182 results about "Gait analysis" patented technology

The invention relates to the technical field of human body posture detection and gait analysis, and discloses a portable posture detection and gait analysis method and system. The method comprises the steps that a hardware system is built, the hardware system comprises an embedded visual module, a data processing module and an interaction module, and human body motion video streams are collected through the embedded visual module; performing dynamic foreground extraction on the acquired video stream by adopting a background difference method and a self-adaptive illumination compensation algorithm, and outputting an illumination compensation image; according to the output illumination compensation image, the data processing module is used for increasing time sequence constraint loss on the basis of a YoLoV8 model, positioning key points of a human body and constructing a three-dimensional skeleton motion model; according to the method, a dynamic shielding processing engine and a multi-target decoupling mechanism are utilized, the constructed three-dimensional skeleton motion model is tracked based on a hierarchical association degree measurement function, human body motion data can be collected in real time, and accurate evaluation of postures and accurate extraction of gait features in multiple scenes are achieved.

The invention discloses a video gait analysis-based asthenia syndrome evaluation method, medium and equipment, and the method comprises the steps: firstly, synchronously collecting gait videos of different visual angles through a plurality of cameras, and extracting 2D joint point sequences of all visual angles through a stacked hourglass network; then, a high-precision 3D joint point time sequence is reconstructed through a multi-view fusion algorithm, depth features are extracted through a residual connection network, and data of all view angles are dynamically weighted and fused based on the shielding rate and the confidence coefficient; performing sliding window segmentation and downsampling on the 3D sequence, and performing action classification by using an LSTM time sequence model; and finally, key motion parameters are extracted from a classification result to calculate a weakness score, and weakness grade evaluation is realized. According to the method, the problem of single-view shielding is solved through multi-view 3D reconstruction, the evaluation precision is remarkably improved by combining dynamic weighted fusion and time sequence modeling, and compared with a traditional method, the method has the advantages of being non-contact, low in cost and high in objectivity, and is suitable for large-scale screening of community and family environments.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention relates to the technical field of biomechanical analysis, in particular to a health data input method and system and a storage medium. The method comprises the following steps: collecting multi-modal data of a subject, and carrying out gait event recognition to obtain a gait event mark; bilateral gait synchronous alignment is carried out according to the gait event marks, bilateral gait cycle data are obtained, and the bilateral gait cycle data comprise plantar pressure data, ground force data, joint angle data and motion state data; plantar pressure power processing is carried out according to the plantar pressure data and the ground force data, and plantar power parameters are obtained; estimating body segment inertial parameters of the subject; joint torque energy processing is carried out according to the body segment inertial parameters and the joint angle data, and joint power parameters are obtained; and performing gait acceleration calculation according to the motion state data to obtain a gait stability index. The accuracy, comprehensiveness and convenience of gait analysis are remarkably improved through a biomechanical analysis technology.

The invention provides a gait analysis method and an early warning system based on multi-source data fusion, three types of original signals are collected through a wearable inertial sensor, a plantar pressure insole and an edge calculation camera, and multi-dimensional motion characteristic parameters are fused through wavelet denoising, low-pass filtering, interpolation alignment and data consistency correction, so that the gait analysis method based on multi-source data fusion is realized. And constructing a user individualized gait feature prototype library. The method further adopts a dynamic causal graph network to model a motion relation between joints, determines an abnormal coupling mode based on causal weight and historical reference, and combines activity context and Bayesian rules to generate a dynamic threshold value to realize risk grading early warning, so that heterogeneous data collaborative analysis precision and abnormal gait detection sensitivity are improved, and the method is suitable for large-scale popularization and application. And support is provided for gait health monitoring and personalized risk management and control under multiple scenes.

The invention discloses a wearable plantar pressure and three-dimensional gait analysis system, and relates to the technical field of gait analysis, the system comprises a wearable in-shoe plantar pressure analysis subsystem and a three-dimensional gait analysis subsystem; the wearable in-shoe plantar pressure analysis subsystem comprises a pressure sensor module, a pressure integration module, an intelligent transmission module, a detection software module, a power supply module, an instantaneous pressure and balance detection module, a standing balance detection module, a balance and gait detection module and a cloud server; the three-dimensional gait analysis subsystem comprises a hardware unit and a software unit; according to the invention, a wearable plantar pressure and three-dimensional gait analysis collaborative system is constructed, bidirectional verification compensation is realized by means of data fusion of two subsystems, and traditional single-dimensional limitation is broken; pressure sensing and a double-view reconstruction technology are fused, data are dynamically captured, and bidirectional error correction is carried out; the limitation of traditional equipment is solved, the evaluation accuracy and robustness are improved, and a quantitative basis is provided for clinical diagnosis, rehabilitation and the like.

The invention relates to a cerebral apoplexy patient gait analysis and personalized rehabilitation correction method and system based on a big data model, and relates to the technical field of medical rehabilitation, a patient feature data set is generated by integrating multi-modal data including gaits, electroencephalogram, myoelectricity and the like, a rehabilitation critical period is determined by using real-time nerve regulation and control and brain-computer interface data, and the rehabilitation accuracy is improved. The system constructs a personalized rehabilitation scheme according to the optimized feature data set and rehabilitation training parameters, the rehabilitation effect is tracked through a rehabilitation progress evaluation module, the scheme relates to data fusion, dimension reduction, correlation analysis and dynamic adjustment of the rehabilitation scheme, the personalization and effectiveness of rehabilitation training are ensured, and the rehabilitation effect is improved. The invention aims to improve the rehabilitation efficiency and life quality of stroke patients.

The invention relates to the technical field of behavior feature recognition, in particular to a beef cattle oestrus state monitoring method based on dynamic gait analysis. According to the method, multi-source data acquisition, such as dynamic gait video and body temperature data, video preprocessing, such as frame extraction, denoising, abnormal frame recognition and restoration, target tracking, dynamic gait characteristic parameter extraction, such as step length, step frequency, step speed, joint angle change and body gravity center offset, and life data acquisition, such as diet, body weight change and drinking water. A large oestrus monitoring and recognition model integrating attitude analysis, behavior analysis, living state analysis and an integration module is constructed, and after training and iterative optimization, accurate judgment of oestrus, non-oestrus and wig oestrus states of beef cattle is achieved in combination with ear tag identity recognition. By monitoring the dynamic gaits and behavior modes of the beef cattle and combining daily life data of the beef cattle, the problem of misjudgment caused by wig oestrus is greatly avoided, and the monitoring accuracy is improved.

The invention discloses an old people lower limb motion function analysis method and system based on the Internet of Things technology, and belongs to the technical field of data analysis, and the method comprises the steps: deploying a data collection device to collect original data, preprocessing the original data to obtain a first data set, dividing the motion into gait cycles through data calculation, step length, step frequency, support time, swing time and joint angle of each gait cycle are calculated and integrated into a second data set, time sequence dynamic features are extracted to obtain a dynamic feature vector, a gait quality score and a gait degradation trend are obtained through model calculation, gait anomaly detection is performed to obtain a gait anomaly recognition result and a gait risk score, and a gait anomaly recognition result is obtained. And constructing a health intervention suggestion generation model. According to the old people lower limb motion function analysis method and system based on the Internet of Things technology, multi-dimensional data from different sources are fused, and a gait analysis model is adjusted in real time by using an adaptive algorithm, so that accurate early warning and personalized analysis are realized.

The invention discloses an intelligent management and control system and method for AI glasses, and the system comprises an environment sensing layer which integrates a millimeter-wave radar, an infrared sensor and a UWB positioning module, and senses the information of the environment where the AI glasses are located; the biological characteristic layer is used for authenticating the identity of the user by adopting an iris recognition and gait analysis composite authentication mode; the strategy engine layer comprises a dynamic permission matrix, a compliance rule base and a risk prediction model, and formulates and executes a corresponding control strategy according to environment perception information and a biological characteristic authentication result; the data protection layer is composed of a trusted execution environment and a federated learning framework and is used for guaranteeing the data security in the AI glasses system; and the man-machine interaction layer is provided with a tactile feedback and bone conduction alarm device, interacts with a user and provides alarm information. Through cooperative work of the layers, comprehensive, intelligent and safe management and control of the AI glasses are realized, so that the AI glasses can stably operate in various complex environments, and more reliable services are provided for users.

The invention discloses a Parkinson's disease gait analysis and diagnosis method based on deep learning, and belongs to the technical field of medical signal processing. The method comprises the following steps: acquiring multi-modal time sequence gait data of Parkinson's disease, preprocessing the multi-modal time sequence gait data, and dividing a data set to obtain preprocessed multi-modal time sequence gait data; the multi-modal time sequence gait data at least comprises foot pressure data, lower limb motion acceleration and lower limb swing angular velocity; designing a feature extraction network structure of a multi-channel convolutional neural, extracting features from the multi-modal time sequence gait data, and obtaining useful features from local to global; establishing a Transform feature fusion module, and introducing a multi-head self-attention mechanism to process all input useful feature data in parallel; a softmax classification diagnosis module is provided, Dropout and regularization technologies are adopted to avoid overfitting of data, an Adam optimizer is utilized to optimize model parameters, and a Parkinson's disease gait analysis diagnosis result is obtained.

The invention discloses a following shooting-based mouse gait analysis method, which comprises the following steps of: obtaining an initial video image of a mouse activity area, and separating a mouse movement area through a preset background model to obtain a first image; calculating a motion vector of the mouse according to the first image to obtain motion trend data; adjusting the shooting angle and the focal length of the camera according to the motion trend data to obtain a second image; according to the second image, adjusting image brightness distribution, eliminating the influence of uneven illumination, and obtaining a third image; based on the third image, extracting contour features of the mouse, and obtaining gait feature data in combination with a pre-established gait model; according to the gait feature data, updating a tracking path of the camera to obtain a fourth image; enhancing the image definition according to the fourth image to obtain a fifth image; based on the fifth image, key joint positions of the mouse are recognized through a deep learning model, and a gait analysis report is generated in combination with gait feature data.

The invention discloses a gait analysis method, system and equipment. The method comprises the following steps: acquiring a pressure value of each measuring point in a sole area in real time when a tested person stands or walks; generating a pressure distribution map and a change curve map based on the acquired pressure values; gait-related parameters are calculated and an electronic report is generated. In order to improve image visibility and analysis precision, a perceptual disturbance guided multi-scale color coding mechanism is adopted, and a high-resolution and vision enhanced pressure distribution diagram is generated through the steps of interpolation expansion, normalization, disturbance enhancement, HSV color wheel mapping and the like. The system also supports two analysis modes of balanced standing and dynamic walking, can output multi-dimensional gait indexes including pressure parameters, gait cycle parameters, pressure center tracks and the like, is matched with a built-in high-density sensor array and a man-machine interaction module of the system, is widely suitable for scenes of rehabilitation medical treatment, sports science, old-age health management and the like, and has wide application prospects. The method has the advantages of high detection precision, visual result, comprehensive analysis and the like.

The present invention relates to a method for creating an individualized machine learning inference model. In accordance with the invention, the aforementioned method and a related system involve a motion capture device adapted to be worn by a user. The motion capture device is adapted to acquire measurements, which are used to compute a first estimate of one or more gait parameters. Next, a database is accessed that contains previously collected observations of gait data and the first estimate is compared to the previously collected observations of gait data. Finally, the subset of previously collected observations of gait data that is most informative for the particular user is identified and the individualized machine learning inference model can be developed using the identified subset of previously collected observations of gait data.

The invention discloses a lumbar postoperative functional recovery evaluation method based on gait analysis, and the method comprises the steps: collecting a multi-modal gait multi-source signal, and building high-quality space-time synchronous multi-modal gait data through standardization processing and abnormity elimination; further extracting multi-point features such as an action sequence, pressure and myoelectricity, and realizing deep association between gait features and clinical evaluation indexes by adopting graph structure fusion and knowledge graph mapping; a graph neural network and an attention mechanism are introduced, the causal relationship between gaits and lumbar function recovery is reasoned, self-supervised dynamic optimization is performed according to individual differences, the objectivity, interpretability and adaptive ability of rehabilitation evaluation can be improved, and the rehabilitation accuracy is improved. And reliable data basis and causal traceability are provided for medical decision and personalized rehabilitation.

The invention discloses a Parkinson's disease patient rehabilitation training method based on video gait analysis, relates to the technical field of image analysis, solves the problem of dependence on subjective evaluation in the prior art, and realizes objective quantitative analysis of a rehabilitation effect. An athletic ability index Xpzs, a gait stability index Yb and a lower limb rehabilitation progress coefficient Ws of a patient are calculated and evaluated to ensure that an evaluation result is more reliable and accurate; meanwhile, an established real-time data feedback mechanism is combined with historical data, the problem that rehabilitation effect monitoring is insufficient is solved, and through regular tracking and effect evaluation, a training scheme and a rehabilitation target are dynamically adjusted, and it is ensured that the rehabilitation progress of a patient is tracked in a whole cycle; besides, the method is high in technical adaptability, personalized analysis can be provided according to illness states and rehabilitation progresses of different patients, the training intensity and the rehabilitation target can be dynamically adjusted, the flexibility and adaptability of a rehabilitation scheme are improved, the method is particularly suitable for patient groups with different illness states, and the rehabilitation effect is improved.

The invention provides a gait analysis and diagnosis method and device, electronic equipment and a readable storage medium, and relates to the field of artificial intelligence. The method comprises the following steps of: performing analysis and judgment according to the specific condition of a tested object to obtain a proper data sampling method, recording the tested object by using a monocular camera or a multi-view camera system, predicting a three-dimensional key point coordinate track of the motion of the tested object by using a visual model corresponding to the shooting method, and determining the three-dimensional key point coordinate track according to the predicted three-dimensional key point coordinate track. And performing motion reconstruction by using the coordinate track of the key point to obtain a diagnosis basis, and performing comprehensive analysis and diagnosis on the tested object through an artificial intelligence algorithm.

The utility model discloses an animal gait analysis device, and belongs to the technical field of medical research. Comprising a limb marking cage and a linear gait analysis footpath, and the gait analysis footpath is connected behind the limb marking cage; the limb marking cage comprises a supporting bottom frame, a left surrounding plate, a right surrounding plate, a front lifting door, a rear lifting door and a cover plate, a hollow cavity is defined by the supporting bottom frame, the left surrounding plate, the right surrounding plate, the front lifting door, the rear lifting door and the cover plate, a vertically-through hole shaped like a Chinese character'tian 'is formed in the supporting bottom frame, and a groove position used for containing ink is formed below the hole shaped like the Chinese character'tian'. A drawing plate is movably arranged on the hole shaped like the Chinese character'tian '; the gait analysis footpath comprises a bottom plate, a surrounding plate, a front door plate and a rear door plate, the bottom plate, the surrounding plate, the front door plate and the rear door plate define a hollow cavity, an inlet is formed in the front door plate, and an outlet is formed in the rear door plate. It can be guaranteed that an animal walks linearly during a gait experiment, and gait confusion of an operative limb and other limbs is avoided.

The invention discloses a six-dimensional force measurement matrix system and method for multi-terrain gait analysis of a humanoid robot. The system comprises a modular terrain simulation platform, a six-dimensional force measurement matrix module, a data acquisition and synchronization module and robot gait performance analysis software. The modular terrain simulation platform comprises a rigid leveling module, a slope module and a step module; the six-dimensional force measurement matrix module comprises a plurality of six-dimensional force measurement plates; the data acquisition and synchronization module acquires synchronous matrix data of each six-dimensional force measurement plate; and the robot gait performance analysis software calculates key kinetic parameters and time-space parameters and generates a comprehensive report. According to the invention, three terrains of a rigid flat ground, a slope and steps can be simulated, force and torque of the humanoid robot walking on the terrains are continuously and synchronously acquired through the measurement matrix, and comprehensive, real and accurate evaluation of gait functions is realized. And high-precision and multi-dimensional quantitative evaluation data are provided for gait control algorithm development, dynamic performance calibration and mechanical foot design of the robot.

The invention relates to the technical field of treadmills, in particular to a treadmill landing mode analysis method, a treadmill and a storage medium. The treadmill landing mode analysis method comprises the following steps: acquiring real-time air pressure data of a left air bag and a right air bag, and respectively generating air pressure time sequence curves; distinguishing a plurality of left foot landing data segments and a plurality of right foot landing data segments based on the air pressure time sequence curve; intercepting a left foot landing data segment and / or a right foot landing data segment for gait analysis; identifying and judging whether double peaks exist in the left foot landing data segment and / or the right foot landing data segment by adopting a peak searching algorithm; if the two peaks exist, the landing mode of the foot is heel landing; if the two peaks do not exist, whether the waveform is smooth or not is judged, if the waveform is smooth, the landing mode of the foot is tiptoe landing, and otherwise, the landing mode of the foot is sole landing. According to the method, the landing mode of the user can be visually reflected, wearing is not needed, and the daily running habit of the user is not affected.

The embodiment of the invention provides an actor recognition method, system and device based on ViT gait analysis and a medium, and belongs to the technical field of gait recognition. The method comprises the following steps: acquiring behavior video data of a target object; performing background subtraction processing on the behavior video data to obtain a binary image of the gait of the target object; inputting the binary image into a feature analysis model, wherein feature analysis is constructed and formed based on Transform; performing attitude feature extraction through a double-path parallel multi-head attention module of the feature analysis model, fitting a feature weight of the attitude feature through a feature average fusion module, and outputting an embedded feature vector; and comparing the embedded feature vector with a preset feature vector, and identifying the identity of the target object according to a comparison result. The embodiment of the invention aims to improve the accuracy of performing actor identity recognition through gaits.

The invention provides a gait analysis and anomaly detection method and system based on spatio-temporal joint modeling, and a medium. The method comprises the following steps: S1, dividing an original GRF signal into a time slice sequence; s2, inputting the time slice sequence into a multi-scale depth residual scaling network to extract multi-scale spatial-temporal features; s3, splicing the multi-scale spatial-temporal features into a time sequence feature sequence according to a time sequence, inputting the time sequence feature sequence into a Transform encoder, shielding invalid time steps at the same time, and outputting global time sequence features; and S4, carrying out classification processing on the global time sequence features, and outputting gait classification probability distribution. According to the method, time sequence modeling and spatial feature extraction are combined, complex space-time characteristics of gait signals are comprehensively captured, an accurate detection means is provided for Parkinson's disease, musculoskeletal injury and other gait-related diseases, meanwhile, model robustness, calculation efficiency and medical interpretation are improved, and the method is suitable for popularization and application. And a new technical support is provided for clinical diagnosis, rehabilitation evaluation and scientific research of movement.

The invention provides a multimodal feature-based gait analysis method and system and a readable storage medium. The method comprises the following steps of: constructing a stimulation signal time sequence rule of each target under a reference clock; generating a stimulation signal, distributing the stimulation signal based on a preset channel distribution matrix, and sending the distributed stimulation signal to each target point along the corresponding stimulation channel according to a stimulation signal time sequence rule; obtaining a motion image of a mark point in the to-be-analyzed target, and obtaining a kinematics signal of the mark point according to a pre-trained target recognition model; collecting electric signals fed back by each target spot in response to the stimulation signal, performing space-time alignment on various signals, and extracting feature information from each electric signal and kinematics signal after space-time alignment; and performing fusion processing on various feature information to obtain fusion features, inputting the fusion features into a pre-trained motion intention recognition model, and outputting a gait recognition result. The accuracy of the gait analysis result can be improved.

Method for processing users' cellphone data, the method comprising using a hardware processor for analyzing at least accelerometer (IMU) data provided by cellphones of at least a threshold number of users in a given location including recognizing at least one urban feature at said given location each time at least the threshold number of users, known to be present at the given location, exhibit a given motion pattern known by the processor to be characteristic of said urban feature; and / or generating a map including a stored representation of said at least one urban feature which is associated in memory with said given location at which the accelerometer data indicative of the urban feature was collected.

The invention relates to the technical field of medical instruments, and discloses a medical rehabilitation underwater gait analysis system and method based on skeleton calculation. The system comprises a plurality of underwater cameras used for shooting moving images of human bodies in water; the light source equipment is mounted underwater and is used for irradiating the reflective marker ball on the human body; the reflective marker ball is used for being mounted on a preset mark point of a human body so as to reflect light; the image processing module is used for acquiring an underwater human body motion image shot by the underwater camera and carrying out image distortion correction and mark point position detection; and the skeleton resolving module is used for carrying out skeleton model construction and skeleton resolving based on the position change of the mark points so as to carry out gait analysis. The method has the advantage of being capable of achieving efficient and accurate motion capture and gait analysis in a complex underwater environment.

The invention discloses a gait analysis and posture correction intelligent insole suitable for high-weight crowds and a preparation method of the gait analysis and posture correction intelligent insole. The intelligent insole comprises a composite functional layer, an electrode layer and a packaging layer, and the composite functional layer comprises a linear elastic gasket and a sensing material arranged in an opening area of the linear elastic gasket. The sensing material adopts a wedge-shaped structure design, and the effective contact area between the sensing material and the electrode can be linearly changed along with the compression displacement under the constraint action of the linear elastic washer, so that the high-linearity electrical response of a pressure signal is realized. And the adjacent sensing units are physically isolated by the linear elastic washers, so that the crosstalk phenomenon in the pressure sensing array is effectively reduced. Through linear regulation and control of the linear elastic gasket on mechanical response and cooperative modulation of the wedge-shaped structure sensing material on electrical response, stable and high-linearity plantar pressure detection is achieved under the condition of high plantar load, and the device is suitable for gait analysis and posture correction of high-weight people in the states of standing, walking, running and the like.

Embodiments disclosed herein are directed to systems and processes that derive clinically meaningful data from a test device and a production device for assessing gait and speech in subjects having a neurological condition. The test device for gait analysis may be a simpler or more updated device in reference to a production device. Speech characteristics may be evaluated using a digital mobile speech-based platform. Other aspects are disclosed and claimed.

The invention relates to the technical field of intelligent monitoring equipment, in particular to a radar point cloud target detection system and method based on multi-modal space-time fusion. Comprising the following steps: at least two radar transceivers synchronously collect echo signals of a target at different visual angles, and establish a three-dimensional coordinate system; based on the three-dimensional coordinate system of the target points, the target points of each time slice are clustered, a coordinate set of point cloud clustering is obtained, and whether the point cloud gravity center of each detection target is displaced or not is judged; if the detected target is displaced, capturing a space trajectory of a target point based on the barycentric coordinates, and performing gait analysis on the detected target; and if the detection target does not displace, judging the body posture of the detection target. The method has the advantage of low cost, reliable radar point cloud can be realized, the defects that the traditional point cloud needs multi-antenna MIMO and the cost is high are overcome, body point cloud modeling in the walking process is realized, meanwhile, posture body point cloud and vital sign data are jointly verified, and the reliability is enhanced.

The invention provides an informatization wound ticket giving system based on facial recognition, and relates to the technical field of identity verification. The system comprises a physiological status monitoring module, a permission adjustment control module, an identity adaptability matching module, a gait behavior verification module and a terminal data linkage module. By analyzing eyeball movement, blinking frequency, pupil change and facial muscle activity and monitoring the physiological state of an individual, dynamic permission adjustment during identity verification is achieved, matching precision is optimized through micro-expression analysis, mismatching caused by emotion fluctuation is reduced, the adaptability of identity verification is enhanced through behavior data in gait analysis, high accuracy is maintained, and the identity verification efficiency is improved. The real-time performance of bill management and access control is improved through a data linkage mechanism among multiple devices, and the verification safety and the remote operation stability are enhanced in combination with multi-dimensional biological characteristics.

This application relates to the technical field of human posture detection and gait analysis, and discloses a portable posture detection and gait analysis method and system. The method includes: building a hardware system, the hardware system including an embedded vision module, a data processing module, and an interaction module, and collecting a human motion video stream through the embedded vision module; using a background difference method and an adaptive illumination compensation algorithm to extract a dynamic foreground from the collected video stream, and output an illumination-compensated image; based on the output illumination-compensated image, using the data processing module, adding a timing constraint loss to the YoLoV8 model, locating key points of the human body, and constructing a three-dimensional skeletal motion model; using a dynamic occlusion processing engine and a multi-target decoupling mechanism, and tracking the constructed three-dimensional skeletal motion model based on a hierarchical correlation metric function. The present invention can collect human motion data in real time and achieve accurate posture assessment and gait feature extraction in multiple scenarios.