Motion and cognitive function integrated evaluation system under data fusion

Abstract

The invention relates to a motion and cognitive function integrated evaluation system under data fusion, and belongs to the technical field of motion detection devices. The evaluation system comprises a server and an evaluation platform in communication connection with the server. An interaction unit of the evaluation platform sends an instruction to a user according to an evaluation rule, a lower limb detection unit collects first action data of the user on the bearing platform, an upper limb detection unit collects second action data through a top guide rail and a detection ball, a communication unit is responsible for data interaction with a server, and a control unit controls the upper limb detection unit to execute the instruction. And the evaluation system analyzes the action data of the user based on the evaluation rule so as to evaluate the exercise and cognitive competence of the user. Wherein the evaluation rule comprises that the server selects the rule suitable for the user from the plurality of rules and generates a final rule, and the server analyzes the action data of the user to generate real-time evaluation data and subsequent evaluation rules, so that the comprehensive and dynamic evaluation of the user function is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of motion detection devices, and in particular, relates to an integrated evaluation system for motion and cognitive functions under data fusion. Background Art

[0002] In the fields of modern medical rehabilitation, sports training, and cognitive science research, accurate assessment of individual motor and cognitive abilities is of great significance. Traditional motor function assessment methods often focus on single-dimensional testing, such as analyzing only the motor ability of the lower limbs, or focusing only on simple motor reactions of the upper limbs. In terms of cognitive ability assessment, most of them are independent tests, such as memory tests, attention tests, etc., and there is a lack of a system for integrating motor and cognitive functions.

[0003] As the research on the overall interaction mechanism between the brain and the body deepens, people gradually realize that there is a close connection between movement and cognitive function. For example, when performing complex motor tasks, such as walking through a maze or operating complex equipment, cognitive resources need to be used simultaneously to plan paths and solve possible problems. The decline in cognitive ability will also affect the individual's motor performance. For example, among the elderly, cognitive impairment is often accompanied by a higher risk of falling, reflecting that their motor ability is also impaired to varying degrees.

[0004] According to the relevant public technologies, the technical solution with publication number WO2020176420A1 proposes a sports training system that combines cognitive tasks with physical training. It uses multiple input devices to generate ergonomic training programs that combine cognitive and motor abilities to improve the trainee's cognitive and motor coordination abilities. Publication number WO2015057471A3 proposes a remote rehabilitation platform for restoring patients' cognitive and motor abilities. It provides a network platform that allows therapists to interact with patients remotely. By providing programs and equipment suitable for interactive behaviors, therapists can easily complete remote treatment of patients. The technical solution with announcement number CN109688926B proposes a method for evaluating cognitive and motor diseases or disorders in subjects suspected of being ill. It analyzes the cognitive ability and fine motor ability of the subject by running a detection program on a mobile device and interacting with the subject, and adjusting the detection method of the detection program in real time.

[0005] The above technical solutions all propose to use various devices to conduct combined detection and training of patients' motor and cognitive abilities, but there are currently few efficient and simple integrated assessment systems that can specifically target the motor and cognitive abilities of the elderly.

[0006] The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion does not acknowledge or admit that any of the material referred to is part of the common general knowledge. Summary of the invention

[0007] The purpose of the present invention is to provide an integrated evaluation system for motor and cognitive functions under data fusion, which belongs to the field of sports monitoring technology. The evaluation system includes a server and an evaluation platform connected to the server in communication. The interactive unit of the evaluation platform sends instructions to the user according to the evaluation rules, the lower limb detection unit collects the action data of user A on the carrier platform, the upper limb detection unit collects the action data of user B through the top guide rail and the detection ball, the communication unit is responsible for data interaction with the server, and the control unit controls the upper limb detection unit to execute instructions. The evaluation system analyzes the user's action data based on the evaluation rules to evaluate the user's motor and cognitive abilities. Among them, the evaluation rules include the server selecting a rule suitable for the user from multiple rules and generating a final rule, and also includes the server analyzing the user's action data to generate real-time evaluation data and subsequent evaluation rules, so as to achieve a comprehensive and dynamic evaluation of the user's functions.

[0008] The present invention adopts the following technical solution: an integrated evaluation system for motor and cognitive functions under data fusion, the evaluation system comprising: a server, and an evaluation platform connected to the server in communication;

[0009] Wherein, the evaluation platform includes:

[0010] An interaction unit, configured to send a behavior instruction based on the evaluation rule to the user;

[0011] The lower limb detection unit is laid on the central platform of the evaluation platform and is used to collect the user's A-level when performing evaluation on the evaluation platform. Action Data ;

[0012] An upper limb detection unit is arranged on the upper part of the evaluation platform, and includes a top rail and two or more detection balls suspended from the top rail to the front of the user; the detection balls include a device for collecting the action data generated by the user when touching the detection balls;

[0013] A communication unit is configured to submit the A to the server Action data and B action data, and receives the Evaluation Rules ;

[0014] a control unit configured to receive a control instruction generated by the server according to the evaluation rule, so as to control the upper limb detection unit to execute the control instruction;

[0015] The evaluation system is based on the evaluation rules and combines and analyzes the action data A and the action data B to evaluate the user's motor ability and cognitive ability.

[0016] Preferably, the lower limb detection unit comprises a carrying platform and a sensor layer;

[0017] The bearing platform is made of a material that can absorb external impact and is non-slip;

[0018] The sensor layer includes a plurality of sensor devices, which are at least used to detect the user's stepping position and stepping force, so as to obtain the Action Data .

[0019] Preferably, each of the detection balls is provided with a B sensor group for detecting the timing when the user touches the detection ball and the movement state of the detection ball itself, and generating B action data.

[0020] Preferably, the detection ball is movably connected to the top guide rail by a driving unit; and the driving unit includes an execution control instruction to pull the suspended detection ball so that the position and / or height of the detection ball changes.

[0021] Preferably, the detection ball further comprises an acousto-optic unit configured to emit a specified sound and / or a specified color of light at a specified time and / or frequency according to a control instruction of the control unit.

[0022] Preferably, the server includes selecting one or more evaluation sub-rules suitable for the motor and cognitive abilities of the current user from the existing multiple evaluation sub-rules, and finally forming the evaluation rule from the multiple evaluation sub-rules.

[0023] Preferably, the server also includes generating real-time action data of the user by instantly analyzing the action data A and the action data B, and generating subsequent evaluation rules based on the real-time action data and the personal information.

[0024] The beneficial effects achieved by the present invention are:

[0025] 1. The evaluation system of this technical solution can organically combine the evaluation of motor ability and cognitive ability. Traditional evaluations are often conducted on a single motor or cognitive function, while this system collects cognitive-related data reflected by the user when touching the detection ball, as well as the user's motion data collected by the lower limb detection unit, and analyzes it based on unified evaluation rules. It can comprehensively evaluate the user's cognitive performance during exercise and the impact of cognition on exercise, and can more accurately reflect the user's comprehensive ability status.

[0026] 2. The evaluation system of this technical solution is highly flexible and personalized. The server can select appropriate rules from multiple evaluation sub-rules to form evaluation rules based on the user's personal information, and in the evaluation process, it can dynamically generate subsequent evaluation rules based on the real-time evaluation data obtained by real-time analysis of action data A and action data B. This adaptive mechanism can adjust the difficulty and tasks for different users at different evaluation stages, ensure the effectiveness and accuracy of the evaluation results, and better adapt to individual differences.

[0027] 3. The evaluation platform of this technical solution adopts a variety of interactive units and data collection methods. The interactive unit sends behavioral instructions to users based on the evaluation rules to guide users to complete corresponding actions; the floor mat and sensor layer of the lower limb detection unit can accurately collect information such as stepping position and strength; the multiple detection balls of the upper limb detection unit can not only collect the touch timing and its own movement state through the internal B sensor group, but also change the position and height under the control of the drive unit, and provide various forms of stimulation feedback with the help of the sound and light unit, enriching the data collection dimension and improving the richness and accuracy of the evaluation data.

[0028] 4. The software and hardware parts of the evaluation system of this technical solution adopt a modular design. The various working modules and components of the hardware part of the system, as well as the instructions, parameters, and algorithms of the software part can be conveniently replaced and / or upgraded later, thereby reducing the construction cost and maintenance cost of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0030] Description of the accompanying drawings: 100-evaluation system; 200-server; 300-evaluation platform; 210-controller; 211-personal information input unit; 212-information processing unit; 213-evaluation platform control unit; 214-evaluation result storage unit; 215-evaluation result output unit; 216-database; 400-evaluation platform; 410-lower limb detection unit; 411-carrying platform; 420-upper limb detection unit; 421-annular guide rail; 422-suspension mechanism; 423-detection ball; 430-interaction unit; 440-control unit; 450-communication unit; 500-computer system; 502-bus; 504-processor; 506-main memory; 508-read-only memory; 510-storage device; 512-display; 514-input device; 516-cursor control device; 518-network device;

[0031] Figure 1It is a schematic diagram of the layout of the evaluation system of the present invention;

[0032] Figure 2 Schematic diagram of the architecture of the evaluation system described in an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of the working steps of the evaluation system described in an embodiment of the present invention;

[0034] Figure 4 is a schematic diagram of the evaluation platform described in an embodiment of the present invention;

[0035] Figure 5 It is a schematic diagram of the framework of the computer system used by the server in the embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. For those skilled in the art, after reviewing the following detailed description, other systems, methods and / or features of the present embodiment will become apparent. It is intended that all such additional systems, methods, features and advantages are included in this specification. Included within the scope of the present invention and protected by the appended claims. Additional features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0037] The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right" and the like indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation. The invention is constructed and operated in a specific orientation, so the terms describing the positional relationship in the drawings are only used for exemplary description and cannot be understood as a limitation of this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0038] Embodiment 1: Exemplarily, a motor and cognitive function integrated evaluation system under data fusion, the evaluation system comprises: a server, and an evaluation platform connected to the server in communication;

[0039] Wherein, the evaluation platform includes:

[0040] An interaction unit, configured to send a behavior instruction based on the evaluation rule to the user;

[0041] The lower limb detection unit is laid on the central platform of the evaluation platform and is used to collect the user's A-level when performing evaluation on the evaluation platform. Action Data ;

[0042] An upper limb detection unit is arranged on the upper part of the evaluation platform, and includes a top rail and two or more detection balls suspended from the top rail to the front of the user; the detection balls include a device for collecting the action data generated by the user when touching the detection balls;

[0043] A communication unit is configured to submit the A to the server Action data and B action data, and receives the Evaluation Rules ;

[0044] a control unit configured to receive a control instruction generated by the server according to the evaluation rule, so as to control the upper limb detection unit to execute the control instruction;

[0045] The evaluation system is based on the evaluation rules and combines and analyzes the action data A and the action data B to evaluate the user's motor ability and cognitive ability.

[0046] Preferably, the lower limb detection unit comprises a carrying platform and a sensor layer;

[0047] The bearing platform is made of a material that can absorb external impact and is non-slip;

[0048] The sensor layer includes a plurality of sensor devices, which are at least used to detect the user's stepping position and stepping force, so as to obtain the Action Data .

[0049] Preferably, each of the detection balls is provided with a B sensor group for detecting the timing when the user touches the detection ball and the movement state of the detection ball itself, and generating B action data.

[0050] Preferably, the detection ball is movably connected to the top guide rail by a driving unit; and the driving unit includes an execution control instruction to pull the suspended detection ball so that the position and / or height of the detection ball changes.

[0051] Preferably, the detection ball further comprises an acousto-optic unit configured to emit a specified sound and / or a specified color of light at a specified time and / or frequency according to a control instruction of the control unit.

[0052] Preferably, the server includes selecting one or more evaluation sub-rules suitable for the motor and cognitive abilities of the current user from the existing multiple evaluation sub-rules, and finally forming the evaluation rule from the multiple evaluation sub-rules.

[0053] Preferably, the server also includes generating real-time action data of the user by instantly analyzing the action data A and the action data B, and generating subsequent evaluation rules based on the real-time action data and the personal information.

[0054] Specifically, combined with Figure 1 and attached Figure 2 An embodiment of the evaluation system is exemplarily described. Figure 1 The schematic diagram of the layout of the evaluation system is shown as an example. Figure 2 2 is an exemplary architecture diagram of the evaluation system. The evaluation system includes a server 200 and an evaluation platform 300. The user stands in the evaluation platform 300 and performs actions and operations according to the instructions of the evaluation platform 300. The evaluation platform 300 collects a series of data of the user during the evaluation process and sends the collected data to the server 200, and the server 200 generates the results of the integrated evaluation of the current user's movement and cognition.

[0055] Exemplarily, according to the functional division, the server 200 may include a controller 210. The controller 210 may include a personal information input unit 211, an information processing unit 212, an evaluation platform control unit 213, an evaluation result storage unit 214, an evaluation result output unit 215 and a database 216.

[0056] The following combination is as attached Figure 3 The evaluation steps shown illustrate the workflow of the evaluation system.

[0057] In a preferred embodiment, executing Figure 3 In step S100 of the workflow, the personal information input unit 211 provides a user interface to receive personal information input from the user. The personal information may include name, age, gender and health status.

[0058] Preferably, the health status may include the user's mental state test results, the user's cognitive function information, the user's daily life fitness measurement, the user's fall risk level, the user's brain age, and the user's happiness index.

[0059] Preferably, the personal information received by the personal information input unit 211 is stored in the database 216 so that it can be retrieved and used at any time later.

[0060] Preferably, the user's personal information can be updated regularly, and after the user completes the motor and cognitive function evaluation provided by the evaluation system, the evaluation results can be updated to the user's personal information.

[0061] In a preferred embodiment, the information processing unit 212 receives the user's personal information and evaluation results, wherein the evaluation results are related to the user's Evaluation Rules of Action Data Based on the personal information and the evaluation results, the information processing unit 212 analyzes and evaluates the user's motor and cognitive function status, thereby generating or updating the user's motor and cognitive function information, and providing optimized information for the user according to the user's motor and cognitive function. Evaluation Rules .

[0062] Preferably, the information processing unit 212 further includes executing the above based on the user's personal information and evaluation results. Evaluation Rules formation.

[0063] In the initial stage, step S200 is executed, and the information processing unit 212 first generates an initial Evaluation rules.

[0064] Among them, the Evaluation Rules It can be one of the existing multiple sets of rules, or one or more sub-rules of multiple sub-rules. The existing rules can be based on historical evaluation optimization summary, and the detection rules are optimized and formed after statistical inspection by relevant technical personnel. The existing rules can be stored in the database 216 for subsequent use.

[0065] and Evaluation Rules The information processing unit 212 may also generate the real-time data according to a predetermined program, and Evaluation Rules Used in subsequent evaluation processes to cyclically obtain user evaluation results.

[0066] Further, the evaluation system executes step S300, and the evaluation platform control unit 213 obtains the determined Evaluation Rules but , read each evaluation sub-rule of the evaluation rule, and generate the evaluation platform needs to execute Operation Instructions , to control the upper limb detection unit 420 to perform actions according to the description of each evaluation sub-rule, such as controlling the height, position, color change, sound, etc. of the detection ball; and also includes generating the interaction unit in the evaluation platform to send to the user Behavioral Instructions .

[0067] Preferably, the evaluation rules can be divided into one or more detection stages, each detection stage includes one or more evaluation sub-rules. After completing a detection stage, the evaluation system can analyze the user's detection results and dynamically generate evaluation sub-rules for the next detection stage.

[0068] Further, the evaluation system executes step S400, and the interaction unit 430 of the evaluation platform continues to send the user the required Behavioral Instructions , Behavioral Instructions For example: the left hand touches the yellow detection ball. Then, wait for the user to execute the behavior instruction.

[0069] Further, the evaluation system executes step S500, and the evaluation platform, such as the lower limb detection unit 410 and the upper limb detection unit 420, starts to collect the user's action data, including but not limited to: the time when the user starts moving, the moving distance, the speed of footsteps, whether any detection ball is touched, and other series of data.

[0070] Furthermore, the evaluation system executes step S600, and the information processing unit 212 records and analyzes the action data of the user executing one or more behavioral instructions to analyze the user's motor ability and cognitive ability reflected by one or more evaluation sub-rules.

[0071] Furthermore, the evaluation system executes step S700, and the information processing unit 212 updates subsequent evaluation sub-rules based on the user's current action data, or based on the movement and cognitive abilities reflected by a series of action data of the user.

[0072] After completing all evaluation rules, the evaluation system executes step S800 to output the final analysis results of the evaluation system on the user's motor and cognitive abilities.

[0073] Further, as attached Figure 4 As shown in FIG. , the structure of the evaluation platform is exemplarily described. Figure 2 and attached Figure 4 The evaluation platform 300 is described below. Exemplarily, the evaluation platform 300 includes a lower limb detection unit 410, an upper limb detection unit 420, and an interaction unit 430; preferably, the communication unit 450 and the control unit 440 are arranged in the internal structure of the lower limb detection unit 410, and are not shown in the figure.

[0074] In a preferred embodiment, the lower limb detection unit 410 includes a carrying platform 411; the carrying platform 411 can be disc-shaped or rectangular, having an aluminum alloy frame, and its surface is covered with a thick silicone buffer layer, which not only achieves a certain buffering effect, but also can reduce the vibration and noise generated when the user moves on its surface.

[0075] Preferably, a sensor layer is provided in the interlayer of the carrier platform. Preferably, the sensor layer is formed by a sensor matrix composed of a plurality of piezoresistive or pressure-sensitive sensors. Preferably, the sampling frequency of the sensor layer may be 60 Hz or 120 Hz, or higher; a higher sampling frequency may obtain more analysis data, but at the same time it will occupy more computing resources, which needs to be determined by relevant technical personnel according to actual needs.

[0076] Preferably, based on the data from the sensors, dynamic analysis of the user's step length, step frequency, gait cycle, etc. can be achieved.

[0077] In some embodiments, the user's lower limb symmetry index G is calculated by the following formula, and by analyzing the pressure comparison between the left and right soles of the user, it can be analyzed whether the pressure of the user's legs is balanced;

[0078]

[0079] Among them, P L and P R The average single foot pressure of the user's left foot and right foot respectively.

[0080] In some embodiments, the stable domain area Area of ​​the user's center of gravity projection point COP can be calculated by the following calculation formula, and the stability of the center of gravity projection point can be analyzed to infer the user's balance ability and reflect the ability range of the human body to maintain static or dynamic stability on the support surface; the COP (Center of Pressure) trajectory refers to the movement path of the center of pressure on the support surface during standing or movement.

[0081]

[0082] Where a and b are the major and minor axis lengths of the COP trajectory envelope ellipse, respectively.

[0083] In some embodiments, the method further includes analyzing the user's movement coordination based on the COP, including the pressure migration rate V cop :

[0084]

[0085] In the above formula, xt and yt are the position coordinates of the user's center of gravity projection point COP on the carrying platform 411 at time t, and Δt is the detection step length, which can be 0.1 s or shorter.

[0086] In a preferred embodiment, the upper limb detection unit 420 is composed of an annular modular structure, and includes an annular guide rail 421 , a suspension mechanism 422 and a detection ball 423 .

[0087] The maximum outer diameter of the annular guide rail 421 is preferably slightly larger than the carrying platform 411, and its diameter may be 2.2 meters or 2 meters, which is substantially equivalent to the user's arm span, or slightly larger than the user's arm span. The annular guide rail 421 is preferably made of a light alloy such as an aluminum alloy, and includes a main track, a pulley module that can slide on the main track by electric drive, and a deceleration drive system that drives the pulley module to move.

[0088] Furthermore, each trolley module is connected to a detection ball 423 via a carbon fiber cable, and the detection ball 423 is suspended around the user. Only two detection balls 423 are shown in the figure. In actual implementation, 4 or 6 detection balls 423 can be configured, but the combination diversity during detection is increased.

[0089] Preferably, a force sensor may be configured at the end of the pulley module to detect the force exerted on the detection ball, such as the tension generated by the carbon fiber cable when the user pulls the detection ball 423. A quick connector may also be configured at the end of the pulley module to achieve quick loading, unloading and replacement of the detection ball 423.

[0090] Preferably, the detection ball 423 is also provided with a B sensor, and the data generated by the B sensors of the multiple detection balls 423 are packaged into a B sensor group data set to be synchronously sent to the server 200. The B sensor may include a 9-axis IMU, which can simultaneously sense the acceleration and angular velocity values ​​of the detection ball 423 itself; the B sensor may also include a capacitive tactile array to sense the specific time node and position of the user touching the detection ball 423.

[0091] Preferably, an LED is provided on the surface of the detection ball 423 so that the detection ball 423 presents a required light color.

[0092] Preferably, a speaker is provided inside the detection ball 423; the speaker of each detection ball 423 can be communicated with the control unit by wired or wireless connection, and the control unit controls one or more designated speakers to make sounds, and the control unit can determine that different detection balls 423 make designated sounds, and control parameters such as the volume and frequency of the sound.

[0093] In more implementations, the upper limb detection unit 420 may also be provided with an image acquisition unit to obtain the dynamic posture of the user during the assessment to obtain more analysis data.

[0094] Embodiment 2: This embodiment should be understood to include at least all the features of any one of the aforementioned embodiments and to be further improved thereon.

[0095] In a preferred embodiment, the evaluation rule includes determining the position layout of multiple simulation balls 423.

[0096] Taking the circular annular guide rail 421 as an example, in this step, the initial distribution angle of each detection ball 423 is first defined:

[0097]

[0098] Where n = 1, 2, ..., N, N is the number of detection balls, φ is the offset angle, and the optional value is 0° to 30°.

[0099] Furthermore, the movement of the detection ball 423 may include a linear oscillation mode, a random walk mode, or a static fixed point mode, etc., to achieve different detection requirements and detection difficulties.

[0100] Furthermore, including in the initial stage of the evaluation rule, the information processing unit 212 first extracts the basic information of the user to determine the basic motor ability and cognitive ability of the user.

[0101] The user information that can be extracted includes height, upper limb length, lower limb length, body fat percentage, etc.

[0102] Preferably, the athletic ability also includes the user's static balance stability index, grip strength, hand strength, hand movement rate, lower limb movement rate. In addition, the user's heart rate, blood pressure, respiratory rate, etc. can also be obtained by having the user wear a detection instrument.

[0103] Regarding cognitive ability, a basic Stroop color-word conflict test can be optionally performed to calculate the user's cognitive flexibility and interference effect on the user by measuring the conflict between the LED color instructions of the detection ball and the voice prompts. Alternatively, an N-back working memory test can be performed on the user to build a spatial memory load using the light-emitting sequence of the detection ball.

[0104] Preferably, in an exemplary embodiment, the following calculation formula may be used to update the difficulty coefficient D of the next detection sub-rule or multiple detection sub-rules of the next detection stage: next :

[0105]

[0106] In the above formula, D current A is the difficulty coefficient of the current detection sub-rule or the current detection stage; actual The actual score of the user's execution of the evaluation sub-rule; A expectedThe evaluation system predicts the expected score of the current user when executing an evaluation sub-rule; α is the difficulty adjustment sensitivity coefficient, which can be set by technical personnel based on the user's personal information, such as 0.3 for healthy people and 0.15 for newly recovered patients.

[0107] Among them, the actual score of the user's execution of the evaluation sub-rule can be independently determined according to each evaluation sub-rule. For example, the evaluation sub-rule may stipulate that it is necessary to calculate the user's reaction time, action speed, touch accuracy (for example, multiple touches on the detection ball are required) in the action data during this action, or it may also include consideration of the user's dynamic pulse, blood pressure and other physical data.

[0108] In addition, B t To assess the user's motor and cognitive baseline abilities in real time; t is the user's ultimate potential evaluated in real time; β is the learning factor.

[0109] For example, for a user, the average reaction time may be 0.8s, corresponding to an absolute reaction time score of 4 points, but the user's possible extreme reaction time may be 0.4s, corresponding to an absolute reaction time score of 8 points; therefore, we can simply set B t =4,P t =8. The above example is only for the convenience of understanding the technical solution. t and P t When setting the value, it needs to be set by relevant technical personnel after comprehensive evaluation of the user's various information based on the automated evaluation program.

[0110] Among them, B t The initial value B0 of can be based on the comprehensive ability benchmark value calculated through preliminary simple tests of the user, such as static standing, basic reaction test, etc., and basic physical conditions, such as average blood pressure, respiratory rate, pulse, etc.; P t The initial value of is P0, which is determined based on the potential ability limit predicted by the user's age, medical history, and body data (such as joint mobility).

[0111] And the initial difficulty coefficient D initial Can be set to: D initial =0.7B0+0.3P0.

[0112] D next In the calculation formula, the first part reflects the adaptive adjustment item of dynamic difficulty adjustment. The second part takes into account the comparison between the user's limit ability and the baseline ability. When the user is close to the limit, the degree of change in difficulty is reduced.

[0113] Among them, the learning factor β can be used to smooth out possible unexpected scores, so that the difficulty adjustment process can be smoothed.

[0114] Embodiment 3: This embodiment should be understood to include at least all the features of any one of the aforementioned embodiments and to be further improved thereon.

[0115] For example, as shown in the attached Figure 5 As shown, an implementation of a computer system 500 used by the server 200 is described; the computer system 500 can be applied to the data storage, calculation and result output process of each working module in the identification and judgment system.

[0116] Illustratively, computer system 500 includes a bus 502 or other communication mechanism for communicating information, and one or more processors 504 coupled with bus 502 for processing information; processor 504 may be, for example, one or more general-purpose microprocessors.

[0117] The computer system 500 also includes a main memory 506, such as a random access memory (RAM), a cache, and / or other dynamic storage device, coupled to the bus 502 for storing information and instructions to be executed by the processor 504; the main memory 506 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processor 504; these instructions, when stored in a storage medium accessible to the processor 504, present the computer system 500 as a special-purpose machine customized to perform the operations specified in the instructions.

[0118] The computer system 500 may also include a read-only memory (ROM) 508 or other static storage device coupled to the bus 502 for storing static information and instructions for the processor 504; a storage device 510 such as a disk, an optical disk, or a USB drive (flash drive) will be coupled to the bus 502 for storing information and instructions.

[0119] Furthermore, coupled to the bus 502 may also include a display 512 for displaying various information, data, media, etc., and an input device 514 for allowing a user of the computer system 500 to control, manipulate and / or interact with the computer system 500 .

[0120] A preferred way to interact with the management system may be through a cursor control device 516, such as a computer mouse or similar control / navigation mechanism.

[0121] Furthermore, the computer system 500 may also include a network device 518 coupled to the bus 502; wherein the network device 518 may include, for example, a wired network card, a wireless network card, a switching chip, a router, a switch, and other components;

[0122] In general, the terms "engine", "component", "system", "database", etc. used in this document may refer to logic embodied in hardware or firmware, or to a collection of software instructions, possibly with entry and exit points, written in a programming language such as Java, C or C++; software components may be compiled and linked into executable programs, installed in a dynamic link library, or may be written in an interpreted programming language (such as BASIC, Perl or Python); it should be understood that software components may be called from other components or from themselves, and / or may be called in response to detected events or interrupts.

[0123] Software components configured to execute on a computing device may be provided on a computer-readable medium, such as a compact disc, digital video disc, flash drive, diskette, or any other tangible medium, or as a digital download (and may be initially stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution); such software code may be stored in part or in whole on a memory device of the executing computing device for execution by the computing device; the software instructions may be embedded in firmware, such as an EPROM; it should also be understood that hardware components may be composed of connected logic units (such as gates and flip-flops), and / or may be composed of programmable units (such as a programmable gate array or processor).

[0124] Computer system 500 includes custom hardwired logic, one or more ASICs or FPGAs, firmware, and / or program logic that, in combination with the computer system, renders computer system 500 a special-purpose computing device, to implement the techniques described herein.

[0125] According to one or more embodiments, the techniques herein are performed by computer system 500 in response to processor 504 executing one or more sequences of one or more instructions contained in main memory 506; such instructions may be read into main memory 506 from another storage medium such as storage device 510; execution of the sequences of instructions contained in main memory 506 causes processor 504 to perform the processing steps described herein; in alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0126] As used herein, the term "non-transitory media" and similar terms refer to any medium that stores data and / or instructions that cause a machine to operate in a specific manner; such non-transitory media may include non-volatile media and / or volatile media; non-volatile media include, for example, optical or magnetic disks, such as storage device 510; volatile media include dynamic memory, such as main memory 506.

[0127] Among them, common forms of non-transitory media include, for example, floppy disks, diskettes, hard disks, solid-state drives, magnetic tapes or any other magnetic data storage medium, CD-ROMs, any other optical data storage medium, any physical medium with a pattern of holes, RAM, PROM and EPROM, FLASH-EPROM, NVRAM, any other memory chip or cartridge, and network versions thereof.

[0128] Non-transient media are distinct from transmission media but may be used in conjunction with transmission media; transmission media participate in the transmission of information between non-transient media; for example, transmission media include coaxial cables, copper wires, and optical fibers, including the wires that make up bus 502; transmission media may also take the form of sound waves or light waves, such as radio waves and infrared data communications.

Claims

1. A motor and cognitive function integrated evaluation system under data fusion, characterized in that: The evaluation system comprises: a server, and an evaluation platform connected to the server in communication; Wherein, the evaluation platform includes: An interaction unit, configured to send a behavior instruction based on the evaluation rule to the user; The lower limb detection unit is laid on the central platform of the evaluation platform and is used to collect the user's A-level when performing evaluation on the evaluation platform. Action Data ; An upper limb detection unit is arranged on the upper part of the evaluation platform, and includes a top rail and two or more detection balls suspended from the top rail to the front of the user; the detection balls include a device for collecting the action data generated by the user when touching the detection balls; A communication unit configured to submit the A to the server Action data and B action data, and receives the Evaluation Rules ; a control unit configured to receive a control instruction generated by the server according to the evaluation rule, so as to control the upper limb detection unit to execute the control instruction; The evaluation system is based on the evaluation rules and combines and analyzes the action data A and the action data B to evaluate the user's motor ability and cognitive ability.

2. The evaluation system according to claim 1, characterized in that: The lower limb detection unit includes a carrying platform and a sensor layer; The bearing platform is made of a material that can absorb external impact and is non-slip; The sensor layer includes a plurality of sensor devices, which are at least used to detect the user's stepping position and stepping force to obtain the A Action Data .

3. The evaluation system according to claim 2, characterized in that: Each of the detection balls is provided with a B sensor group for detecting the timing of a user touching the detection ball and the movement state of the detection ball itself, and generating B action data.

4. The evaluation system according to claim 3, characterized in that: The detection ball is movably connected to the top guide rail by a driving unit; and the driving unit includes an execution control instruction to pull the suspended detection ball so that the position and / or height of the detection ball changes.

5. The evaluation system according to claim 4, characterized in that: The detection ball also includes an acousto-optic unit configured to emit a specified sound and / or a specified color of light at a specified time and / or frequency according to the control instruction of the control unit.

6. The evaluation system according to claim 5, characterized in that: The server includes selecting one or more evaluation sub-rules suitable for the current user's motor and cognitive abilities from the existing multiple evaluation sub-rules, and finally forming the evaluation rule from the multiple evaluation sub-rules.

7. The evaluation system according to claim 6, characterized in that: The server also includes generating real-time action data of the user by instantly analyzing the action data A and the action data B, and generating subsequent evaluation rules based on the real-time action data and the personal information.

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