Group athletic ability assessment method and device based on rehabilitation training pad

Abstract

The invention relates to the technical field of athletic ability assessment, and discloses a group athletic ability assessment method and device based on a rehabilitation training pad. The method comprises the following steps: acquiring pressure matrix data of a rehabilitation training pad in a hand area and a foot area; calculating gravity center matrix data and reference gravity center matrix data according to the pressure matrix data of the rehabilitation training pad in the hand area and the foot area; calculating a gravity center stability index according to the pressure matrix data and the gravity center matrix data of the rehabilitation training pad in the hand area and the foot area; according to the pressure matrix data, the gravity center matrix data and the reference gravity center matrix data of the rehabilitation training pad in the hand area and the foot area, calculating a gravity center balance index; and calculating an athletic ability scoring index according to the gravity center stability index and the gravity center balance index, and performing group athletic ability evaluation based on the athletic ability scoring index.

Description

Technical Field

[0001] The present invention relates to the technical field of motor ability assessment, and in particular to a method and device for assessing group motor ability based on a rehabilitation training mat. Background Art

[0002] As the world's population ages, daily care for the elderly has become a major social need. Aging and disease have caused a decline in the motor function of the elderly, and the risk of falls among the elderly is increasing. The risk of falls threatens the elderly, and the social costs and disease burden caused by falls are becoming increasingly heavy. Timely prediction of the elderly's motor ability is critical to effective care, allowing caregivers to intervene as early as possible to ensure the safety of the caregivers, thereby improving the quality of life of the elderly.

[0003] Motor ability assessment is one of the effective means to deal with motor impairment. There are many motor ability methods currently used in clinical practice, including TUG (Timed Up and Go Test), POMA (Performance-Oriented Mobility Assessment), BBT (Berg Balance Test), EFST (Elderly Fall Screening Test), DGI (Dynamic Gait Index), etc. Although motor ability assessment methods have been widely adopted, current motor ability assessments are mostly based on video image analysis, and the equipment setup is cumbersome and not suitable for rapid screening of group motor ability. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and device for evaluating group motor ability based on a rehabilitation training mat.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention proposes a method for evaluating group motor ability based on a rehabilitation training mat, comprising the following steps:

[0007] Obtain pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0008] Calculate center of gravity matrix data and reference center of gravity matrix data according to pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0009] Calculate the center of gravity stability index based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data;

[0010] Calculate the center of gravity balance index according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data;

[0011] The athletic ability scoring index is calculated according to the center of gravity stability index and the center of gravity balance index, and the group athletic ability is evaluated based on the athletic ability scoring index.

[0012] Optionally, obtaining pressure matrix data of the rehabilitation training mat in the hand area and the foot area includes:

[0013] Flexible pressure sensors are respectively arranged in a dot matrix form in the hand area and the foot area of ​​the rehabilitation training mat;

[0014] The pressure data collected by each flexible pressure sensor is obtained to obtain the pressure matrix data of the rehabilitation training mat in the hand area and the foot area.

[0015] Optionally, the center of gravity matrix data is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0016] Calculate total pressure matrix data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0017] Calculate the center of gravity data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the coordinate data of the pressure collection point;

[0018] The center of gravity matrix data is calculated based on the total pressure matrix data and center of gravity data of each area.

[0019] Optionally, the reference center of gravity matrix data is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0020] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0021] Calculate the binary total pressure matrix data of each area according to the binary pressure matrix data of each area in the hand area and the foot area of ​​the rehabilitation training mat;

[0022] Calculate the binary center of gravity data of each area according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the pressure collection point coordinate data;

[0023] The reference centroid matrix data are calculated based on the binary total pressure matrix data and the binary centroid data of each area.

[0024] Optionally, the center of gravity stability index is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data, including:

[0025] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0026] Calculate the single-point stability characteristics based on the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0027] Calculate regional stability features based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0028] Calculate the center of gravity stability characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area;

[0029] The center of gravity stability index is calculated based on the single point stability characteristics, regional stability characteristics and center of gravity stability characteristics.

[0030] Optionally, the single-point stability feature is calculated based on the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, including:

[0031] Generate a detrended pressure curve of a single pressure collection point within a collection period according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0032] According to the detrended pressure curve of a single pressure acquisition point within the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure acquisition point are calculated;

[0033] The stability characteristics of a single pressure collection point are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure collection point.

[0034] According to the stability characteristics of a single pressure collection point, the single-point stability characteristics are calculated.

[0035] Optionally, the regional stability features are calculated based on the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, including:

[0036] According to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the longitudinal pressure difference of each area within the acquisition period is calculated;

[0037] According to the longitudinal pressure difference of each area during the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each area are calculated;

[0038] The stability characteristics of each region are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each region;

[0039] According to the stability characteristics of each region, the regional stability characteristics are calculated.

[0040] Optionally, the center of gravity stability characteristics are calculated based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0041] According to the centroid matrix data of the rehabilitation training mat in the hand area and the foot area, the 2 norm of each pressure collection point and the origin of the coordinate system is calculated;

[0042] According to the 2 norm of each pressure acquisition point and the origin of the coordinate system, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum are calculated;

[0043] The center of gravity stability characteristics are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum.

[0044] Optionally, the center of gravity balance index is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data, including:

[0045] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0046] Calculate hand-foot balance characteristics based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0047] Calculate the center of gravity balance characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area;

[0048] The center of gravity balance index is calculated based on the hand-foot balance characteristics and the center of gravity balance characteristics.

[0049] In a second aspect, the present invention proposes a group exercise ability assessment device based on a rehabilitation training mat, comprising:

[0050] A data acquisition module is used to obtain pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0051] A center of gravity calculation module, used to calculate center of gravity matrix data and reference center of gravity matrix data according to pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0052] A stability analysis module, used to calculate the center of gravity stability index based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data;

[0053] A balance analysis module, used to calculate a center of gravity balance index based on pressure matrix data of the rehabilitation training mat in the hand area and the foot area, center of gravity matrix data, and reference center of gravity matrix data;

[0054] The group sports ability assessment module is used to calculate the sports ability scoring index according to the center of gravity stability index and the center of gravity balance index, and to conduct group sports ability assessment based on the sports ability scoring index.

[0055] The present invention has the following beneficial effects:

[0056] The present invention uses a flexible pressure sensor array to collect pressure sensor data of users when they are performing group sports, and accurately evaluates the group sports ability of users by extracting the center of gravity stability index and the center of balance index. It has the advantages of low cost, high efficiency, no need for professional debugging, and easy use. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a flowchart of a method for evaluating group motor ability based on a rehabilitation training mat;

[0058] Figure 2 Schematic diagram for setting up the flexible pressure sensor;

[0059] Figure 3 This is a schematic diagram of the structure of a group motor ability assessment device based on a rehabilitation training mat. DETAILED DESCRIPTION

[0060] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0061] like Figure 1 As shown, a group exercise ability assessment method based on a rehabilitation training mat provided by an embodiment of the present invention includes the following steps S1 to S5:

[0062] S1, obtaining pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0063] In an optional embodiment of the present invention, step S1 obtains pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0064] Flexible pressure sensors are respectively arranged in a dot matrix form in the hand area and the foot area of ​​the rehabilitation training mat;

[0065] The pressure data collected by each flexible pressure sensor is obtained to obtain the pressure matrix data of the rehabilitation training mat in the hand area and the foot area.

[0066] In this embodiment, the rehabilitation training pad is composed of a series of precision modules, covering a flexible pressure sensor module, a front-end processing module, a controller module, a memory module, a wireless communication module and a power module. In the flexible pressure sensor module, this embodiment uses, including but not limited to, resistive thin film pressure sensors, which are cleverly arranged on the hands and feet of the evaluation pad, and the measurement area is clearly marked on the front of the pad. The front-end processing module has a built-in resistance measurement switching matrix, which accurately obtains the data of each point of the pressure sensor through scanning technology, and amplifies the signal. As the core of the system, the controller module is responsible for the overall control function. The selected microcontroller chip (such as Stm32H750 of STMicroelectronics) has a powerful computing power, a main frequency of not less than 200MHz, and a memory capacity of not less than 512kB. The microcontroller integrates a rich set of functional modules, including internal analog-to-digital conversion, USB and serial ports, etc., to facilitate the collection of sensor analog data, and realizes wired connection with an external system (such as a personal computer) through a USB interface. The memory module uses a large-capacity flash chip or TF card to store sensor data, and uses a file system (such as Fatfs) for storage space management. In addition, the module also supports data transmission to external systems through the controller, and can realize offline upgrade of system firmware, and complete firmware verification and upgrade operations at startup by monitoring specific folders. The wireless communication module uses low-power Bluetooth module and WiFi module to realize wireless transmission of sensor data to external devices (such as PC, mobile phone, tablet, etc.). The Bluetooth module version is not less than 5.0, and the transmission power is not less than 0dBm; the WiFi module supports the WiFi 6 standard, ensuring low-power data transmission.

[0067] When collecting pressure matrix data in this embodiment, the user does plank support or push-ups on the rehabilitation training mat (taking 1 minute as an example), and pressure data of the limbs (four sensor areas) are collected at the same time, and the sampling rate is not less than 100 Hz.

[0068] S2, calculating center of gravity matrix data and reference center of gravity matrix data according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0069] In an optional embodiment of the present invention, step S2 calculates the center of gravity matrix data according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0070] Calculate total pressure matrix data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0071] Calculate the center of gravity data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the coordinate data of the pressure collection point;

[0072] The center of gravity matrix data is calculated based on the total pressure matrix data and center of gravity data of each area.

[0073] Step S2 calculates reference center of gravity matrix data according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0074] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0075] Calculate the binary total pressure matrix data of each area according to the binary pressure matrix data of each area in the hand area and the foot area of ​​the rehabilitation training mat;

[0076] Calculate the binary center of gravity data of each area according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the pressure collection point coordinate data;

[0077] The reference centroid matrix data are calculated based on the binary total pressure matrix data and the binary centroid data of each area.

[0078] In this embodiment, a rectangular coordinate system is established with the lower left corner of the rehabilitation training mat as the origin, and the four areas are A, B, C, and D. The collected data is divided into time segments, such as 100ms, and the collected data is divided into 600 data segments, and the data of each time segment is averaged.

[0079] Calculate the center of gravity of each area in each time period (taking the left-hand area as an example), C xy is the coordinate value of the point number (x, y), value P xy is the pressure value measured at the point number (x,y), where x∈[1,MH], y∈[1,NH].

[0080] The total pressure matrix data for the current area is calculated as:

[0081]

[0082] The calculation method of the center of gravity data of the current area is:

[0083]

[0084] Calculate the centroid matrix data of the four regions in each time period, specifically:

[0085]

[0086] When calculating the body reference center of gravity matrix data for each time period, when P xy When it is lower than the threshold (such as 10N), let P xy =0; when xy is greater than the threshold, let P xy =1; then calculate the reference centroid matrix data Gr.

[0087] The obtained centroid matrix data G = [G1, G2, ..., G600] and the reference centroid matrix data Gr = [Gr1, Gr2, ..., Gr600].

[0088] S3, calculating the center of gravity stability index according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data;

[0089] In an optional embodiment of the present invention, step S3 calculates the center of gravity stability index according to the pressure matrix data and the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area, including:

[0090] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0091] Calculate the single-point stability characteristics based on the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0092] Calculate regional stability features based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0093] Calculate the center of gravity stability characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area;

[0094] The center of gravity stability index is calculated based on the single point stability characteristics, regional stability characteristics and center of gravity stability characteristics.

[0095] Among them, according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the single-point stability characteristics are calculated, including:

[0096] Generate a detrended pressure curve of a single pressure collection point within a collection period according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0097] According to the detrended pressure curve of a single pressure acquisition point within the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure acquisition point are calculated;

[0098] The stability characteristics of a single pressure collection point are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure collection point.

[0099] According to the stability characteristics of a single pressure collection point, the single-point stability characteristics are calculated.

[0100] Among them, according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the regional stability characteristics are calculated, including:

[0101] According to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the longitudinal pressure difference of each area within the acquisition period is calculated;

[0102] According to the longitudinal pressure difference of each area during the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each area are calculated;

[0103] The stability characteristics of each region are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each region;

[0104] According to the stability characteristics of each region, the regional stability characteristics are calculated.

[0105] Among them, according to the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity stability characteristics are calculated, including:

[0106] According to the centroid matrix data of the rehabilitation training mat in the hand area and the foot area, the 2 norm of each pressure collection point and the origin of the coordinate system is calculated;

[0107] According to the 2 norm of each pressure acquisition point and the origin of the coordinate system, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum are calculated;

[0108] The center of gravity stability characteristics are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum.

[0109] In this embodiment, the single-point stability analysis of the four regions is performed for the entire measurement phase P xy The data points whose mean is greater than the threshold are analyzed as follows:

[0110] First, the possible drift trend of the pressure sensor is removed for each point. The specific methods are as follows: (a) the data is segmented and quadratically fitted to obtain the trend curve, and then the trend curve is subtracted from the original data to obtain the detrended curve; (b) the data is subjected to wavelet decomposition, and after removing the low-frequency components, wavelet reconstruction is performed to obtain the detrended curve; (c) the data is subjected to empirical mode decomposition (EMD), after removing the last two intrinsic mode functions, EMD synthesis is performed to obtain the detrended curve.

[0111] Then, statistical analysis is performed on the data after detrending analysis at each point to calculate (a) standard deviation SD (b) fuzzy entropy FE (c) ratio R of high-frequency energy to low-frequency energy in the power spectrum.

[0112] Then calculate the stability characteristics of each point, specifically:

[0113] s i =α*SD i +β*FE i +γ*R i

[0114] Among them, α, β and γ are weighting coefficients.

[0115] Finally, the single-point stability characteristics of this measurement are calculated, specifically:

[0116]

[0117] Where N is the number of data points exceeding the threshold in the current area.

[0118] In this embodiment, the stability analysis method of each region is as follows:

[0119] First, take the two valid points with the largest and smallest y coordinates (the data points whose Pxy mean value is greater than the threshold value during the entire measurement phase);

[0120] Then subtract the data of these two measurement points;

[0121] Then, the obtained data is statistically analyzed to calculate (a) standard deviation SD (b) fuzzy entropy FE (c) ratio of high-frequency energy to low-frequency energy R of the power spectrum;

[0122] Then calculate the stability characteristics of region A, specifically:

[0123] S A =α*SD A +β*FE A +γ*R A

[0124] Finally, the regional stability characteristics are calculated, specifically:

[0125] S area =(S A +S B +S C +S D ) / 4

[0126] In this embodiment, the stability characteristics of the center of gravity are analyzed by analyzing the center of gravity data obtained in step S2. The analysis method is as follows:

[0127] First, for each coordinate point in the centroid matrix data G, the 2-norm with the origin is obtained, and then statistical analysis is performed to calculate (a) standard deviation SD (b) fuzzy entropy FE (c) ratio R of high-frequency energy to low-frequency energy of the power spectrum;

[0128] Then calculate the center of gravity stability characteristics, specifically:

[0129] S G =α*SD G +β*FE G +γ*R G

[0130] For the above characteristics, the larger the eigenvalue, the worse the stability.

[0131] Finally, the center of gravity stability characteristics are calculated, specifically:

[0132]

[0133] Among them, a is the exponential coefficient, and M1, M2 and M3 are weighting coefficients.

[0134] S4, calculating the center of gravity balance index according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data;

[0135] In an optional embodiment of the present invention, step S4 calculates the center of gravity balance index according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data, including:

[0136] Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0137] Calculate hand-foot balance characteristics based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area;

[0138] Calculate the center of gravity balance characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area;

[0139] The center of gravity balance index is calculated based on the hand-foot balance characteristics and the center of gravity balance characteristics.

[0140] In this embodiment, the method for analyzing the balance of both hands and both feet is as follows:

[0141] First, the effective measurement point data of each area is summed to obtain vectors PA, PB, PC and PD, where PA and PB are hand sensor data, and PC and PD are foot sensor data;

[0142] Then subtract PA and PB data to get D AB;

[0143] Then to D AB The data values ​​are arranged according to size, and D AB Remove the largest 10% and the smallest 10% of the data, average the remaining data, and get the difference B AB , and calculate the difference B in the same way CD ;

[0144] Finally, the hand and foot balance characteristics are calculated, specifically:

[0145] B hf =M1*B AB +M2*B CD

[0146] In this embodiment, the center of gravity balance analysis method is as follows:

[0147] First, find the difference between Gr and G, and then take the 2 norm to get D G .

[0148] Then for D G The data values ​​are arranged according to size, and D G Remove the largest 10% and the smallest 10% of the data, average the remaining data, and get feature B G ;

[0149] Finally, the center of gravity balance characteristics are calculated, specifically:

[0150]

[0151] S5. Calculate the athletic ability scoring index according to the center of gravity stability index and the center of gravity balance index, and conduct group athletic ability assessment based on the athletic ability scoring index.

[0152] In an optional embodiment of the present invention, step S5 calculates the athletic ability scoring index, specifically:

[0153] A=N1*S+N2*B

[0154] Among them, N1 and N2 are weighting coefficients.

[0155] like Figure 3 As shown, an embodiment of the present invention provides a group sports ability assessment device based on a rehabilitation training mat, comprising:

[0156] A data acquisition module is used to obtain pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0157] A center of gravity calculation module, used to calculate center of gravity matrix data and reference center of gravity matrix data according to pressure matrix data of the rehabilitation training mat in the hand area and the foot area;

[0158] A stability analysis module, used to calculate the center of gravity stability index based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data;

[0159] A balance analysis module, used to calculate a center of gravity balance index based on pressure matrix data of the rehabilitation training mat in the hand area and the foot area, center of gravity matrix data, and reference center of gravity matrix data;

[0160] The group sports ability assessment module is used to calculate the sports ability scoring index according to the center of gravity stability index and the center of gravity balance index, and to conduct group sports ability assessment based on the sports ability scoring index.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0165] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for evaluating group motor ability based on a rehabilitation training mat, characterized in that: The following steps are involved: Obtain pressure matrix data of the rehabilitation training mat in the hand area and the foot area; Calculate center of gravity matrix data and reference center of gravity matrix data according to pressure matrix data of the rehabilitation training mat in the hand area and the foot area; Calculate the center of gravity stability index based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data; Calculate the center of gravity balance index according to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data; The athletic ability scoring index is calculated according to the center of gravity stability index and the center of gravity balance index, and the group athletic ability is evaluated based on the athletic ability scoring index.

2. A method for evaluating group athletic ability based on a rehabilitation training mat according to claim 1, characterized in that: Obtain the pressure matrix data of the rehabilitation training mat in the hand area and foot area, including: Flexible pressure sensors are respectively arranged in a dot matrix form in the hand area and the foot area of ​​the rehabilitation training mat; The pressure data collected by each flexible pressure sensor is obtained to obtain the pressure matrix data of the rehabilitation training mat in the hand area and the foot area.

3. A method for evaluating group athletic ability based on a rehabilitation training mat according to claim 1, characterized in that: The center of gravity matrix data is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including: Calculate total pressure matrix data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area; Calculate the center of gravity data of each area according to the pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the coordinate data of the pressure collection point; The center of gravity matrix data is calculated based on the total pressure matrix data and center of gravity data of each area.

4. A method for evaluating group athletic ability based on a rehabilitation training mat according to claim 1, characterized in that: The reference center of gravity matrix data is calculated based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, including: Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area; Calculate the binary total pressure matrix data of each area according to the binary pressure matrix data of each area in the hand area and the foot area of ​​the rehabilitation training mat; Calculate the binary center of gravity data of each area according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area and the pressure collection point coordinate data; The reference centroid matrix data are calculated based on the binary total pressure matrix data and the binary centroid data of each area.

5. The method for evaluating group athletic ability based on a rehabilitation training mat according to claim 1, characterized in that: According to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data, the center of gravity stability index is calculated, including: Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area; Calculate the single-point stability characteristics based on the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area; Calculate regional stability features based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area; Calculate the center of gravity stability characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area; The center of gravity stability index is calculated based on the single point stability characteristics, regional stability characteristics and center of gravity stability characteristics.

6. A method for evaluating group athletic ability based on a rehabilitation training mat according to claim 5, characterized in that: According to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the single-point stability characteristics are calculated, including: Generate a detrended pressure curve of a single pressure collection point within a collection period according to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area; According to the detrended pressure curve of a single pressure acquisition point within the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure acquisition point are calculated; The stability characteristics of a single pressure collection point are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of the single pressure collection point. According to the stability characteristics of a single pressure collection point, the single-point stability characteristics are calculated.

7. A method for evaluating group athletic ability based on a rehabilitation training mat according to claim 5, characterized in that: According to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the regional stability characteristics are calculated, including: According to the binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area, the longitudinal pressure difference of each area within the acquisition period is calculated; According to the longitudinal pressure difference of each area during the acquisition period, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each area are calculated; The stability characteristics of each region are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum of each region; According to the stability characteristics of each region, the regional stability characteristics are calculated.

8. The method for evaluating group athletic ability based on a rehabilitation training mat according to claim 5, characterized in that: According to the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity stability characteristics are calculated, including: According to the centroid matrix data of the rehabilitation training mat in the hand area and the foot area, the 2 norm of each pressure collection point and the origin of the coordinate system is calculated; According to the 2 norm of each pressure acquisition point and the origin of the coordinate system, the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum are calculated; The center of gravity stability characteristics are calculated based on the pressure standard deviation, pressure fuzzy entropy and the ratio of high-frequency energy to low-frequency energy of the power spectrum.

9. The method for evaluating group athletic ability based on a rehabilitation training mat according to claim 1, characterized in that: According to the pressure matrix data of the rehabilitation training mat in the hand area and the foot area, the center of gravity matrix data and the reference center of gravity matrix data, the center of gravity balance index is calculated, including: Binarization processing is performed on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area; Calculate hand-foot balance characteristics based on binary pressure matrix data of each area of ​​the rehabilitation training mat in the hand area and the foot area; Calculate the center of gravity balance characteristics based on the center of gravity matrix data of the rehabilitation training mat in the hand area and the foot area; The center of gravity balance index is calculated based on the hand-foot balance characteristics and the center of gravity balance characteristics.

10. A group exercise ability assessment device based on a rehabilitation training mat, characterized in that: include: A data acquisition module is used to obtain pressure matrix data of the rehabilitation training mat in the hand area and the foot area; A center of gravity calculation module, used to calculate center of gravity matrix data and reference center of gravity matrix data according to pressure matrix data of the rehabilitation training mat in the hand area and the foot area; A stability analysis module, used to calculate the center of gravity stability index based on the pressure matrix data of the rehabilitation training mat in the hand area and the foot area and the center of gravity matrix data; A balance analysis module, used to calculate a center of gravity balance index based on pressure matrix data of the rehabilitation training mat in the hand area and the foot area, center of gravity matrix data, and reference center of gravity matrix data; The group sports ability assessment module is used to calculate the sports ability scoring index according to the center of gravity stability index and the center of gravity balance index, and to conduct group sports ability assessment based on the sports ability scoring index.

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