Balance training coordination management method based on foot data analysis

Abstract

The application discloses a balance training coordination management method based on foot data analysis, and relates to the technical field of intelligent health and sports analysis, which comprises the following steps: using a deep learning model to extract gait features and fatigue indexes in a user foot comprehensive data set, analyzing balance ability and fatigue state of the user, obtaining a quantitative evaluation report, defining a reward function for balance ability improvement according to the quantitative evaluation report, recommending targeted training actions and recovery actions for the user, and obtaining a complete training plan. The application realizes personalized customization of the training plan and ensures that the training intensity matches the actual ability of the user, avoids the problems of overtraining or insufficient training, improves the balance ability of the user, and reduces the fatigue risk.

Description

Technical Field

[0001] This invention relates to the field of intelligent health and exercise analysis technology, and in particular to a balance training coordination management method based on foot data analysis. Background Technology

[0002] With the rapid development of biomechanics and health monitoring technologies, personalized health management methods based on human movement data analysis have gradually become a research hotspot. In the field of balance training, by collecting foot data and combining it with advanced signal processing and machine learning algorithms, it is possible to accurately assess a user's gait characteristics and fatigue state.

[0003] Existing methods for foot data analysis and balance training management have many shortcomings. Conventional gait analysis methods are often limited to simple parameter calculations under static conditions, failing to meet the dynamic balance assessment needs in complex environments, resulting in incomplete and inaccurate assessment results. On the other hand, existing training recommendation programs generally lack personalized design and fail to fully consider the differences in users' specific balance abilities and fatigue states, thus affecting the improvement of training effectiveness. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a balance training coordination management method based on foot data analysis, which solves the problems of incomplete foot data analysis and lack of personalization in balance training programs.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a balance training coordination management method based on foot data analysis, which includes collecting the user's foot data, the foot data including foot movement trajectory data, IMU data, temperature distribution data, foot spatial position information and pressure distribution data, and performing preprocessing;

[0008] By fusing foot spatial location information with IMU data, a three-dimensional foot model is generated. At the same time, the temperature change trend and pressure distribution characteristics of various areas of the user's foot are extracted to obtain a comprehensive dataset of the user's foot.

[0009] Deep learning models are used to extract gait features and fatigue indices from comprehensive user foot datasets, and users' balance and fatigue status are analyzed to obtain quantitative assessment reports.

[0010] Based on the quantitative assessment report, a reward function for improving balance ability is defined, and training and recovery exercises are recommended to the user to obtain a complete training plan.

[0011] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the preprocessing includes smoothing the foot movement trajectory data using a bilateral filtering algorithm;

[0012] Use a Kalman filter to eliminate high-frequency noise in IMU data;

[0013] Wavelet transform is used to eliminate high-frequency noise in temperature distribution data;

[0014] The pressure distribution data were smoothed using the moving average method.

[0015] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the method involves fusing foot spatial position information with IMU data to generate a three-dimensional foot model, including the following steps.

[0016] Align the foot spatial location information with the timestamps of the IMU data;

[0017] The drift of foot spatial position information is corrected by using the dynamic information of IMU data to obtain the corrected foot spatial position information;

[0018] The dynamic information in the IMU data includes angular velocity and acceleration;

[0019] The corrected foot spatial position information is saved in the form of discrete point cloud, and the data points of each discrete point cloud are connected.

[0020] The nearest neighbor algorithm is used to find the neighboring points around the connected data points and generate connecting line segments.

[0021] Triangulation technology is used to convert connecting line segments into triangular patches;

[0022] The Delaunay triangulation algorithm is used to maximize the minimum angle of the triangular facets to generate a preliminary triangular mesh model;

[0023] The Laplacian smoothing method was used to eliminate the spikes in the initial triangular mesh model, and the key areas of the foot were identified and enhanced to obtain the optimized triangular mesh model.

[0024] The key areas of the foot include the arch, heel, toes, and instep;

[0025] By combining the dynamic information from the IMU data with the optimized triangular mesh model, a complete three-dimensional model of the foot is obtained.

[0026] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the method includes: extracting the temperature change trends and pressure distribution characteristics of different areas of the user's foot, comprising the following steps.

[0027] The temperature change trend of each area of ​​the user's foot includes the average temperature of each area of ​​the user's foot and the temperature fluctuation range of each area of ​​the user's foot.

[0028] The pressure distribution characteristics include the maximum pressure value of each area of ​​the user's foot, the location of the pressure center in each area of ​​the user's foot, and the direction of movement of the pressure center in each area of ​​the user's foot.

[0029] The infrared thermal imager is used to collect the temperature of different areas of the user's foot at fixed time intervals and perform statistical analysis to calculate the average temperature of each area of ​​the user's foot.

[0030] The difference between the highest and lowest temperatures in different areas of the user's feet is determined based on the average temperature, and this difference is used as the temperature fluctuation range.

[0031] The pressure sensor pad is used to collect pressure in different areas of the user's foot and to calculate the maximum pressure value in each area of ​​the user's foot.

[0032] The maximum pressure value of each area of ​​the user's foot is represented in two-dimensional coordinate form, and the specific position of the foot pressure center in the horizontal and vertical directions is calculated to determine the position of the pressure center of each area of ​​the user's foot.

[0033] Based on the location of the pressure center in each area of ​​the user's foot, determine the direction of movement of the pressure center in each area of ​​the user's foot.

[0034] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, obtaining a comprehensive foot dataset of the user includes the following steps.

[0035] The temperature change trends and pressure distribution characteristics of different areas of the user's foot are combined into a foot feature vector;

[0036] Z foot feature vectors were randomly selected as cluster centers using the K-Means clustering algorithm;

[0037] Calculate the Euclidean distance between each foot feature vector and the cluster center;

[0038] Based on Euclidean distance, each foot feature vector is assigned to the category of the nearest cluster center, and the mean of all foot feature vectors in each category is calculated as the new cluster center;

[0039] Repeatedly calculate the mean of all foot feature vectors in each category until the new cluster centers no longer change, and obtain the foot feature vectors of similar foot regions;

[0040] We chose support vector machines as the classification model.

[0041] Foot feature vectors of similar foot regions are input into a classification model to assign state labels to each region of the user's foot and compile them into foot assessment results;

[0042] Based on foot assessment results, temperature change trends and pressure distribution data within functionally similar foot regions are summarized to obtain a comprehensive foot dataset for the user. As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the method involves: using a deep learning model to extract gait features and fatigue indicators from the comprehensive foot dataset, including the following steps.

[0043] We selected CNN and LSTM models as a hybrid learning model;

[0044] The spatial location information of the feet is organized into a two-dimensional matrix and input into the CNN;

[0045] The convolution kernels in a CNN are treated as small weight matrices that slide across the two-dimensional matrix of foot spatial location information. Convolution and summation operations are performed on the pixels in each foot spatial location information to generate an activation map.

[0046] Based on the activation map, the feature dimension of foot spatial location information in the form of a two-dimensional matrix is ​​retained in each pooling window by the pooling layer in the CNN, and the spatial feature map with reduced feature dimension is obtained.

[0047] Combine the spatial feature maps after reducing the feature dimension into a one-dimensional vector;

[0048] A small weight matrix is ​​learned using the fully connected layers of a CNN, and the weights of different spatial features are assigned to a one-dimensional vector to obtain gait features.

[0049] The temperature change trend and pressure distribution data are divided into multiple time windows according to fixed time intervals and input into the LSTM.

[0050] For the temperature change trend of each time window, calculate the temperature difference with the previous time window, and determine the temperature rise rate based on the temperature difference;

[0051] Set a normal range for pressure values. For pressure distribution data within each time window, count the number of pressure values ​​that exceed the normal range to obtain the abnormal pressure distribution ratio.

[0052] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the method includes the following steps: analyzing the user's balance ability and fatigue state to obtain a quantitative assessment report.

[0053] Based on the user's 3D foot model and center of gravity movement trajectory, the instantaneous offset of the center of gravity movement trajectory is defined, and the distribution of the center of gravity offset in the center of gravity movement trajectory is statistically analyzed to obtain the maximum allowable offset of the center of gravity.

[0054] The user's balance ability score is calculated based on the user's gait characteristics;

[0055] The user's fatigue status score is calculated based on the rate of temperature rise and the proportion of abnormal pressure distribution.

[0056] The user's balance ability score and fatigue status score are combined to form a quantitative assessment report.

[0057] As a preferred embodiment of the balance training coordination management method based on foot data analysis described in this invention, the method includes: defining a reward function for improved balance ability based on a quantitative assessment report, recommending training and recovery exercises to the user, and obtaining a complete training plan, comprising the following steps.

[0058] The reward value of the current training scheme is calculated based on the reward function for improving balance ability.

[0059] Set the minimum and maximum values ​​of the support surface tilt angle and the basic training time;

[0060] Based on the reward value of the current training program, calculate the initial support surface tilt angle and the initial training time;

[0061] Based on the initial support surface tilt angle and initial training time, combined with the user's gait characteristics, training and recovery movements are recommended to the user.

[0062] During the user's training and recovery movements, the system monitors the user's real-time performance and updates the initial support surface tilt angle and initial training time to obtain a complete training plan.

[0063] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the balance training coordination management method based on foot data analysis as described in the first aspect of the present invention.

[0064] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the balance training coordination management method based on foot data analysis as described in the first aspect of the present invention.

[0065] The beneficial effects of this invention are as follows: by fusing foot spatial position information with IMU data to generate a three-dimensional foot model, and extracting temperature change trends and pressure distribution characteristics, a comprehensive characterization of the user's foot morphology and functional state is achieved, enhancing the predictive ability of the three-dimensional foot model. Based on balance ability scores and fatigue state scores, a reward function for improving balance ability is defined. By dynamically adjusting the support surface tilt angle and training time, targeted training and recovery movements are recommended to the user, realizing personalized customization of the training plan and ensuring that the training intensity matches the user's actual ability, avoiding the problems of overtraining or undertraining, and reducing the risk of fatigue while improving the user's balance ability. Attached Figure Description

[0066] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of the balance training coordination management method based on foot data analysis in Example 1.

[0068] Figure 2 This is a flowchart of obtaining the comprehensive user foot dataset in Example 1. Detailed Implementation

[0069] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0070] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0072] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a balance training coordination management method based on foot data analysis, including the following steps:

[0073] S1. The preprocessing includes smoothing and high-frequency noise elimination.

[0074] Includes the following steps,

[0075] The foot movement trajectory data is smoothed using a bilateral filtering algorithm;

[0076] The bilateral filtering algorithm in this step can not only effectively remove random noise, but also preserve the inflection points and abrupt change points of foot movement trajectory data, ensuring the accuracy of subsequent analysis;

[0077] Use a Kalman filter to eliminate high-frequency noise in IMU data;

[0078] In this step, the Kalman filter processes the noise in the IMU data in real time, which can significantly reduce the impact of high-frequency noise on data quality while preserving the authenticity of the signal.

[0079] Wavelet transform is used to eliminate high-frequency noise in temperature distribution data;

[0080] In this step, the wavelet transform method decomposes the temperature distribution data into different frequency components by multi-scale decomposition, retaining the low-frequency part while suppressing high-frequency noise, thereby obtaining a smoother and more realistic temperature distribution curve.

[0081] The pressure distribution data was smoothed using the moving average method.

[0082] This step, by applying the moving average method to the pressure distribution data, can effectively reduce the impact of short-term fluctuations while preserving the overall trend of the pressure distribution data.

[0083] S2. The foot spatial location information is fused with IMU data to generate a three-dimensional foot model.

[0084] Includes the following steps,

[0085] Align the foot spatial location information with the timestamps of the IMU data;

[0086] This step of aligning timestamps ensures the time matching of spatial location information and dynamic information, and lays the foundation for subsequent use of IMU data to correct the drift of foot spatial position;

[0087] The drift of foot spatial position information is corrected by using dynamic information from IMU data (using angular velocity and acceleration to estimate the displacement increment and posture change of the foot at the current moment). This is divided into horizontal drift and rotational angle drift. The horizontal drift in foot spatial position information is corrected by using displacement increment, and the rotational angle drift in foot spatial position information is adjusted by using posture change. The results of displacement correction and posture correction are combined and applied to foot spatial position information to obtain the corrected foot spatial position information.

[0088] The dynamic information in IMU data includes angular velocity and acceleration (i.e., the rotational motion of the foot);

[0089] The corrected foot spatial position information is saved in the form of discrete point cloud (this form can capture complex details of the foot surface, such as the shape and structure of the heel), and the data points of each discrete point cloud are connected (the purpose of the connection is to transform isolated data points into a geometric model with topological relationships, thereby reflecting the true shape of the foot).

[0090] The nearest neighbor algorithm is used to find the neighboring points around the connected data points and generate connecting line segments.

[0091] First, determine the search radius based on the density of the data points in the connected discrete point cloud. Use this search radius to search the three-dimensional spatial distance between each pair of points, and select points whose three-dimensional spatial distance is less than the search radius as neighbor points. Connect all neighbor points in a one-way direction (i.e., if point A is connected to point B, then do not connect back to point A from point B) to generate connecting line segments.

[0092] Triangulation technology is used to convert connecting line segments into triangular patches;

[0093] First, select three non-collinear data points (i.e., the three points are not on the same straight line) from the discrete point cloud data points and form an initial triangle. Then, gradually expand to the surrounding points based on this initial triangle, find two adjacent triangle sides of unused data points, and connect the discrete point cloud data points and these two triangle sides to obtain a new triangular patch.

[0094] The Delaunay triangulation algorithm is used to maximize the minimum angle of the triangular facets to generate a preliminary triangular mesh model;

[0095] The Delaunay triangulation algorithm is used to divide the triangle into a bounding triangle (which needs to cover the entire point cloud region). The triangle region where each data point in the triangle patch is located is determined. It is checked whether its circumcircle contains the newly inserted point (i.e. the discrete point cloud data point being processed). If it does, it means that the triangle needs to be re-divided. All triangle edges that violate the empty circumcircle condition are marked as invalid and deleted. A new set of triangles is reconstructed using the remaining valid edges and the newly inserted discrete point cloud data points (while removing the bounding triangle) so that the minimum angle of the triangle patch is maximized, thus obtaining the preliminary triangular mesh model.

[0096] The Laplacian smoothing method was used to eliminate the spikes in the initial triangular mesh model, and the key areas of the foot were identified and enhanced to obtain the optimized triangular mesh model.

[0097] Key areas of the foot include the arch (since the arch is a concave curved surface, the area with the largest curvature in the triangular mesh model is the arch), the heel (the circular area with the largest radius is used to locate it), the toes (since the toes are slender convex structures, the area with the shorter length but smaller width in the triangular mesh model is used to identify them), and the instep (the part with the smallest curvature and the highest height is used to determine it).

[0098] Select a target vertex from the initial triangular mesh model (and find all vertices adjacent to the target vertex), adjust the position of the target vertex to be the same as the neighboring points, and repeat this operation iteratively until the spikes of the initial triangular mesh model completely disappear and the desired smoothness is achieved.

[0099] The enhancement process involves increasing the number of vertices in key areas of the foot using interpolation. For the arch, the vertex positions are adjusted to better match the actual concave curve; for the heel, the vertex distribution range is expanded to make it more rounded; and for the toes, the vertices are stretched to make their shape more slender.

[0100] During this step, local constraints need to be applied to ensure that the connection between key areas of the foot and other parts remains smooth (e.g., the boundary between the arch and the instep should remain continuous and without obvious creases).

[0101] By combining the dynamic information from the IMU data with the optimized triangular mesh model, a complete three-dimensional model of the foot is obtained.

[0102] The dynamic information from the IMU data is mapped onto the vertices of the triangular mesh model (when a vertex is located in a critical area of ​​the foot, the distribution of dynamic information needs to be further refined based on the geometric characteristics of that area). The angular velocity and acceleration are used to correct the attitude (when the angular velocity indicates that the foot has rotated, the position of the vertex in the triangular mesh model is adjusted to match the actual rotation angle) and displacement (the instantaneous displacement of the foot is measured based on the acceleration and applied to the overall translation or local deformation of the triangular mesh model). The corrected triangular mesh model is then fused with the dynamic information from the IMU data to obtain a complete three-dimensional model of the foot.

[0103] S3. Extract the temperature change trend and pressure distribution characteristics of different areas of the user's feet.

[0104] Includes the following steps,

[0105] The temperature change trend of each area of ​​the user's foot includes the average temperature of each area of ​​the user's foot (reflecting the overall thermal state of the area; for example, a lower average temperature indicates poor blood circulation or insufficient local blood supply) and the temperature fluctuation range of each area of ​​the user's foot (i.e., the stability of the area temperature).

[0106] Pressure distribution characteristics include the maximum pressure value of each area of ​​the user's foot (when the maximum pressure value of a certain area is abnormally high, it indicates that the area is under excessive force, which can lead to foot damage in the long term, such as plantar fasciitis or bone spurs), the location of the pressure center of each area of ​​the user's foot, and the direction of movement of the pressure center of each area of ​​the user's foot (e.g., in a normal gait, the pressure center usually moves from back to front along the foot).

[0107] The infrared thermal imager is used to collect the temperature of various areas of the user's foot at fixed time intervals (such as once per second) and perform statistical analysis to calculate the average temperature of each area of ​​the user's foot (for example, if the temperature values ​​collected in a certain area within 5 seconds are 36.5℃, 36.6℃, and 36.7℃, then the average temperature of that area is 36.6℃).

[0108] The difference between the highest and lowest temperatures in each area of ​​the user's foot is determined based on the average temperature, and this difference is used as the temperature fluctuation range (for example, if the temperature in a certain area rises from 36.5℃ to 37.0℃ within 5 seconds, then the temperature fluctuation range of that area is 0.5℃).

[0109] The pressure sensor pad collects the pressure of different areas of the user's foot and calculates the maximum pressure value of each area (for example, if the pressure values ​​collected in an area within 5 seconds are 10N, 12N, and 15N, then the maximum pressure value of that area is 15N).

[0110] The maximum pressure values ​​of each area of ​​the user's foot are represented in two-dimensional coordinate form (the maximum pressure value in the horizontal direction is the x-axis, and the maximum pressure value in the vertical direction is the y-axis. Assuming there are four maximum pressure values ​​in each area of ​​the user's foot, namely 10N, 15N, 20N, and 25N, these four maximum pressure values ​​are represented as (1,3), (3,3), (1,1), and (3,1) respectively). The specific positions of the foot pressure center in the horizontal direction (by adding the x-axis coordinates of the four maximum pressure values ​​together and taking the average, the specific position of the foot pressure center in the horizontal direction is 2) and the vertical direction (by adding the y-axis coordinates of the four maximum pressure values ​​together and taking the average, the specific position of the foot pressure center in the vertical direction is 2) are calculated. The position of the pressure center of each area of ​​the user's foot is determined (taking this example as an example, the position of the pressure center of each area of ​​the user's foot is (2,2)).

[0111] Based on the location of the pressure center in each region of the user's foot, determine the direction of movement of the pressure center in each region of the user's foot (e.g., if the pressure center moves from the heel region to the toe region, the direction of movement is from back to front). S4. Obtain the user's comprehensive foot dataset, including the following steps.

[0112] The temperature change trends and pressure distribution characteristics of different areas of the user's foot are combined into a foot feature vector;

[0113] The foot feature vectors generated in this step provide a comprehensive description of the foot state and can be directly used for classification or prediction tasks (such as gait analysis).

[0114] The user's feet are divided into normal, slightly fatigued, and severely fatigued areas (the purpose of which is to accurately assess the user's foot condition), and Z (Z=3) foot feature vectors are randomly selected as cluster centers using the K-Means clustering algorithm;

[0115] The Euclidean distance between each foot feature vector and the cluster center is calculated as follows:

[0116]

[0117] Where x represents the foot feature vector, u represents the cluster center, d(x,u) represents the Euclidean distance between each foot feature vector and the cluster center, and n represents the dimension of the foot feature vector. g u represents the g-th eigenvalue in the foot feature vector. g This represents the g-th feature value of the cluster center;

[0118] Based on Euclidean distance, each foot feature vector is assigned to the category of the nearest cluster center. The mean of all foot feature vectors in each category is calculated and used as the new cluster center. The expression is as follows:

[0119]

[0120] Where Q represents the mean of all foot feature vectors in each category, m represents the total number of foot feature vectors, and x... f This represents the f-th foot feature vector;

[0121] The mean of all foot feature vectors in each category is repeatedly calculated until the new cluster centers no longer change, thus obtaining foot feature vectors of similar foot regions (i.e., all foot feature vectors belonging to the same similar region after multiple iterations; these foot feature vectors are close to each other in the feature space, so they can represent a specific state of the foot (such as normal or fatigued state)).

[0122] This step, through iterative adjustment of cluster centers, can more accurately divide the foot into different regions. This high-precision classification helps to identify which regions are in a normal state and which regions show signs of fatigue, thus providing a reliable basis for subsequent analysis.

[0123] Support Vector Machine (SVM) was chosen as the classification model;

[0124] The reason for choosing support vector machines here is that there are multiple classification tasks in foot state assessment, and SVM can easily extend the classification task to multi-class scenarios through a "one-to-one" or "one-to-many" strategy.

[0125] Foot feature vectors from similar foot regions are input into a classification model. A state label is assigned to each region of the user's foot (the core mechanism of SVM, which finds the optimal hyperplane, is used to analyze the relationship between the input foot feature vectors and fatigue regions, and a state label is assigned to each region, such as a slightly fatigued region → labeled as slightly fatigued). The results are then compiled into a foot assessment result (the state labels of all foot regions are summarized to form a foot state distribution map, such as the arch being normal, the heel being slightly fatigued, and the instep being normal, and the proportion of each region of the entire foot is counted, such as the proportion of severely fatigued regions. The state labels of all foot regions and the proportions of each region are integrated to form the foot assessment result).

[0126] Based on the foot assessment results, the temperature change trends and pressure distribution data of foot areas with the same function (such as the heel and arch, which are both weight-bearing functional areas of the human body) are summarized to obtain the user's comprehensive foot dataset.

[0127] The comprehensive user foot dataset obtained in this step can more accurately identify users' foot problems (such as fatigue and injury risk), providing a more comprehensive basis for balance training and gait analysis in subsequent steps.

[0128] S5. Use a deep learning model to extract gait features and fatigue indicators from the user's comprehensive foot dataset. This includes the following steps:

[0129] Gait characteristics include stride length (horizontal distance between the landing points of the two feet), stride width (lateral distance between the landing points of the two feet), and center of gravity movement trajectory (the spatial path of the body's center of gravity as it changes over time).

[0130] Fatigue indicators include the rate of temperature rise and the proportion of abnormal pressure distribution (i.e., the percentage of time during which the pressure values ​​of different areas of the foot exceed the normal range within a certain period of time).

[0131] We selected CNN and LSTM models as a hybrid learning model;

[0132] The advantage of using these two models as a hybrid learning model is that CNN is responsible for extracting the spatial features of foot data, while LSTM is responsible for capturing the changes of these features over time. By combining the two, the hybrid learning model can utilize both spatial and temporal information to provide more accurate extraction results.

[0133] The spatial location information of the foot is organized into a two-dimensional matrix (rows represent time steps, and columns represent coordinate dimensions, such as the x-axis and y-axis) and input into the CNN;

[0134] The convolution kernels in a CNN are treated as small weight matrices that slide across the two-dimensional matrix of foot spatial location information. Convolution and summation operations are performed on the pixels in each foot spatial location information to generate an activation map.

[0135] The convolution kernel starts from the top left corner of the foot spatial position information in the form of a two-dimensional matrix and moves gradually to the right. When it reaches the end of each row, the convolution kernel moves down one row and repeats the sliding process from left to right.

[0136] At each sliding position, the convolution kernel covers a local region of foot spatial position information in the form of a two-dimensional matrix (its size is the same as the size of the convolution kernel, such as 3×3). Element-wise multiplication is used to multiply each weight value in the convolution kernel with the corresponding value in the covered local region to obtain the product result (for example, if a weight value in the convolution kernel is 0.5 and the corresponding value in the covered local region is 10, then the product result is 5) and then summed.

[0137] Based on the activation map, the feature dimension of foot spatial location information in the form of a two-dimensional matrix is ​​retained in each pooling window by the pooling layer in the CNN, and the spatial feature map with reduced feature dimension is obtained.

[0138] The pooling window of the pooling layer covers a local region of the activation map, selects the maximum value in this local region as the representative value of this region, and stores it in a new two-dimensional matrix (i.e., the spatial feature map after reducing the feature dimension).

[0139] Combine the reduced feature dimension spatial feature map (two-dimensional, its size is height multiplied by width) into a one-dimensional vector (flatten this spatial feature map into a one-dimensional vector in row-major order, for example, the size and shape of the spatial feature is four by four, and its form when unfolded by row is {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16}).

[0140] The fully connected layers of a CNN are used to learn a small weight matrix, and the weights of different spatial features are assigned to a one-dimensional vector (for each feature in the one-dimensional vector, the Pearson correlation coefficient between the feature and the step size is calculated. When the value is closer to 1 or -1, it indicates that the correlation between the feature and the step size is higher. If a feature has a high correlation with the step size, it will be assigned a larger weight value, and vice versa). This yields gait features.

[0141] The temperature change trend and pressure distribution data are divided into multiple time windows at fixed time intervals (1 second) (using an overlapping division, allowing a certain proportion of overlap between adjacent time windows. For example, each window contains 1 second of data, but the step size between windows is 0.5 seconds, so the first window and the second window will have 0.5 seconds of data overlap) and input into LSTM.

[0142] For the temperature change trend of each time window, calculate the temperature difference between it and the previous time window (for each time window, select its highest temperature value and calculate the temperature difference. For example, if the highest temperature value of the current time window is 30 and the highest temperature value of the previous time window is 27, then the temperature difference is 3. If the highest temperature value of the current time window is higher than the highest temperature value of the previous time window, the difference is positive; otherwise, the difference is negative). Determine the temperature rise rate based on the temperature difference (if the temperature difference values ​​of multiple consecutive time windows are positive, it indicates that the temperature is showing an upward trend; otherwise, it shows a downward trend).

[0143] Set a normal range for pressure values ​​(based on the biomechanical characteristics of the human foot under normal gait, for example, the pressure in the arch area is relatively low because it mainly plays a supporting and cushioning role). For the pressure distribution data in each time window, count the number of pressure values ​​that exceed the normal range to obtain the abnormal pressure distribution ratio (for example, if the total number of pressure values ​​in a certain time window is 9 and the number of pressure values ​​that exceed the normal range is 4, then the abnormal ratio is 44%).

[0144] S6. Analyze the user's balance ability and fatigue status to obtain a quantitative assessment report.

[0145] Includes the following steps,

[0146] Based on the user's 3D foot model and center of gravity movement trajectory, the instantaneous offset of the center of gravity movement trajectory is defined, and the distribution of the center of gravity offset in the center of gravity movement trajectory is statistically analyzed to obtain the maximum allowable offset of the center of gravity.

[0147] The instantaneous offset is divided into three ranges: small offset (0-5 cm), medium offset (6-10 cm), and large offset (greater than 10 cm). For each offset range, the number of instantaneous offsets belonging to that range is counted (e.g., 30 small offsets, 15 medium offsets, and 5 large offsets) to form the distribution of the center of gravity offset.

[0148] Analyze the distribution of center of gravity offset (for example, if the proportion of large offsets exceeds the tolerance limit of offset, i.e., 10%, then it is considered that the user will have a balance problem). Based on the analysis results, determine the maximum allowable offset of the center of gravity (if the statistical results show that most offsets are concentrated between 5 and 10 cm and the proportion of large offsets is low, then the maximum allowable offset of the center of gravity can be set to 10 cm).

[0149] Based on the user's gait characteristics, the user's balance ability score is calculated, as expressed by:

[0150]

[0151] Where B represents the user's balance score, reflecting the user's balance stability during walking or standing; T represents the time length (i.e., gait cycle); λ represents the decay coefficient of the time length; C(t) represents the instantaneous offset of the center of gravity trajectory within time t; and D... max Indicates the maximum allowable offset of the center of gravity, where N represents the number of steps, and L represents the number of steps. i W represents the step size of the i-th step. i Let dt represent the step width of the i-th step, and dt represent the time integral.

[0152] Based on the rate of temperature rise and the proportion of abnormal pressure distribution, the user's fatigue status score is calculated, expressed as:

[0153]

[0154] Where F represents the user's fatigue rating, O represents the number of time windows, and ΔE k Let P represent the rate of temperature increase within the k-th time window, and tanh() represent the hyperbolic tangent function, used to convert P... k Normalized to the range [-1, 1], P kV represents the proportion of abnormal pressure distribution within the k-th time window, V represents the normal range of pressure values, and M represents the number of key areas on the user's foot.

[0155] The user's balance score and fatigue score are combined to form a quantitative assessment report;

[0156] In this step, balance ability and fatigue status are two important factors in assessing foot health. Analyzing either factor alone cannot fully reflect the overall condition of the user's feet. Quantitative assessment reports can simultaneously focus on the user's dynamic balance ability and fatigue recovery, thus providing a more comprehensive health assessment.

[0157] S7. Define a reward function for improving balance ability based on the quantitative assessment report, recommend training and recovery exercises to the user, and obtain a complete training plan.

[0158] Includes the following steps,

[0159] Based on the reward function for improving balance ability, the reward value of the current training scheme is calculated, and the expression is:

[0160] R = α × B - β × F;

[0161] Where R represents the reward value of the current training scheme, α represents the weighting coefficient of the balance ability score, and β represents the weighting coefficient of the fatigue state score.

[0162] The reason for defining the reward function for improving balance ability in this step is that balance ability and fatigue state are two related but conflicting goals (i.e., improving balance ability requires increasing training intensity, which leads to increased fatigue, while controlling fatigue requires reducing training intensity, which also limits the improvement of balance ability). This reward function combines these two goals, and by balancing α and β, finds the balance point between balance ability and fatigue state, thereby designing a perfect training plan.

[0163] Set the minimum and maximum values ​​of the support surface tilt angle (the minimum and maximum values ​​are set according to the user's specific muscle endurance) and the basic training time (set according to the user's specific fatigue recovery cycle).

[0164] Based on the reward value of the current training scheme, the initial support surface inclination angle and initial training time are calculated, expressed as follows:

[0165]

[0166] S0 = S1 × e -γ×F ;

[0167] Where θ0 represents the initial inclination angle of the support surface, θmin θ represents the minimum angle of inclination of the supporting surface. max B represents the maximum angle of inclination of the supporting surface. min B represents the minimum score for a user's balance ability. max S0 represents the user's maximum balance score, S1 represents the initial training time, S1 represents the basic training time, and γ represents the decay coefficient for adjusting the user's fatigue score.

[0168] Based on the initial support surface tilt angle and initial training time, combined with the user's gait characteristics, training and recovery movements are recommended to the user.

[0169] The training exercises include: single-leg standing training (the user stands on a stable surface, with hands hanging naturally or gently holding onto a wall, lifts one leg and keeps the body upright, holds this position for at least 10 seconds, and repeats the action with the other leg. If the user can do this easily, the difficulty can be increased, such as standing with eyes closed or standing on a soft mat. Repeat 5 times for each leg, each time for 10-20 seconds, gradually increasing the time to 30 seconds) and dynamic balance gait training (the user walks in a straight line, with the toes of each foot touching the heel of the previous foot. While walking, pay attention to keeping the body upright and add a slight tilt to the support surface, such as the initial tilt angle of the support surface. Walk 10 steps each time, repeat 3-5 times, and gradually increase the walking distance).

[0170] Recovery exercises include: foot stretching and relaxation (the user sits in a chair, places one foot on the thigh of the other leg, gently presses the toes with their hands and stretches backward, holds for 10-15 seconds, repeats the action with the other foot, and repeats 3 times for each foot) and calf muscle massage (the user sits or stands, massages the calf muscles from the ankle upward with their palms and repeats several times, foam roller can be used for assistance, each massage lasts 3-5 minutes, and is repeated 2 times).

[0171] During the user's training and recovery movements, the system monitors the user's real-time performance (if the user's center of gravity shift is small and the quality of the movement is high, it indicates that the user has strong balance ability; if the temperature rise rate is fast or the proportion of abnormal pressure distribution is high, it indicates that the user has fatigue problems) and updates the initial support surface tilt angle (increasing or decreasing by 1° each time) and the initial training time (increasing or decreasing by 5 minutes each time) to obtain a complete training plan.

[0172] For a practical example, the user's real-time performance is evaluated at fixed time intervals (e.g., 5 minutes). When the user's center of gravity shift is less than 10% and the fatigue score is low, the tilt angle is increased from 5° to 6° while the training time remains unchanged.

[0173] Record the updated tilt angle and training time (forming an adjustment history) and provide real-time feedback to the user via voice prompts (e.g., the tilt angle has been adjusted to 6°, please continue training).

[0174] The final training plan is generated based on the adjustment history (including the initial tilt angle, initial training time, adjusted tilt angle and training time, user's balance score, and fatigue score).

[0175] This embodiment also provides a computer device applicable to the balance training coordination management method based on foot data analysis, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the balance training coordination management method based on foot data analysis as proposed in the above embodiment.

[0176] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0177] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the balance training coordination management method based on foot data analysis as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0178] In summary, this invention achieves a comprehensive depiction of the user's foot morphology and functional state by fusing foot spatial location information with IMU data to generate a three-dimensional foot model, extracting temperature change trends and pressure distribution characteristics, and enhancing the predictive ability of the three-dimensional foot model. Based on balance ability scores and fatigue status scores, a reward function for improving balance ability is defined. By dynamically adjusting the support surface tilt angle and training time, targeted training and recovery exercises are recommended to the user, enabling personalized customization of the training plan and ensuring that the training intensity matches the user's actual ability, avoiding overtraining or undertraining, and reducing the risk of fatigue while improving the user's balance ability.

[0179] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A balance training coordination management method based on foot data analysis, characterized in that: include, Collect user's foot data, including foot movement trajectory data, IMU data, temperature distribution data, foot spatial location information, and pressure distribution data, and perform preprocessing. By fusing foot spatial location information with IMU data, a three-dimensional foot model is generated. At the same time, the temperature change trend and pressure distribution characteristics of various areas of the user's foot are extracted to obtain a comprehensive dataset of the user's foot. Deep learning models are used to extract gait features and fatigue indices from comprehensive user foot datasets, and users' balance and fatigue status are analyzed to obtain quantitative assessment reports. Based on the quantitative assessment report, a reward function for improving balance ability is defined, and training and recovery exercises are recommended to the user to obtain a complete training plan. The process of fusing foot spatial location information with IMU data to generate a 3D foot model includes the following steps. Align the foot spatial location information with the timestamps of the IMU data; The drift of foot spatial position information is corrected by using the dynamic information of IMU data to obtain the corrected foot spatial position information; The dynamic information in the IMU data includes angular velocity and acceleration; The corrected foot spatial position information is saved in the form of discrete point cloud, and the data points of each discrete point cloud are connected. The nearest neighbor algorithm is used to find the neighboring points around the connected data points and generate connecting line segments. Triangulation technology is used to convert connecting line segments into triangular patches; The Delaunay triangulation algorithm is used to maximize the minimum angle of the triangular facets to generate a preliminary triangular mesh model; The Laplacian smoothing method was used to eliminate the spikes in the initial triangular mesh model, and the key areas of the foot were identified and enhanced to obtain the optimized triangular mesh model. The key areas of the foot include the arch, heel, toes, and instep; By combining the dynamic information from the IMU data with the optimized triangular mesh model, a complete three-dimensional model of the foot is obtained.

2. The balance training coordination management method based on foot data analysis as described in claim 1, characterized in that: The preprocessing includes smoothing and high-frequency noise elimination.

3. The balance training coordination management method based on foot data analysis as described in claim 1, characterized in that: Extract temperature change trends and pressure distribution characteristics of different areas of the user's feet. Includes the following steps, The temperature change trend of each area of ​​the user's foot includes the average temperature of each area of ​​the user's foot and the temperature fluctuation range of each area of ​​the user's foot. The pressure distribution characteristics include the maximum pressure value of each area of ​​the user's foot, the location of the pressure center in each area of ​​the user's foot, and the direction of movement of the pressure center in each area of ​​the user's foot. The infrared thermal imager is used to collect the temperature of different areas of the user's foot at fixed time intervals and perform statistical analysis to calculate the average temperature of each area of ​​the user's foot. The difference between the highest and lowest temperatures in different areas of the user's feet is determined based on the average temperature, and this difference is used as the temperature fluctuation range. The pressure sensor pad is used to collect pressure in different areas of the user's foot and to calculate the maximum pressure value in each area of ​​the user's foot. The maximum pressure value of each area of ​​the user's foot is represented in two-dimensional coordinate form, and the specific position of the foot pressure center in the horizontal and vertical directions is calculated to determine the position of the pressure center of each area of ​​the user's foot. Based on the location of the pressure center in each area of ​​the user's foot, determine the direction of movement of the pressure center in each area of ​​the user's foot.

4. The balance training coordination management method based on foot data analysis as described in claim 3, characterized in that: Obtaining a comprehensive dataset of a user's feet includes the following steps. The temperature change trends and pressure distribution characteristics of different areas of the user's foot are combined into a foot feature vector; Z foot feature vectors were randomly selected as cluster centers using the K-Means clustering algorithm; Calculate the Euclidean distance between each foot feature vector and the cluster center; Based on Euclidean distance, each foot feature vector is assigned to the category of the nearest cluster center, and the mean of all foot feature vectors in each category is calculated as the new cluster center; Repeatedly calculate the mean of all foot feature vectors in each category until the new cluster centers no longer change, and obtain the foot feature vectors of similar foot regions; We chose support vector machines as the classification model. Foot feature vectors of similar foot regions are input into a classification model to assign state labels to each region of the user's foot and compile them into foot assessment results; Based on the foot assessment results, the temperature change trends and pressure distribution data of the same functional foot areas are summarized to obtain the user's comprehensive foot dataset.

5. The balance training coordination management method based on foot data analysis as described in claim 4, characterized in that: The following steps are included in extracting gait features and fatigue indicators from a comprehensive user foot dataset using a deep learning model. We selected CNN and LSTM models as a hybrid learning model; The spatial location information of the feet is organized into a two-dimensional matrix and input into the CNN; The convolution kernels in a CNN are treated as small weight matrices that slide across the two-dimensional matrix of foot spatial location information. Convolution and summation operations are performed on the pixels in each foot spatial location information to generate an activation map. Based on the activation map, the feature dimension of foot spatial location information in the form of a two-dimensional matrix is ​​retained in each pooling window by the pooling layer in the CNN, and the spatial feature map with reduced feature dimension is obtained. Combine the spatial feature maps after reducing the feature dimension into a one-dimensional vector; A small weight matrix is ​​learned using the fully connected layers of a CNN, and the weights of different spatial features are assigned to a one-dimensional vector to obtain gait features. The temperature change trend and pressure distribution data are divided into multiple time windows according to fixed time intervals and input into the LSTM. For the temperature change trend of each time window, calculate the temperature difference with the previous time window, and determine the temperature rise rate based on the temperature difference; Set a normal range for pressure values. For pressure distribution data within each time window, count the number of pressure values ​​that exceed the normal range to obtain the abnormal pressure distribution ratio.

6. The balance training coordination management method based on foot data analysis as described in claim 5, characterized in that: Analyzing users' balance ability and fatigue status to obtain a quantitative assessment report includes the following steps: Based on the user's 3D foot model and center of gravity movement trajectory, the instantaneous offset of the center of gravity movement trajectory is defined, and the distribution of the center of gravity offset in the center of gravity movement trajectory is statistically analyzed to obtain the maximum allowable offset of the center of gravity. The user's balance ability score is calculated based on the user's gait characteristics; The user's fatigue status score is calculated based on the rate of temperature rise and the proportion of abnormal pressure distribution. The user's balance ability score and fatigue status score are combined to form a quantitative assessment report.

7. The balance training coordination management method based on foot data analysis as described in claim 6, characterized in that: Based on the quantitative assessment report, a reward function for improved balance ability is defined, and training and recovery exercises are recommended to the user to obtain a complete training plan. Includes the following steps, The reward value of the current training scheme is calculated based on the reward function for improving balance ability. Set the minimum and maximum values ​​of the support surface tilt angle and the basic training time; Based on the reward value of the current training program, calculate the initial support surface tilt angle and the initial training time; Based on the initial support surface tilt angle and initial training time, combined with the user's gait characteristics, training and recovery movements are recommended to the user. During the user's training and recovery movements, the system monitors the user's real-time performance and updates the initial support surface tilt angle and initial training time to obtain a complete training plan.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the balance training coordination management method based on foot data analysis as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the balance training coordination management method based on foot data analysis as described in any one of claims 1 to 7.

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