249 results about "Gait cycle" patented technology

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention provides a muscle stimulation adjusting method and system based on gait detection. The method comprises the following steps: acquiring lower limb biomechanical data; performing phase recognition according to a dynamic programming algorithm, and constructing a pressure center track to perform feature extraction to obtain gait features; carrying out feature analysis on the gait features through a reverse dynamics model and a machine learning model, and carrying out output judgment according to a double-model collaborative decision-making mechanism to obtain a stimulation parameter decision-making result; generating an electrical stimulation waveform according to the decision result, and applying differential electrical stimulation to the target muscle in the gait cycle to obtain a stimulation scheme; and monitoring stimulated myoelectricity feedback signals and gait changes, and carrying out parameter updating by calculating a signal change rate and analyzing a motion track error to obtain an updating scheme. According to the method, through a double decision-making mechanism of the reverse dynamics model and the machine learning model and a real-time feedback regulation mechanism, the gait phase recognition precision is improved, and precise regulation and control of stimulation parameters are realized.

The invention provides a humanoid machine control method and system based on model prediction, and the method comprises the steps: firstly obtaining joint sensing data (hip joint movement angle sequences and knee joint stress signals) and environment interaction data (depth image flow data and sole contact pressure data) of a humanoid machine, and generating a dynamic state feature set through feature correlation processing; the feature matrix comprises a joint cooperation feature vector and an environment constraint feature matrix; calling a pre-training model to carry out spatio-temporal joint prediction on the feature set, and outputting a future motion control sequence containing gait cycle features; then, a joint synchronous control model is constructed on the basis, and a driving control instruction set containing time coordination constraint is generated; and finally, the driving control instruction set is transmitted to the distributed execution unit, and joint state feedback data is collected for next round of processing, so that the motion control performance of the humanoid machine in a complex environment is improved.

Embodiments can relate to a prosthetic foot system. The system can include an ankle joint housing co-locating a rotation sub-assembly, a torsional shock absorbing sub-assembly, and a vertical shock absorbing sub-assembly. The system can include a foot component attached to the ankle joint housing. The system can be configured as a co-designed architecture to functionally integrate at least two functions of: (i) torsional shock absorption, (ii) multi-axial motion with stiffness modulation in single gait cycle, (iii) active dorsiflexion, and (iv) vertical shock absorption by causing the at least two functions to operate in concert.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a muscle group stress analysis method and device, a terminal and a storage medium, and the method comprises the steps: respectively obtaining motion data collected by inertial measurement units arranged on the body of a user, and recognizing gait events based on the motion data corresponding to the inertial measurement units of the lower limbs, dividing the continuous motion data into a plurality of gait cycles according to the gait event; fusing the motion data acquired by each inertial measurement unit to obtain a three-dimensional posture of each preset segment of the body, and constructing a three-dimensional motion track of the spine area of the user in combination with a human kinematics chain model; processing the three-dimensional motion track and the known muscle group stress data by adopting a singular value decomposition embedding regression method to obtain a preliminary target muscle group stress curve of each gait cycle; and correcting the initial target muscle group stress curve based on a human body multi-rigid-body dynamic model in combination with a control cost function to obtain a final target muscle group stress curve.

The invention discloses a method and a device for testing motility and wear resistance of socks, and relates to the technical field of wear resistance detection of socks. The method comprises the following steps: S1, collecting user parameters and gait parameters of different motion types, constructing a gait model, calculating a pressure distribution evaluation value, and carrying out data preprocessing; s2, new material socks are installed, the stress path and the friction environment of the socks under the real movement condition are controlled and reproduced through the rhythms of different movement modes, the friction force, the sliding track and the local shearing condition are synchronously recorded, and a wear track is formed; s3, obtaining a sock sole image, performing visual identification and wear region extraction, and outputting a quantitative wear index; and S4, evaluating the wear resistance of the new material socks, carrying out suitability matching analysis, taking optimization measures, and outputting an evaluation report. The problem that due to the fact that different movement types have completely different gait cycles and movement postures, wear of the socks is difficult to test is solved.

The invention relates to a personalized exercise rehabilitation risk early warning method and device based on IMU dynamic instability assessment, and the method comprises the steps: collecting multi-dimensional exercise data of a subject, carrying out the posture fusion, and obtaining the limb posture, joint angle change rate and angular momentum of the subject; performing time sequence mode decomposition to extract a single gait cycle and a specific jump landing attitude; calculating a biomechanical feature vector of each motion primitive; inputting the single gait cycle, the specific jump landing attitude and the biomechanical feature vector into a hybrid injury induced motion model, and outputting a multi-dimensional injury risk index vector; and comparing the multi-dimensional injury risk index vector with historical health data and a moving target dynamic adjustment threshold value of the subject, and if the threshold value is exceeded, providing real-time auditory, tactile or visual feedback to the subject. According to the method, the influence of movement speed and rhythm change is overcome, dynamic instability in movement can be more sensitively reflected, and personalized risk assessment in a real sense is realized.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively 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 a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention relates to a control method of an artificial limb knee joint. According to the technical scheme, the control method comprises the steps that a heel pressure sensor signal Ph, a sole pressure sensor signal Pf and an inclination angle theta and an angular velocity omega output by a shank inertial measurement unit are obtained in real time; when it is detected that the Pf decline rate exceeds a preset multiple threshold value of the heel pressure decline rate, dynamically calibrating an inclination angle reference theta base and calculating an offset theta cal; dividing six stages of a gait period based on the theta < cal > and the pressure signal, and calculating period duration T; a step speed-damping mapping knowledge base is constructed, and the damping opening degree in the early stage of swing is optimized through iterative learning; finally, the adjusted U value is applied in the early stage of swinging, and fixed damping is kept in other stages. The method is mainly used for improving the walking naturalness and terrain adaptability of the artificial limb at different speeds.

The invention relates to the technical field of medical health data analysis, in particular to a multi-modal data fusion abnormal behavior early warning system before falling of a high-risk patient. According to the method, for any patient, a plurality of gait cycles formed by time frames are obtained according to ankle coordinate distribution of different time frames; according to the ankle coordinate distribution of different time frames in each gait cycle, gait high risk of each gait cycle is obtained; according to the hip coordinate distribution of different time frames in each gait cycle, obtaining the center-of-gravity shift degree of each gait cycle, and obtaining the high-risk center-of-gravity shift reference degree of all patients in each gait cycle; for any patient, the falling probability in each sliding window is obtained according to the gravity center shift degrees of different gait cycles, the high-risk gravity center shift reference degree and various physiological parameter data at different moments; and early warning is carried out on abnormal falling behaviors. According to the invention, the accuracy of early warning of abnormal falling behaviors is improved by accurately analyzing the falling probability of the high-risk patient.

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 invention relates to the technical field of artificial intelligence and medical detection, and particularly provides a human body gait variability detection system based on an improved TCN model. The system comprises a multi-modal sensor array, a signal preprocessing module, a self-adaptive time sequence feature extraction module, a bidirectional time sequence context fusion module, a progressive depth feature enhancement module and a variability quantization output module, a receptive field is dynamically adjusted through a learnable expansion coefficient, the bidirectional time sequence context is fused to improve the period segmentation precision, and the precision of the period segmentation is improved. And gradual feature enhancement is adopted to relieve deep network feature degradation, so that accurate quantification of five indexes such as a gait cycle variation coefficient and a stride time standard deviation is realized, and the sensitivity and reliability of clinical gait variation detection are improved.

The invention relates to an improved YOLOv8-Pose-based artificial limb user gait coordination scoring method, which comprises the following steps of: firstly, acquiring coordinates and confidence coefficients of 17 key points of a human body in the whole process by using an improved YOLOv8-Pose model frame by frame for a video of finishing a TUG test of an artificial limb user, and acquiring diagonal vertex coordinates of a detection frame; thirdly, dividing the video into a forward walking part and a reverse walking part by utilizing the height change of the detection frame and the shoulder breadth change of the artificial limb user, dividing a complete gait cycle in forward walking and a complete gait cycle in reverse walking by utilizing the coordinate change of the ankle joint, and calculating gait conventional parameters; and then, calculating a gait coordination index by using the collected key point coordinates. And finally, independently scoring each item of part of gait conventional parameters and all gait coordination indexes by referring to the standard of a normal person. And standardizing the gait routine parameters and the gait coordination indexes, inputting the standardized gait routine parameters and the standardized gait coordination indexes into the trained MLP classifier, and calculating a comprehensive score by using a classification probability.

The invention relates to the technical field of shoes, and discloses a foot self-adaptive support adjusting method based on a biomechanical model, which comprises the following steps: S1, foot biomechanical modeling: acquiring foot geometric data of a user through three-dimensional scanning, acquiring dynamic load parameters in combination with gait analysis, and constructing a personalized foot finite element model; s2, stress distribution calculation: sampling according to 0-100% phase intervals of 10% in a gait cycle; s3, material gradient design: determining a gradient material rigidity mapping relation according to Pmax distribution; and S4, realizing a self-adaptive structure: constructing a gradient sole structure through 3D printing. A high-shear-force area is judged through double threshold values, and a pressure gradient self-adaptive non-uniform honeycomb structure and configuration classification are arranged in the area, so that local deformation is dynamically regulated and controlled in a pedaling and stretching period and a landing period, and the effects of reducing a stress peak value of the high-shear area and reducing friction heat accumulation are achieved.

The invention relates to the technical field of knee joint exoskeleton devices, in particular to a clutch control type knee joint exoskeleton damping device and a control method. Comprising a pair of rotating assemblies which are rotationally connected; the pair of leg connecting assemblies are connected with the pair of rotating assemblies correspondingly, and the pair of leg connecting assemblies are used for being connected with the thighs and the shanks on the same side of the user correspondingly; the rotary damper is controllably coupled with the knee joint assembly; the clutch device is arranged between the rotary damper and the knee joint assembly, and the clutch device is used for switching the coupling state of the knee joint assembly and the rotary damper; and the control unit is used for judging a gait cycle phase and a motion mode according to the signal acquired by the detection unit, and controlling the clutch device to switch the coupling state of the knee joint assembly and the rotary damper according to a preset control rule. And the exoskeleton device can intelligently control the clutch device to dynamically adjust the damping state of the knee joint.

The invention relates to a self-adaptive knee joint protection method and device based on motion risk prediction. The method comprises the steps that knee joint motion data, environment data and gait data of a user are collected in real time through an embedded inertial measurement unit, a pressure sensor, a myoelectricity sensor and a depth camera; the environment data comprises a ground material, a gradient and illumination intensity; the gait data comprises stride frequency, stride and gait cycle; inputting the knee joint movement data, the environment data and the gait data into a knee joint movement risk prediction model, and predicting the knee joint movement risk of the user in a future period; when the knee joint movement risk exceeds a preset threshold value, the exoskeleton is controlled through the knee joint auxiliary equipment to adjust the flexion and extension damping and the movement range of the knee joint. According to the method, the motion intention is recognized through the risk prediction model, and real-time monitoring and personalized protection of the knee joint motion risk are achieved based on the variable stiffness execution mechanism of the shape memory alloy and the gait mode.

The invention discloses a weeding method of a quadruped robot and a control system of the quadruped robot. The method comprises the steps that the quadruped robot is controlled to enter a weeding operation area based on a preset advancing path, and to-be-removed weeds located in front of a robot body of the quadruped robot are determined according to sensing data in the weeding operation area; determining a weeding phase according to the current gait cycle of the robot body; according to the current stopping posture of the robot body, the space coordinates corresponding to the to-be-removed weeds and the gravity center shift predictive quantity of the robot body, track interpolation is conducted on the track at the tail end of a footprint of the robot body, and a weeding track is obtained, the weeding track represents a track from the current foot stopping position of the robot body to the weed position of the weed to be removed; and using a weeding cutter to cut off weeds to be removed within the weeding phase based on the weeding trajectory.

The invention relates to the technical field of health assessment, in particular to an orthopedic rehabilitation process real-time monitoring and assessment method, which comprises the following steps of: based on gait biomechanical parameters, identifying feature points, optimizing nodes in combination with body weight, stride frequency and stride change, extracting node correction parameter groups, analyzing muscle force change of knee joints and ankle joints, and evaluating the rehabilitation process of the orthopedics department. And combining the gait stability and the joint movement to obtain a correlation index proportion sequence. Parameters such as stride frequency, stride and body weight are combined with automatic recognition of feature points in a gait cycle, the time sequence sensitivity and individual adaptability of data are enhanced by dynamically correcting a feature point judgment standard, muscle force recovery and gait stability indexes are synchronously integrated and quantified in different stages, and the accuracy and the accuracy of gait recognition are improved. The linkage relation between the indexes can be adjusted according to real-time data, accurate tracking of a function recovery path is further supported, and the rehabilitation progress can be reflected by a dynamic structure of multi-dimensional parameters.

The invention relates to a control method for dynamically optimizing contact force distribution of a foot-type robot, belongs to the technical field of foot-type robots, and solves the problem that the foot end is easy to sink when the foot-type robot advances in a soft terrain in the prior art. The method comprises the following steps: acquiring real-time ground counter-acting force of each foot of the foot robot in a current gait cycle support phase and joint control information of each foot in a previous gait cycle support phase; based on the joint control information of each foot in the previous gait cycle support phase and the standard ground reaction force time curve, obtaining an expected ground reaction force time curve of each foot in the current gait cycle support phase; based on the expected ground reaction force time curve of each foot in the current gait cycle supporting phase and the corresponding real-time ground reaction force, a PID controller is used for obtaining the control torque of each foot at each moment, the control torque is applied to each joint motor of each foot, and the contact force distribution of each foot is optimized. The walking stability of the foot type robot in the unstructured terrain is achieved.

The invention relates to a humanoid robot three-dimensional complex environment navigation method and system, and belongs to the technical field of robot navigation. Comprises: obtaining a random path node; constructing a collision detection model according to the gait cycle of the robot; detecting whether the random path node passes collision detection by using a collision detection model; if not, deleting the random path node, and obtaining the random path node again; otherwise, adding the random path nodes into the random tree; for each node added to the random tree, collision detection and plane size detection at the node are carried out, and when the detection is passed, candidate nodes are added to a global path point sequence; screening the sequence according to the complexity of the current plane to obtain complex region path nodes and flat region path nodes; and corresponding motion instructions are generated according to the operation requirements of the robot on the path nodes in the different areas. According to the method, the dynamic envelope space calculation cost is reduced, and the global path planning accuracy is improved.

Orthotic devices for providing arch support for the foot are disclosed. The orthotic device includes a base member, an arch support portion, and a plurality of connectors coupled to the base member at a first end and each connector of the plurality of connectors coupled at a second end to each arch member of the plurality of arch members. The orthotic devices provide continuous contact with the plantar surface of the foot during all phases of the gait cycle. In addition, the orthotic devices are assembled by coupling the connectors ends to the base member on one end and each arch member on the other end.

The invention discloses a multi-modal sensing data automatic labeling method and system for gait assessment, and the system comprises a sensing collection module which comprises two paths of pressure sensors and a six-axis IMU sensor; the two paths of pressure sensors are used for acquiring plantar pressure data; the six-axis IMU sensor is used for acquiring lower limb movement data; the wireless transmission module is used for uploading the collected plantar pressure data and lower limb movement data to a cloud Internet of Things platform; the cloud data processing module is used for receiving and storing the plantar pressure data and the lower limb movement data; the data labeling module is used for automatically recognizing a gait cycle and carrying out multi-dimensional score labeling; and the user interaction module is used for interacting with the data annotation module and the cloud data processing module and displaying gait evaluation related data and results. According to the invention, a multi-mode sensing PCB is self-developed, a closed-loop system of collection, cloud loading, labeling and display is constructed, and accurate, continuous and remote evaluation of gaits of children with cerebral palsy is realized.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

The invention provides an electrical stimulation and lower limb exoskeleton device fusion control method and system, and the method comprises the steps: driving an exoskeleton to move through bottom admittance control based on the actual human-computer interaction torque and expected torque of a knee joint and reference trajectory data of the knee joint in a gait cycle, and triggering ankle joint electrical stimulation through a fixed stimulation parameter group; after the gait cycle is finished, torque errors are calculated, and periodic iteration updating is conducted on the knee joint reference trajectory data through an iterative learning control algorithm till convergence is conducted to obtain target knee joint reference trajectory data; and then determining a target moment of a gait phase based on the target knee joint reference trajectory data, and obtaining a corresponding target stimulation parameter group from the set stimulation parameter time mapping library to replace the fixed stimulation parameter group for subsequent stimulation. According to the method, through single-cycle and cross-cycle trajectory iteration, the adaptation defect of a traditional fixed trajectory is avoided, the stimulation parameters are synchronously optimized, and the dorsiflexion assisting effect is indirectly improved.

An acetabular bone defect filling block design method based on a force conduction path comprises the steps that a mechanical conduction target path is determined, specifically, the stress condition of a hip joint in a gait cycle is simulated on the basis of a three-dimensional model of a pelvis of a patient, a bone defect area Vdefect and a three-dimensional model of an implanted supporting bracket which are obtained in advance, and the mechanical conduction target path is determined; determining an expected force flow conduction path from the acetabular cup loading area to the host bone main bearing area; a filling space determining step: calculating a residual defect space Vremain formed after the support is implanted; a filling block main body generation step: taking the residual defect space Vremain as a design boundary, taking following of the expected force flow conduction path and realization of matching of the structural rigidity and the host bone as an optimization target, generating a filling block main body three-dimensional model with an internal support truss through a topological optimization algorithm, and steering a strong gluteus column by depending on an incomplete acetabular ring, and a fixed foundation which cannot be shaken is provided. An intelligent structure with the optimal mechanical property is generated by converting empirical shape matching into simulation driving generation type design.

The invention discloses a clothes changing pedestrian re-identification method, device and equipment, a medium and a product, and relates to the technical field of target detection, and the method comprises the steps: decomposing the appearance characteristics of each pedestrian in target video data into body structure characteristics and clothes variable characteristics; on the basis of target video data, generating space-time dynamic characteristics of joint movement in a gait cycle by adopting a trained space-time diagram convolutional network, and generating multi-modal gait representation of each pedestrian in combination with a gait energy diagram; the gait energy diagram is generated according to target video data; inputting the body structure features, the clothes variable features and the multi-modal gait representation of each pedestrian into a trained scene perception gating network to obtain fusion features of each pedestrian; according to the fusion features of the pedestrians, whether the pedestrians are the same person or not is judged, cross-modal feature complementation can be achieved, and the accuracy of re-recognition of the clothes changing pedestrians is improved.

The invention discloses a video data processing method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring a video clip comprising a plurality of video frames, wherein the video clip comprises gait action information of human body walking; extracting position information of human skeleton key points in the plurality of video frames to obtain a data sequence; according to the data sequence, the gait cycle of human body walking in the video clip is recognized, multiple gait cycles are obtained, and the multiple gait cycles are obtained by segmenting the video clip according to the data sequence; screening a target gait cycle in the plurality of gait cycles, the target gait cycle including at least two target video frames in the plurality of video frames, the at least two target video frames including a video frame in which a left foot is in a landing state and a video frame in which a right foot is in a landing state; and determining gait feature information according to the target gait cycle and the data sequence. According to the embodiment of the invention, the accuracy of the gait feature information can be effectively ensured.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.