Multi-dimensional motion data analysis and management system
The multi-dimensional motion data analysis system solves the problems of misaligned data acquisition, closed systems, and delayed feedback in existing technologies, and achieves high-precision, real-time motion data processing and personalized feedback, thereby improving the effectiveness of exercise training and rehabilitation guidance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-10
AI Technical Summary
Existing sports data analysis systems suffer from problems such as misalignment of data acquisition time and space, system closure, shallow analysis models, and delayed user feedback in multi-dimensional data processing, making it difficult to achieve efficient integration, in-depth mining, and real-time response.
A multi-dimensional motion data analysis and management system is adopted, including a data acquisition and synchronization module, a data fusion and feature extraction module, a dynamic behavior analysis and modeling module, and a real-time feedback and adaptive adjustment module. Through timestamp alignment, coordinate system unification, deep neural network models, and reinforcement learning mechanisms, real-time data processing and personalized feedback are achieved.
It improves the precision and accuracy of data fusion, meets the immediate needs of high-intensity training and injury risk warning, and has openness and scalability, enabling personalized and precise sports management.
Smart Images
Figure CN121839115A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of data processing technology, specifically relating to a multi-dimensional motion data analysis and management system. Background Technology
[0002] With the continuous advancement of sports science and health monitoring technology, the collection and analysis of sports data has become an important means to improve training effectiveness, prevent sports injuries, and optimize health management. Modern sports scenarios involve increasingly diverse data types, covering multiple dimensions such as positional trajectory, acceleration, angular velocity, heart rate, electromyography, and video images. These multi-source, heterogeneous data collectively constitute a comprehensive characterization of an individual's athletic status. However, current mainstream sports data analysis systems still face significant limitations in processing such complex data, struggling to achieve efficient integration and in-depth analysis, thus hindering the improvement of personalized feedback and intelligent decision-making capabilities.
[0003] Among these, the collaborative management and dynamic analysis of multi-dimensional motion data has become a key technological direction. This direction aims to construct a high-precision, low-latency motion behavior understanding model by synchronously collecting, standardizing, and analyzing heterogeneous data from wearable devices, environmental sensors, and vision systems through a unified architecture. Its core objective is to break through the perception boundaries of single-modal data, achieving a leap from "data recording" to "cognitive reasoning," and providing scientific support for competitive training, rehabilitation guidance, and public fitness.
[0004] Existing technologies for processing multi-dimensional motion data generally suffer from the following problems: First, data acquisition lacks a spatiotemporal alignment mechanism, resulting in significant differences in timestamps, coordinate systems, and sampling frequencies among data from different sources, leading to distortion in fusion analysis. Second, the closed system architecture makes it difficult to be compatible with new sensors or expand into new analytical dimensions, limiting the adaptability of application scenarios. Third, the analysis models are mostly based on static rules or shallow statistics, failing to capture the nonlinear dynamic characteristics in complex motion patterns, affecting the accuracy of evaluation results. Finally, user feedback mechanisms are lagging, with analysis results often presented in offline reports, lacking real-time interaction and adaptive adjustment capabilities. These shortcomings make it difficult for existing systems to meet the precise and intelligent management needs in key scenarios such as high-intensity training monitoring and injury risk warning. Therefore, a multi-dimensional motion data analysis and management system with high compatibility, strong fusion capabilities, and real-time response characteristics is urgently needed. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-dimensional motion data analysis and management system that can effectively solve the problems in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: In one aspect, a multi-dimensional motion data analysis and management system, comprising the following components:
[0007] The system comprises the following modules: a data acquisition and synchronization module, used to acquire raw motion data in real time from multiple heterogeneous data sources, and preprocess the raw motion data by timestamp alignment and coordinate system unification to generate a standardized multi-dimensional motion data stream; a data fusion and feature extraction module, used to receive the standardized multi-dimensional motion data stream, fuse data from different dimensions through a spatiotemporal registration algorithm, and extract key feature vectors representing the motion state from the fused data based on preset feature extraction rules; a dynamic behavior analysis and modeling module, used to receive the key feature vectors, use a deep neural network model to perform nonlinear dynamic feature modeling on the key feature vectors, and generate dynamic analysis results of motion behavior; a real-time feedback and adaptive adjustment module, used to receive the dynamic analysis results, generate real-time motion state evaluation feedback according to preset evaluation thresholds, and dynamically adjust data acquisition parameters or model analysis parameters based on the feedback results; and a system interface and expansion module, used to provide standardized data interfaces and protocols to support plug-and-play access to new sensors or analysis dimensions.
[0008] Preferably, the data acquisition and synchronization module includes a data source interface unit, a clock synchronization unit, and a coordinate transformation unit. The data source interface unit is configured to support multiple communication protocols, including Bluetooth 5.0, Wi-Fi 6, and ZigBee 3.0, and has protocol adaptive recognition capabilities. The clock synchronization unit employs a hardware timestamp mechanism based on the IEEE 1588 precision time protocol to achieve microsecond-level time synchronization accuracy. The coordinate transformation unit incorporates a rotation matrix and quaternion transformation algorithm to uniformly transform local coordinate system data from different sensors to the global world coordinate system.
[0009] Furthermore, the data fusion and feature extraction module employs a hybrid filtering algorithm based on Kalman filtering and particle filtering for data fusion. The feature extraction rules include the extraction of time-domain features, frequency-domain features, and time-frequency-domain features. Time-domain features include mean, variance, peak value, and zero-crossing rate; frequency-domain features are obtained by calculating the main frequency components and their amplitudes through Fast Fourier Transform; and time-frequency-domain features are extracted by extracting wavelet coefficient energy at a specific scale through wavelet transform.
[0010] Furthermore, the dynamic behavior analysis and modeling module employs a hybrid architecture of Long Short-Term Memory (LSTM) and Graph Convolutional Networks (GCNs) in its deep neural network model. The LSM is used to capture temporal dependencies in motion data, with 128 hidden layer units. The GCN is used to model the spatial topological relationships of body joints; its graph structure is built based on the connections within the human skeleton, with adjacency matrix element a.ij This represents the connection strength between joints i and j.
[0011] Preferably, the real-time feedback and adaptive adjustment module includes an evaluation unit and a parameter adjustment unit. The evaluation unit compares the dynamically analyzed results with a preset threshold, which is dynamically set based on the exercise type and individual physiological baseline. A real-time alarm is triggered when the analyzed results exceed the threshold range. The parameter adjustment unit dynamically optimizes the sampling frequency of data acquisition or the number of dimensions for feature extraction using a reinforcement learning-based policy network, based on historical data and real-time feedback.
[0012] On the other hand, a multi-dimensional motion data analysis and management method, the specific steps of which are as follows:
[0013] Step S110: Simultaneously collect raw motion data including position trajectory, acceleration, angular velocity, heart rate, electromyography and video images by deploying multiple sensor devices in the moving individual and environment;
[0014] Step S120: Perform time alignment and coordinate system unification processing on the collected raw motion data to eliminate data deviations caused by differences in sampling frequency and inconsistencies in coordinate systems, and generate a standardized multi-dimensional motion data sequence.
[0015] Step S130: Input the standardized multi-dimensional motion data sequence into the data fusion model, perform multi-source data fusion using an algorithm based on a weighted fusion strategy, and extract key feature sets for describing motion patterns from the fused data based on a preset feature dictionary.
[0016] Step S140: Analyze the key feature set using a pre-trained deep behavior analysis model to identify abnormal states, technical movement standardization, and fatigue levels in the movement pattern, and output quantitative movement behavior evaluation indicators.
[0017] Step S150: Based on the motion behavior evaluation index, generate real-time feedback information containing text, graphics, or tactile signals, and adaptively adjust subsequent data acquisition strategies or model analysis parameters based on the feedback effect.
[0018] Preferably, in step S120, time alignment is achieved by using linear interpolation to unify data with different sampling rates to the highest sampling rate, and coordinate system unification is achieved by solving the relative pose transformation matrix between sensors.
[0019] Furthermore, the data fusion model in step S130 adopts a fusion algorithm based on DS evidence theory, and its basic probability allocation function is defined as: Where K represents the conflict factor. The feature dictionary is constructed by reducing the dimensionality of historical motion data through principal component analysis, retaining principal components with a variance contribution rate of over 95% as feature bases.
[0020] Furthermore, the deep behavioral analysis model in step S140 undergoes supervised training using a large amount of labeled motion data. Its loss function is defined as the weighted sum of the cross-entropy loss and mean squared error between the predicted output and the true label. The model output includes a sports technique score, an injury risk index, and an estimated energy expenditure.
[0021] The beneficial effects of this invention are:
[0022] Through a precise time synchronization and coordinate unification mechanism, the mismatch problem of multi-source heterogeneous motion data in the spatiotemporal dimension is effectively solved, significantly improving the accuracy and reliability of data fusion.
[0023] The deep neural network model with a hybrid architecture can fully capture the nonlinear dynamic features and spatial topological relationships in complex motion patterns, thereby improving the accuracy of motion behavior analysis by about 25%.
[0024] It achieves real-time processing of the entire process from data acquisition to feedback output, with feedback latency controlled within 200 milliseconds, meeting the stringent requirements for immediacy in high-intensity training and injury risk warning scenarios.
[0025] The system architecture is highly open and scalable. Through standardized interface design, it can seamlessly integrate new sensing devices and analysis algorithms to adapt to the ever-evolving needs of sports science applications.
[0026] An adaptive adjustment mechanism based on reinforcement learning was introduced, enabling the system to dynamically optimize its parameters according to individual differences and real-time status, thus achieving personalized and precise exercise management and guidance. Attached Figure Description
[0027] Figure 1 This is a schematic diagram of the overall technical architecture of the multi-dimensional motion data analysis and management system proposed in this invention;
[0028] Figure 2 This is a schematic diagram of the core principle framework of the data fusion and feature extraction module in this invention;
[0029] Figure 3 This is a logical flow diagram of the dynamic behavior analysis and modeling module in this invention. Detailed Implementation
[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.
[0031] Example 1
[0032] At professional athlete physical training centers, multi-dimensional sports data analysis and management systems conduct comprehensive data collection and processing for high-intensity physical training scenarios. (See also...) Figure 1 This system comprises a data acquisition and synchronization module, a data fusion and feature extraction module, a dynamic behavior analysis and modeling module, a real-time feedback and adaptive adjustment module, and a system interface and expansion module. The data acquisition and synchronization module synchronously acquires raw motion data, including 3D spatial trajectory, triaxial acceleration, triaxial angular velocity, real-time heart rate, surface electromyography signals, and high-definition video images, through multiple sensor devices deployed on key parts of the athlete's body and in the training environment. The data source interface unit is configured to simultaneously support Bluetooth 5.0, Wi-Fi 6, and ZigBee 3.0 communication protocols. When a new sensor is connected, this unit automatically selects the optimal communication method through protocol adaptive recognition. The clock synchronization unit adopts a hardware timestamp mechanism based on the IEEE 1588 precision time protocol, achieving microsecond-level time synchronization accuracy within the sensor network and ensuring strict alignment of data streams with different sampling frequencies in the time dimension. The coordinate transformation unit has a built-in rotation matrix and quaternion transformation algorithm to uniformly transform the local coordinate system data from the inertial measurement unit, the global coordinate system data from the optical motion capture system, and the image coordinate system data obtained from video analysis to the global world coordinate system with the center of the training field as the origin.
[0033] The specific process for standardizing raw motion data in the data acquisition and synchronization module includes: time alignment processing uses linear interpolation to uniformly interpolate the 1Hz sampling data from the heart rate sensor, the 1000Hz sampling data from the electromyography sensor, and the 200Hz sampling data from the motion capture system to the highest sampling rate of 1000Hz; coordinate system unification is achieved by solving the relative pose transformation matrix of each sensor coordinate system relative to the world coordinate system. This transformation matrix consists of a rotation matrix and a translation vector, where the rotation matrix is calculated using a quaternion transformation algorithm, and the four components of the quaternion represent the rotation axis and rotation angle, respectively. The standardized multi-dimensional motion data sequence generated after preprocessing contains 128 data fields, including timestamps, spatial coordinates, and physiological parameters. Each field undergoes data validity verification, and outliers exceeding reasonable physical ranges are removed.
[0034] After receiving the standardized multi-dimensional motion data sequence, the data fusion and feature extraction module refers to... Figure 2A hybrid filtering algorithm based on Kalman filtering and particle filtering is used for data fusion. The Kalman filtering part is used to handle linear systems with Gaussian noise. Its state equation describes the dynamic characteristics of the moving system, and the observation equation establishes the relationship between the state variables and the sensor observations. The particle filtering part approximates the posterior probability distribution of the system state through a set of random samples, effectively handling nonlinear non-Gaussian problems. The outputs of the two filtering algorithms are integrated through a weighted fusion strategy, with the weights dynamically adjusted according to sensor accuracy and real-time signal-to-noise ratio. The fused data enters the feature extraction stage, with feature extraction rules clearly divided into three categories: time-domain features, frequency-domain features, and time-frequency-domain features. Time-domain feature calculation includes the arithmetic mean, variance, peak value, and zero-crossing rate of the data sequence. Peak value detection uses the local extremum method, and the zero-crossing rate is the number of times the signal crosses the zero axis. Frequency-domain features are calculated through fast Fourier transform, extracting the main frequency components and their corresponding amplitudes, with a frequency resolution set to 0.5Hz. Time-frequency-domain features are extracted through wavelet transform, selecting the db4 wavelet basis function, and calculating the wavelet coefficient energy at a specific scale within the scale range of 1 to 64.
[0035] The data fusion and feature extraction module employs a fusion algorithm based on DS evidence theory to fuse multi-source data. Its basic probability allocation function is defined as: Where K represents the conflict factor, and the calculation formula is: The feature dictionary is constructed by reducing the dimensionality of historical motion data through principal component analysis. It extracts 32 principal components with a variance contribution rate of more than 95% from the original 256-dimensional feature space as feature bases. These feature bases constitute the standard feature set for subsequent analysis.
[0036] After receiving the key feature vectors, the dynamic behavior parsing and modeling module refers to... Figure 3 This paper utilizes a deep neural network model for nonlinear dynamic feature modeling. The model employs a hybrid architecture of Long Short-Term Memory (LSTM) and Graph Convolutional Networks (GCNNs). The LSM portion has 128 hidden layers, with sigmoid activation functions for the input, forget, and output gates, and tanh function for cell state updates. The network is trained using backpropagation, effectively capturing long-term dependencies across time steps in motion data. The GCNN constructs a graph structure based on the human skeleton's connectivity, using 25 key joints as nodes and physiological connections between joints as edges. Adjacency matrix elements represent the connection strength between joints, with values determined based on the actual physiological distance and motion coupling degree between joints. Graph convolution operations are performed in the spectral domain, using Chebyshev polynomial approximation of the graph Fourier transform to achieve multi-layered abstraction and aggregation of node features.
[0037] The dynamic behavior analysis and modeling module undergoes supervised training using a large amount of labeled motion data. The training dataset contains 10,000 complete motion cycle data sets, each containing a complete action sequence from start to finish. The loss function is defined as the weighted sum of the cross-entropy loss and mean squared error between the predicted output and the true label. The weighting coefficients are adjusted according to the specific task; the assessment of technical movement standardization focuses on cross-entropy loss, while the assessment of fatigue level focuses on mean squared error. The model output includes three quantitative indicators: a sports technique score, an injury risk index, and an energy consumption estimate. The sports technique score ranges from 0 to 100, the injury risk index is a continuous value from 0 to 1, and the energy consumption estimate is in kilocalories per minute.
[0038] After receiving the dynamic analysis results, the real-time feedback and adaptive adjustment module compares the exercise technique score, injury risk index, and preset thresholds with the evaluation unit. The thresholds are dynamically set based on the exercise type and individual physiological baseline. For strength training exercises, the technique score threshold is set to 80 points; for endurance training exercises, the threshold is set to 75 points; and the injury risk index threshold is uniformly set to 0.7. When the analysis result exceeds the threshold range, a real-time alarm mechanism is triggered. The alarm level is divided into three levels based on the degree of exceedance, each corresponding to a different response strategy. The parameter adjustment unit dynamically optimizes system parameters using a reinforcement learning-based policy network based on historical data and real-time feedback. The policy network takes the current system state as input and outputs adjustments to the sampling frequency of data acquisition or the number of dimensions for feature extraction. The reward function comprehensively considers three factors: analysis accuracy, system latency, and energy consumption.
[0039] The real-time feedback and adaptive adjustment module generates multi-dimensional feedback information including text, graphics, and tactile signals. Text feedback is presented through a head-mounted display, providing specific suggestions for improving technical movements; graphic feedback is displayed on a screen in the training area, reproducing the differences between the athlete's actual movements and the standard movements in the form of 3D animation; tactile signals are transmitted through wearable vibration motors, generating specific vibration patterns on the corresponding body parts when deviations occur in technical movements. The latency of the entire feedback loop is strictly controlled within 200 milliseconds to ensure that athletes can receive and respond to feedback information in a timely manner during movement execution.
[0040] The system interface and expansion modules provide standardized data interfaces and communication protocols, supporting plug-and-play access for new sensors or analytical dimensions. The data interface uses a unified JSON format to define the data transmission structure, including necessary fields such as device identifier, data type, timestamp, and data value. The communication protocol supports both TCP / IP and UDP transmission modes, automatically selecting the appropriate mode based on real-time data requirements. TCP is used for motion trajectory data to ensure reliability, while UDP is used for physiological parameter data to reduce latency. When a new sensor is connected, the system automatically identifies the sensor type, data format, and communication requirements through the device description file, completing integration without manual configuration.
[0041] Example 2
[0042] In rehabilitation medical institutions, a multi-dimensional motion data analysis and management system provides personalized monitoring and guidance for the rehabilitation training process of patients with motor dysfunction. The data acquisition and synchronization module employs a low-load wearable sensor combination, including a flexible strain sensor, an inertial measurement unit, and a heart rate monitor, specifically adapted to the physiological characteristics and comfort needs of patients with limited mobility. The clock synchronization unit, based on the standard IEEE 1588 protocol, adds an adaptive adjustment mechanism for low-speed movements, automatically reducing synchronization accuracy requirements to minimize system energy consumption when timing fluctuations exceed a set threshold. The coordinate transformation unit is specially optimized for common non-standard patient positions, accurately processing motion data from supine, sitting, and other non-upright positions through an extended coordinate system transformation algorithm.
[0043] The data fusion and feature extraction module, based on standard feature extraction rules, adds specific feature dimensions for rehabilitation training, including joint range of motion, muscle synergistic contraction index, and motion symmetry indicators. Time-domain feature calculation pays particular attention to the statistical characteristics of motion amplitude and velocity, frequency-domain analysis focuses on monitoring tremor frequency components, and time-frequency domain analysis extracts motion stability features through wavelet transform. The feature dictionary has been expanded based on professional knowledge in rehabilitation medicine, adding 20 new feature dimensions directly related to motor function assessment; all features have been confirmed by rehabilitation physicians to be clinically significant.
[0044] The deep neural network model for the dynamic behavior analysis and modeling module has been specifically optimized for the characteristics of rehabilitation training, based on a general architecture. The number of hidden layer units in the Long Short-Term Memory network has been adjusted to 64 to accommodate the relatively simple movement patterns in rehabilitation training; the graph convolutional network is built based on a simplified human skeletal model, containing only 12 key joints, and the adjacency matrix has been redefined according to the joint connections that are of primary concern in rehabilitation training. In addition to basic movement assessment indicators, the model output now includes two specialized outputs: rehabilitation progress score and compensatory movement recognition, providing data support for therapists to develop personalized rehabilitation plans.
[0045] The real-time feedback and adaptive adjustment module adjusts the assessment thresholds and feedback strategies based on the specific needs of rehabilitation patients. The technical score threshold is dynamically adjusted according to the patient's initial ability and rehabilitation stage, with the initial threshold set at 60 points, gradually increasing to 80 points as rehabilitation progresses. The injury risk index threshold is strictly controlled below 0.5 to ensure training safety. Feedback information avoids technical jargon and emphasizes encouraging language; graphical feedback uses simplified two-dimensional diagrams, and the intensity of tactile feedback is individualized according to the patient's sensitivity. The parameter adjustment unit optimizes the update frequency of the reinforcement learning strategy network, changing from real-time adjustment to daily scheduled updates, ensuring personalized adaptation while avoiding instability caused by over-adjustment, considering the long-term nature of rehabilitation training.
[0046] Building upon the standard interface, the system interface and expansion modules add a dedicated data channel for communication with medical information systems, supporting the direct writing of motion analysis results into electronic medical records. The data interface expands upon the fields required by medical data exchange standards, including patient identification, treatment item codes, and assessment time. The communication protocol adds a compatibility layer with the HL7 medical information exchange standard, ensuring seamless integration with existing hospital information systems. When medical-specific sensors are connected, the system automatically identifies the device type and activates appropriate data security and privacy protection mechanisms to meet the specific requirements of medical scenarios.
[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
[0048] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.
Claims
1. A multi-dimensional motion data analysis and management system, characterized in that, The system includes the following components: The data acquisition and synchronization module is used to acquire raw motion data from multiple heterogeneous data sources in real time, and perform timestamp alignment and coordinate system unification preprocessing on the raw motion data to generate a standardized multi-dimensional motion data stream. The data fusion and feature extraction module is used to receive the standardized multi-dimensional motion data stream, fuse the data of different dimensions through a spatiotemporal registration algorithm, and extract key feature vectors representing the motion state from the fused data based on preset feature extraction rules; the dynamic behavior analysis and modeling module is used to receive the key feature vectors, perform nonlinear dynamic feature modeling on the key feature vectors using a deep neural network model, and generate dynamic analysis results of motion behavior. The real-time feedback and adaptive adjustment module is used to receive the dynamic analysis results, generate real-time motion state evaluation feedback according to the preset evaluation threshold, and dynamically adjust the data acquisition parameters or model analysis parameters based on the feedback results; the system interface and expansion module is used to provide standardized data interfaces and protocols to support plug-and-play access to new sensors or analysis dimensions.
2. The multi-dimensional motion data analysis and management system according to claim 1, characterized in that, The data acquisition and synchronization module includes a data source interface unit, a clock synchronization unit, and a coordinate transformation unit. The data source interface unit is configured to support multiple communication protocols, including Bluetooth 5.0, Wi-Fi 6, and ZigBee 3.0, and has protocol adaptive recognition capabilities. The clock synchronization unit adopts a hardware timestamp mechanism based on the IEEE 1588 precision time protocol to achieve microsecond-level time synchronization accuracy. The coordinate transformation unit incorporates a rotation matrix and quaternion transformation algorithm to uniformly transform local coordinate system data from different sensors to the global world coordinate system.
3. The multi-dimensional motion data analysis and management system according to claim 1, characterized in that, The data fusion and feature extraction module uses a hybrid filtering algorithm based on Kalman filtering and particle filtering for data fusion; the feature extraction rules include the extraction of time-domain features, frequency-domain features, and time-frequency features; the time-domain features include mean, variance, peak value, and zero-crossing rate; the frequency-domain features are obtained by calculating the main frequency components and their amplitudes through fast Fourier transform; Time-frequency domain features are used to extract wavelet coefficient energy at a specific scale through wavelet transform.
4. The multi-dimensional motion data analysis and management system according to claim 1, characterized in that, The dynamic behavior analysis and modeling module employs a hybrid architecture of Long Short-Term Memory (LSTM) and Graph Convolutional Networks (GCNs) in its deep neural network model. The LSM captures temporal dependencies in motion data, with 128 hidden layer units. The GCN models the spatial topological relationships of body joints, its graph structure built upon the connections of the human skeleton, with adjacency matrix element a. ij This represents the connection strength between joints i and j.
5. The multi-dimensional motion data analysis and management system according to claim 1, characterized in that, The real-time feedback and adaptive adjustment module includes an evaluation unit and a parameter adjustment unit. The evaluation unit compares the dynamic analysis results with a preset threshold, which is dynamically set according to the type of exercise and the individual's physiological baseline. When the analysis results exceed the threshold range, a real-time alarm is triggered. The parameter adjustment unit dynamically optimizes the sampling frequency of data acquisition or the number of dimensions of feature extraction using a reinforcement learning-based policy network based on historical data and real-time feedback.
6. The multi-dimensional motion data analysis and management system according to claim 3, characterized in that, The data fusion and feature extraction module employs a fusion algorithm based on DS evidence theory to fuse multi-source data. Its basic probability allocation function is defined as: Where K represents the conflict factor; The feature dictionary is constructed by reducing the dimensionality of historical motion data through principal component analysis, and the principal components with a variance contribution rate of more than 95% are retained as feature bases.
7. The multi-dimensional motion data analysis and management system according to claim 4, characterized in that, The dynamic behavior analysis and modeling module is trained in a supervised manner using a large amount of labeled motion data. Its loss function is defined as the weighted sum of the cross-entropy loss and mean square error between the predicted output and the true label. The model output includes three quantitative indicators: sports technique score, injury risk index and energy consumption estimate.
8. A method for multi-dimensional motion data analysis and management, characterized in that, The method includes the following steps: Simultaneously collecting raw motion data, including position trajectory, acceleration, angular velocity, heart rate, electromyography, and video images, using multiple sensor devices deployed on the individual and in the environment; performing time alignment and coordinate system unification processing on the collected raw motion data to eliminate data deviations caused by differences in sampling frequency and coordinate system inconsistencies, generating a standardized multi-dimensional motion data sequence; inputting the standardized multi-dimensional motion data sequence into a data fusion model, using an algorithm based on a weighted fusion strategy to perform multi-source data fusion, and extracting a key feature set for describing the motion pattern from the fused data based on a preset feature dictionary; analyzing the key feature set using a pre-trained deep behavior analysis model to identify abnormal states, technical movement standardization, and fatigue levels in the motion pattern, and outputting quantified motion behavior evaluation indicators; generating real-time feedback information including text, graphics, or tactile signals based on the motion behavior evaluation indicators, and adaptively adjusting subsequent data acquisition strategies or model analysis parameters based on the feedback effect.
9. The multi-dimensional motion data analysis and management method according to claim 8, characterized in that, The time alignment uses a linear interpolation method to unify data from different sampling rates to the highest sampling rate, and coordinate system unification is achieved by solving the relative pose transformation matrix between sensors; the data fusion model adopts a fusion algorithm based on DS evidence theory, and its basic probability allocation function is defined as: Where K represents the conflict factor.
10. The multi-dimensional motion data analysis and management method according to claim 8, characterized in that, The deep behavioral analysis model is trained in a supervised manner using a large amount of labeled motion data. Its loss function is defined as the weighted sum of the cross-entropy loss and mean square error between the predicted output and the true label. The model output includes a sports technique score, an injury risk index, and an energy consumption estimate. The real-time feedback information is presented in the form of text, graphics, and tactile signals through a head-mounted display, a training ground display screen, and a wearable vibration motor, respectively, with the delay of the entire feedback loop controlled within 200 milliseconds.