, the eye movement data of a user searching for items in a refrigerator is recorded using an eye movement capture sensor as an example. By calculating the scanning speed, the gaze duration, and the search path deviation through the sensor data, the user's visual search ability and cognitive function can be effectively evaluated. Evaluation principle: Analyze the user's eye movement data, including scanning speed, dwell time, and search path of the target item, to evaluate their visual search ability and cognitive function. Specifically, the following steps are included:

[0077] The user opens the refrigerator door and begins searching for a specific item;

[0078] The eye movement capture sensor records the user's eye movement data in real time;

[0079] Extracting relevant data of the user's scanning angle distance, scanning duration, gaze start time when gazing at the preset object, gaze end time when gazing at the preset object, and actual position angle of the gaze point when searching for the preset object;

[0080] Eye movement data were systematically analyzed based on the extracted data, and saccadic velocity, fixation duration, and search path deviation were calculated.

[0081] Specifically, the calculation formula for scanning speed is:

[0082]

[0083] Among them, v saccade is the scanning speed (unit: degrees / second), d i is the angular distance of the i-th scan (unit: degree), t i is the time of the i-th scan (unit: seconds);

[0084] The calculation formula for fixation time is: fixation =t end2 -t start2 ,

[0085] Among them, t fixation is the fixation time (unit: seconds), t start2 is the fixation start time (unit: seconds), t end2 is the fixation end time (unit: seconds);

[0086] The calculation formula for the search path deviation is:

[0087] Where, Δd is the search path deviation (unit: degree), p i is the actual position angle of the i-th gaze point (unit: degree), p ideal Preset position angle for the gaze point (unit: degrees).

[0088] The correlation between the above-calculated visual search function evaluation parameters and the results of the nervous system function evaluation is as follows:

[0089] Scanning speed: A faster speed indicates that the user has strong visual search ability and can quickly locate the target item; a slower speed indicates that the user has difficulty in the visual search process, which may be related to cognitive impairment;

[0090] Gaze duration: A shorter time indicates that the user can quickly identify the target object; a longer time indicates that the user has difficulty in identifying the target object or has attention problems;

[0091] Search path deviation: The smaller the deviation, the closer the user's search path is to the ideal state and the higher the visual search efficiency; the larger the deviation, the more likely the user is lost or distracted during the search process.

[0092] In one embodiment, obtaining hand movement function assessment parameters according to the hand movement data includes:

[0093] extracting, based on the hand motion data, the actual hand position, movement distance, movement duration, number of tremors in a tremor cycle, and total duration of the tremor cycle when the user performs a preset hand task;

[0094] obtaining hand movement accuracy according to the actual hand position and a target position of a preset trajectory corresponding to the preset hand task;

[0095] Obtaining a hand movement speed according to the movement distance and the movement duration;

[0096] Obtaining a hand tremor frequency according to the number of tremors in the tremor cycle and the total duration of the tremor cycle;

[0097] The hand movement accuracy, the hand movement speed and the hand tremor frequency are used as the hand movement function evaluation parameters.

[0098] In this embodiment, by capturing hand motion data through sensors and analyzing multiple aspects of hand motion data, parameter data that can accurately evaluate hand motion function can be obtained, thereby providing effective parameter data for nervous system function evaluation and improving the accuracy of evaluation results.

[0099] In specific implementation, the hand motion assessment is explained by adding a hand motion capture sensor in front of the wash mirror. The accuracy of the user's hand movements and spatial position when brushing teeth and washing face is evaluated to detect the user's hand fine motor skills and coordination. Evaluation principle: The hand motion capture sensor records the user's movement trajectory when completing preset hand tasks (such as brushing teeth and washing face). By analyzing this data, the user's hand movement accuracy and coordination are evaluated. Specifically, the following steps are included:

[0100] Instruct users to brush their teeth and wash their face before the mirror;

[0101] The hand motion capture sensor starts recording the user's hand motion data;

[0102] Extracting the actual hand position, movement distance, movement duration, number of tremors in a tremor cycle, and total duration of the tremor cycle when the user performs a preset hand task;

[0103] The hand movements were systematically analyzed based on the extracted data, and movement accuracy, movement speed, and tremor frequency were calculated.

[0104] Specifically, the calculation formula for hand movement accuracy is:

[0105]

[0106] Among them, a accuracy is the motion accuracy, n is the total number of data points collected for hand motion, and p i ′ is the actual position of the i-th hand data point, p target is the target position of the preset trajectory;

[0107] The calculation formula for hand movement speed is:

[0108]

[0109] Among them, v motion is the hand movement speed (unit: m / s), d is the movement distance (unit: m), and t is the movement duration (unit: seconds);

[0110] The formula for calculating hand tremor frequency is:

[0111]

[0112] Among them, f tremor is the tremor frequency (unit: Hz), N is the number of tremors in the tremor cycle; T is the total duration of the tremor cycle (unit: second).

[0113] The correlation between the various hand motor function assessment parameters calculated above and the results of the nervous system function assessment is as follows:

[0114] Hand movement accuracy: Lower accuracy values ​​indicate better coordination and precision in the user's hand movements; higher values ​​may indicate problems with the user's hand movements.

[0115] Hand movement speed: faster speeds indicate better hand movement abilities; slower speeds may indicate limited hand movement abilities.

[0116] Hand tremor frequency: A lower frequency indicates mild hand tremor; a higher frequency indicates severe hand tremor.

[0117] In one embodiment, obtaining gait function assessment parameters according to the three-dimensional motion trajectory includes:

[0118] Extracting the number of steps, the position coordinates of each step, and the total walking time of the user during walking according to the three-dimensional motion trajectory;

[0119] Obtaining an average stride length and gait symmetry according to the number of steps and the position coordinates of each step;

[0120] Obtaining a cadence based on the number of steps and the total walking time;

[0121] The average stride length, the gait symmetry and the stride frequency are used as the gait function evaluation parameters.

[0122] In this embodiment, by capturing walking motion data through sensors and analyzing multiple aspects of walking motion data, parameter data that can accurately evaluate gait function can be obtained, thereby providing effective parameter data for nervous system function evaluation and improving the accuracy of evaluation results.

[0123] In practice, a 3D point cloud data capture device is installed at home to perform a gait function assessment. This allows real-time analysis of the user's gait function to detect their walking ability and lower limb coordination. The assessment works by: The 3D point cloud data capture device records the user's 3D motion trajectory while walking at home. By analyzing this data, the user's stride length, speed, and gait stability are assessed. The assessment involves the following steps:

[0124] Users can walk freely around their homes;

[0125] The 3D point cloud data capture device records the user's gait data in real time;

[0126] Extract data such as the number of steps taken by the user during walking, the location coordinates of each step, and the total walking time;

[0127] The gait condition was systematically analyzed based on the extracted data, and stride length, stride frequency and gait symmetry were calculated.

[0128] Specifically, the calculation formula of the stride parameter is:

[0129]

[0130] Among them, step length is the stride (unit: meter), n is the number of steps in the selected data point, X i is the starting position coordinate of step i;

[0131] The formula for calculating cadence is:

[0132]

[0133] Among them, step frequency is the cadence (unit: steps / minute), steps is the total number of steps, and time is the total walking time (unit: seconds);

[0134] The formula for calculating gait symmetry is:

[0135]

[0136] Among them, symmetry gait is the gait symmetry, n is the number of steps in the selected data points, is the length of the i-th left step, is the length of the i-th right step.

[0137] The correlation between the gait function evaluation parameters calculated above and the results of the nervous system function evaluation is as follows:

[0138] Stride length: A larger stride length indicates a good gait function and a natural walking posture; a smaller stride length indicates a limited gait function or lower limb strength deficiency.

[0139] Cadence: A higher cadence indicates a good gait function and a fast walking speed; a lower cadence indicates a limited gait function or balance problems.

[0140] Gait Symmetry: Lower symmetry values ​​indicate better balance while walking; higher values ​​may indicate balance issues or gait abnormalities.

[0141] By determining the various evaluation parameters mentioned above, a data basis is provided for the evaluation of the nervous system function. This application not only evaluates the neurological function of a single task, but also conducts a multi-dimensional comprehensive evaluation of neurological function by integrating the results of standing movement, eye movement, hand movement and gait function evaluation. This method can integrate a variety of different parameters to establish a more comprehensive and accurate neurological function evaluation model to help detect the user's potential neurological risks.

[0142] In one embodiment, obtaining a nervous system function assessment result based on the multiple motor function assessment parameters and a normal population data model of all age groups specifically includes the following steps:

[0143] Data input: parameters of multiple dimensions, including standing movement assessment parameters, visual search function assessment parameters, hand movement function assessment parameters, gait function assessment parameters, etc., specifically including standing speed, standing stability, scanning speed, search path deviation, hand movement accuracy and gait symmetry;

[0144] Data processing: Transmitting various parameter data to the central control system for data preprocessing and analysis;

[0145] Data fusion: The multi-dimensional parameters after the above processing are input into the evaluation model for data fusion calculation. The evaluation model is constructed through machine learning and has a built-in data model of normal people of all age groups;

[0146] Comprehensive neurological function assessment: Generate comprehensive neurological function assessment results through fusion calculation to evaluate the user's neurological health status;

[0147] Output results: Based on the assessment results, risk indicators indicating possible neurological dysfunction will be output and their risk index will be prompted to provide an overall status of the user's neurological health.

[0148] In this application, various smart sensors are deployed in the home environment to implicitly collect data on a user's daily motor and cognitive functions. Leveraging a built-in data model of a healthy Chinese population across all age groups, this method analyzes the user's health status in real time and provides an accurate assessment of neurological function. This method offers the advantages of non-contact, non-wearable, and natural data collection, enabling a better reflection of the user's true health status, identifying potential risks, and providing a scientific basis for health management and rehabilitation.

[0149] Continue to see Figure 3 , Figure 3 This is a schematic diagram of an application scenario of the method for evaluating nervous system function according to this embodiment. Figure 3 In an application scenario, an execution entity 301 obtains sensor data 302 from multiple body parts of a user's daily activities. This sensor data 302 is obtained by sensors placed in a home environment. Based on this sensor data 302, the execution entity 301 obtains multiple motor function assessment parameters 303. Based on these multiple motor function assessment parameters 303 and a data model of a normal population across all age groups, the execution entity 301 obtains a neurological function assessment result 304. Based on this neurological function assessment result 304, the execution entity 301 outputs a neurological function risk indicator and a risk index 305 for the user.

[0150] Further references Figure 4 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a nervous system function assessment device, which is similar to Figure 2 Corresponding to the method embodiment shown, in addition to the features described below, the device embodiment may also include Figure 2 The device can be applied to various electronic devices.

[0151] like Figure 4 As shown, the nervous system function assessment device 400 of this embodiment includes: a first acquisition unit 401, a second acquisition unit 402, an assessment unit 403, and an output unit 404. The first acquisition unit 401 is configured to acquire sensor data from multiple body parts of a user during daily activities, the sensor data being acquired via sensors placed in a home environment; the second acquisition unit 402 is configured to acquire multiple motor function assessment parameters based on the sensor data; the assessment unit 403 is configured to acquire a nervous system function assessment result based on the multiple motor function assessment parameters and a data model of a normal population of all age groups; and the output unit 404 is configured to output the user's nervous system function risk indicator and risk index based on the nervous system function assessment result.

[0152] In this embodiment, the specific processing of the first acquisition unit 401, the second acquisition unit 402, the evaluation unit 403 and the output unit 404 of the nervous system function evaluation device 400 and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of step 201, step 202, step 203 and step 204 in the corresponding embodiment are not repeated here.

[0153] In some optional implementations of this embodiment, the first acquiring unit 401 is configured to acquire plantar pressure data of the user when standing up from a sitting position, eye movement data when searching for a preset item, hand movement data when performing a preset hand task, and a three-dimensional motion trajectory of the user when walking;

[0154] Among them, the plantar pressure data is obtained through a plantar pressure sensor arranged at the user's position, the eye movement data is obtained through an eye movement capture sensor, the hand movement data is obtained through a hand movement capture sensor, and the three-dimensional motion trajectory is obtained through a three-dimensional point cloud data capture device.

[0155] In some optional implementations of this embodiment, the second acquisition unit 402 is configured to obtain standing action evaluation parameters based on the plantar pressure data, obtain visual search function evaluation parameters based on the eye movement data, obtain hand movement function evaluation parameters based on the hand movement data, and obtain gait function evaluation parameters based on the three-dimensional movement trajectory.

[0156] In some optional implementations of this embodiment, the second acquiring unit 402 is configured to extract, based on the plantar pressure data, the start time and end time of the user's standing-up action, and the left foot pressure value and the right foot pressure value at each data point at different standing-up heights during the standing-up process;

[0157] Obtaining a standing-up time from a sitting position to a standing position according to the standing-up action start time and the standing-up action end time;

[0158] Obtaining a standing-up speed according to the standing-up time and the standing-up height;

[0159] Obtaining standing stability based on the difference between the left foot pressure value and the right foot pressure value at each data point;

[0160] The standing-up duration, the standing-up speed, and the standing-up stability are used as standing-up action evaluation parameters.

[0161] In some optional implementations of this embodiment, the second acquisition unit 402 is configured to extract, based on the eye movement data, the scanning angle distance, scanning duration, gaze start time when gazing at the preset item, gaze end time when gazing at the preset item, and actual position angle of the gaze point when the user searches for the preset item;

[0162] Obtaining a scanning speed according to the scanning angle distance and the scanning duration;

[0163] Obtaining a gaze duration according to the gaze start time and the gaze end time;

[0164] Obtaining a search path deviation according to the actual position angle of the gaze point and the preset position angle of the gaze point;

[0165] The scanning speed, the fixation duration and the search path deviation are used as the visual search function evaluation parameters.

[0166] In some optional implementations of this embodiment, the second acquisition unit 402 is configured to extract, based on the hand motion data, the actual hand position, movement distance, movement duration, number of tremors in a tremor cycle, and total duration of the tremor cycle when the user performs a preset hand task;

[0167] obtaining hand movement accuracy according to the actual hand position and a target position of a preset trajectory corresponding to the preset hand task;

[0168] Obtaining a hand movement speed according to the movement distance and the movement duration;

[0169] Obtaining a hand tremor frequency according to the number of tremors in the tremor cycle and the total duration of the tremor cycle;

[0170] The hand movement accuracy, the hand movement speed and the hand tremor frequency are used as the hand movement function evaluation parameters.

[0171] In some optional implementations of this embodiment, the second acquiring unit 402 is configured to extract the number of steps, the position coordinates of each step, and the total walking time of the user during walking according to the three-dimensional motion trajectory;

[0172] Obtaining an average stride length and gait symmetry according to the number of steps and the position coordinates of each step;

[0173] Obtaining a cadence based on the number of steps and the total walking time;

[0174] The average stride length, the gait symmetry and the stride frequency are used as the gait function evaluation parameters.

[0175] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0176] like Figure 5 , is a block diagram of an electronic device according to a method for evaluating nervous system function according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0177] like Figure 5As shown, the electronic device includes: one or more processors 501, a memory 502, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 501 is taken as an example.

[0178] Memory 502 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the neurological function assessment method provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the neurological function assessment method provided in this application.

[0179] The memory 502 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the nervous system function assessment method in the embodiment of the present application (for example, the attached Figure 4 The processor 501 executes the non-transient software programs, instructions, and modules stored in the memory 502 to execute various functional applications and data processing of the server, thereby implementing the nervous system function assessment method in the above method embodiment.

[0180] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the electronic device for evaluating nervous system function, etc. In addition, the memory 502 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely located relative to the processor 501, and these remote memories may be connected to the electronic device for evaluating nervous system function via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0181] The electronic device of the nervous system function assessment method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0182] The input device 503 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device for evaluating nervous system function, such as input devices such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, and a joystick. The output device 504 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0183] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0185] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0186] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0187] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0188] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0189] The units involved in the embodiments described in this application can be implemented by software or hardware. The units described can also be set in a processor. For example, it can be described as: a processor includes a first acquisition unit, a second acquisition unit, an evaluation unit, and an output unit. The names of these units do not constitute a limitation of the units themselves in some cases. For example, the first acquisition unit can also be described as a "sensor data acquisition unit."

[0190] As another aspect, the present application also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the device, the device causes the device to: obtain sensor data of multiple body parts of the user's daily activities, where the sensor data is obtained by sensors arranged in the home environment; obtain multiple motor function assessment parameters based on the sensor data; obtain a neurological function assessment result based on the multiple motor function assessment parameters and a data model of normal people of all age groups; and output the user's neurological function risk indicator and risk index based on the neurological function assessment result.

[0191] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for evaluating nervous system function, comprising: Acquire sensor data of multiple body parts of a user's daily activities, wherein the sensor data is obtained through sensors arranged in a home environment; obtaining a plurality of motor function assessment parameters according to the sensor data; Obtaining a nervous system function assessment result based on the multiple motor function assessment parameters and a normal population data model of all age groups; Output the user's neurological function risk indicator and risk index according to the neurological function assessment result.

2. The method for evaluating nervous system function according to claim 1, wherein: The method of obtaining sensor data of multiple body parts of the user's daily activities includes: Acquire plantar pressure data of the user when standing up from a sitting position, eye movement data when searching for preset items, hand movement data when performing preset hand tasks, and three-dimensional movement trajectory of the user when walking; Among them, the plantar pressure data is obtained through a plantar pressure sensor arranged at the user's position, the eye movement data is obtained through an eye movement capture sensor, the hand movement data is obtained through a hand movement capture sensor, and the three-dimensional motion trajectory is obtained through a three-dimensional point cloud data capture device.

3. The method for evaluating nervous system function according to claim 2, wherein: The obtaining of a plurality of motor function assessment parameters according to the sensor data includes: Standing action evaluation parameters are obtained based on the plantar pressure data, visual search function evaluation parameters are obtained based on the eye movement data, hand movement function evaluation parameters are obtained based on the hand movement data, and gait function evaluation parameters are obtained based on the three-dimensional movement trajectory.

4. The method for evaluating nervous system function according to claim 3, wherein: The step of obtaining a standing action evaluation parameter according to the plantar pressure data includes: Extracting, based on the plantar pressure data, the start time and end time of the user's standing-up action, as well as the left foot pressure value and the right foot pressure value at each data point at different standing heights during the standing-up process; Obtaining a standing-up time from a sitting position to a standing position according to the standing-up action start time and the standing-up action end time; Obtaining a standing-up speed according to the standing-up time and the standing-up height; Obtaining standing stability based on the difference between the left foot pressure value and the right foot pressure value at each data point; The standing-up duration, the standing-up speed, and the standing-up stability are used as standing-up action evaluation parameters.

5. The method for evaluating nervous system function according to claim 3, wherein: Obtaining visual search function evaluation parameters according to the eye movement data includes: Extracting, based on the eye movement data, the scanning angle distance, scanning duration, gaze start time when gazing at the preset object, gaze end time when gazing at the preset object, and actual position angle of the gaze point when the user searches for the preset object; Obtaining a scanning speed according to the scanning angle distance and the scanning duration; Obtaining a gaze duration according to the gaze start time and the gaze end time; Obtaining a search path deviation according to the actual position angle of the gaze point and the preset position angle of the gaze point; The scanning speed, the fixation duration and the search path deviation are used as the visual search function evaluation parameters.

6. The method for evaluating nervous system function according to claim 3, wherein: The obtaining of hand movement function evaluation parameters according to the hand movement data includes: extracting, based on the hand motion data, the actual hand position, movement distance, movement duration, number of tremors in a tremor cycle, and total duration of the tremor cycle when the user performs a preset hand task; obtaining hand movement accuracy according to the actual hand position and a target position of a preset trajectory corresponding to the preset hand task; Obtaining a hand movement speed according to the movement distance and the movement duration; Obtaining a hand tremor frequency according to the number of tremors in the tremor cycle and the total duration of the tremor cycle; The hand movement accuracy, the hand movement speed and the hand tremor frequency are used as the hand movement function evaluation parameters.

7. The method for evaluating nervous system function according to claim 3, wherein: Obtaining gait function evaluation parameters according to the three-dimensional motion trajectory includes: Extracting the number of steps, the position coordinates of each step, and the total walking time of the user during walking according to the three-dimensional motion trajectory; Obtaining an average stride length and gait symmetry according to the number of steps and the position coordinates of each step; Obtaining a cadence based on the number of steps and the total walking time; The average stride length, the gait symmetry and the stride frequency are used as the gait function evaluation parameters.

8. A device for evaluating nervous system function, comprising: a first acquisition unit, configured to acquire sensor data of multiple body parts of a user during daily activities; a second acquiring unit, configured to acquire a plurality of motor function assessment parameters according to the sensor data; An evaluation unit, configured to obtain a nervous system function evaluation result based on the plurality of motor function evaluation parameters and a data model of normal people of all age groups; An output unit is used to output the user's neurological function risk indicator and risk index based on the neurological function assessment result.

9. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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