267 results about "Gait (human)" patented technology

The invention discloses a patient gait abnormity evaluation auxiliary system based on human body posture detection, and relates to the technical field of intelligent medical treatment, and the system comprises the steps: collecting patient gait video data, preprocessing the patient gait video data, and marking patient skeleton key points; based on the patient skeleton data set, calculating position coordinates of patient skeleton key points, and according to the position coordinates of the patient skeleton key points, identifying motion tracks of the patient skeleton key points; on the basis of the patient skeleton key point motion trail, patient gait features are extracted, and a comprehensive gait feature vector is formed; training a gait prediction model by using the patient skeleton data set, inputting the comprehensive gait feature vector into the gait prediction model, and predicting a gait anomaly index of the patient; and evaluating the prediction result, and generating an evaluation report. The gait abnormity index of the patient is predicted by recognizing the motion trail of the skeleton key points of the patient, comprehensive evaluation of the gait state of the patient is achieved, and clinical diagnosis accuracy and efficiency are improved.

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 lower limb exoskeleton gait track prediction method based on an LSTM-KAN fusion model, and the method comprises the steps: collecting human motion data through a sensor assembly, and carrying out the filtering, missing value processing and normalization of the human motion data; then constructing an overall architecture of a prediction model based on an LSTM-KAN network, optimizing parameters of the prediction model by using a particle swarm optimization (PSO) algorithm, extracting key features in the preprocessed data as a training data set, and inputting the training data set into the prediction model for training; and finally, collecting current human body motion data, pre-processing the current human body motion data, inputting the pre-processed current human body motion data into the trained prediction model, and outputting future human body gaits and tracks by the prediction model. And the controller takes a future gait track generated by the prediction model as a reference track to generate a driving signal and sends the driving signal to the actuator so as to realize accurate control of the actuator. By adopting the gyroscope sensor, the acceleration sensor and the pressure sensor for gait estimation, exoskeleton motion control can be effectively improved, and man-machine interaction experience is effectively improved.

The invention belongs to the technical field of gait recognition, and provides a gait recognition method fusing a depth map derived from an RGB image and a contour sequence, and the method fuses a depth map sequence based on the RGB image and a traditional contour map sequence. An estimation depth map is displayed from a given RGB image sequence by using an existing RGB video data set and a latest depth estimation model, and the estimation depth map is used as a new mode to capture inherent distinctive features in human motion; compared with a traditional input mode, the depth map provides clearer 3D geometric information about the human body and the movement of the human body, and gait characterization is enriched; the invention provides a more comprehensive and accurate gait recognition solution; and a relatively good recognition effect is achieved on the basis of a widely used gait recognition reference.

The invention belongs to the technical field of deep learning, and particularly relates to a multi-modal gait recognition method based on SMPL modal decomposition and embedding fusion, and the method comprises the following steps: constructing a multi-modal gait recognition model DFGait fusing an SMPL model and a contour; providing an adaptive frame joint attention module, and adaptively extracting important joint information of a gait sequence key frame in an SMPL posture branch; a modal embedding fusion module is provided, and efficient fusion of two kinds of modal information is achieved by aligning and fusing SMPL model features and contour features in a unified semantic space; and jointly supervising the training of the DFGait model by using a joint loss function combining triple loss, cross entropy loss and modal consistency loss. According to the method, the SMPL human body model is decomposed, and gait information of dynamic postures and static shapes contained in the SMPL model is fully extracted. Branch optimization is carried out through an adaptive frame joint attention module, and finer spatial-temporal feature extraction of gait information is realized.

The present invention relates to the field of human body motion posture recognition, and in particular to a bipedal gait recognition system and method based on multi-sensor fusion. According to the system, a corresponding gait deep learning LSTM neural network model is obtained by training, and the deep learning LSTM neural network model is stored in a computer. Motion information or data of a human body collected by using the bipedal gait recognition system of the present application is transmitted into the trained deep learning LSTM neural network model, and the current motion state of a user is recognized. The system of the present application collects human body motion information by means of direct contact with a human body, so that the system can conveniently and accurately collect motion information, is easy to carry, is suitable for most motion scenarios and has wide development prospects.

The invention discloses a figure gait recognition method based on a dynamic space-time diagram convolutional neural network, and the method comprises the following steps: carrying out the human body posture estimation of a CASIA-B data set, and generating a thermodynamic diagram; inputting the thermodynamic diagram into an HM-C module to generate human skeleton sequence information; extracting joint, skeleton and speed features; inputting into a local attention module to obtain feature vectors containing weight information of different body parts in the whole action sequence; inputting the data into a backbone network dynamic GCN module to generate a spatial feature map; inputting the human skeleton sequence information and the spatial features into a dynamic TCN module to generate a rich time feature map; and inputting the time feature map into a joint and skeleton dynamic fusion module, fusing joint-level and skeleton-level motion modes in different time ranges, and generating joint-skeleton motion features which are richer and stronger in discrimination. According to the method, the problem that a baseline GCN method seriously depends on a manually defined skeleton topology is solved.

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 hip replacement postoperative gait dynamics prediction method based on discrete optimal control, and belongs to the field of clinical medical auxiliary prediction. The implementation method comprises the steps that kinematics data are collected through a preoperative gait motion capture experiment, muscle activation signals are collected through a surface myoelectricity system, and on this basis, a neuromuscular control strategy of a patient is obtained through a neural network; predicting the gait motion characteristics of the postoperative patient based on discrete mechanics and an optimal control method, and predicting the postoperative muscle activation degree by using the neuromuscular control strategy; by comparing the muscle activation degrees before and after the operation, the influence of the replacement operation on the muscle force change of the hip joint of the patient is investigated. According to the method, discrete optimal control and the neural network are combined, the learning characteristics of human nerve-muscle control are considered while the physical characteristics of the system are not deviated, personalized muscle control strategies better conforming to different patients can be found, and personalized prediction of postoperative gait dynamic characteristics is achieved.

The invention relates to the technical field of knee joint rehabilitation evaluation, in particular to a knee joint rehabilitation evaluation method based on a human skeleton by using a space-time diagram convolutional network, and the method comprises the following steps: obtaining a rehabilitation evaluation gait video set; extracting human body key point skeleton data, and slicing the human body key point skeleton data into gait samples according to the number of time frames; preprocessing the gait sample, and extracting joint, skeleton and joint angle features; a gait-link method is adopted to divide a space gait skeleton diagram, a space-time attention mechanism is added, and an improved CTR-GCN network model is constructed; training the improved CTR-GCN network model by using the gait sample to obtain a gait evaluation model; and processing a gait video to be evaluated, and inputting the processed gait video into the gait evaluation model to obtain a rehabilitation evaluation result of the patient. According to the method, the human body key point skeleton in the walking video of the patient is extracted and input into the space-time diagram convolutional network for training, the rehabilitation evaluation model is obtained, the rehabilitation condition of the patient is evaluated by using the model, and the requirement of rehabilitation evaluation is met.

The invention discloses a double-clutch variable-stiffness knee joint exoskeleton, and relates to the technical field of wearable lower limb exoskeletons.The double-clutch variable-stiffness knee joint exoskeleton comprises a gait sensing module arranged outside and a limb connecting arm module used for being connected with a human body, and an energy storage module is installed on one side of the limb connecting arm module; a sensor module is mounted on the other side of the limb connecting arm module; the limb connecting arm module comprises a limb connecting arm assembly I used for being connected with the thigh of the human body and a limb connecting arm assembly II used for being connected with the shank of the human body, the limb connecting arm assembly I and the limb connecting arm assembly II are each provided with a clutch unit, and the core speed increaser is installed between the limb connecting arm assembly I and the limb connecting arm assembly II. The energy storage module and the sensor module are arranged on the two sides of the core speed increaser, and the core speed increaser is connected with the energy storage module. The device has the advantages of compact structure, variable rigidity bandwidth and good motion adaptability, can realize high energy recovery efficiency in a multi-gait mode, and reduces the metabolic consumption of a human body.

The invention discloses a household elderly falling monitoring system and method based on feature fusion, and relates to the technical field of falling monitoring. The technical key points are as follows: firstly, the reasonability and effectiveness of an obtained detection result are guaranteed to obtain first gait image recognition of the home elderly to estimate the posture of the home elderly, the improved OpenPose network and CoordConv are utilized to replace VGG-16 into EfficientNet, the first gait of the home elderly is accurately monitored, the balance influence proportion is calculated for different gaits, and the balance influence proportion is calculated for the different gaits. According to the method, the probability that the old people have falling behaviors is judged to generate different risk signals, falling behaviors and postures under medium and high risk signals are further analyzed in a targeted mode, through deflection analysis, changes of human body postures are monitored in real time, characteristics of different falling postures are understood, different falling postures are independently and accurately modeled, and a second falling coefficient is generated; the tumble degree is accurately identified, and a foundation is laid for optimization of a subsequent model.

The invention provides a patient gait recognition algorithm based on an improved Lite-HRNet model, and provides a lightweight high-resolution human body posture estimation network applied to human body gait recognition and detection for the problems of limited computing resources and low accuracy of the lightweight human body posture estimation network. Specifically, videos are randomly selected from a patient gait video data set for video data enhancement, the videos after data enhancement are obtained, the videos are input into the improved Lite-HRNet model frame by frame for training, and the trained improved Lite-HRNet model is obtained; the method for improving the Lite-HRNet model comprises the following steps: firstly, introducing phantom convolution and a CBAM attention mechanism into a second convolution in a trunk stage to carry out image feature extraction; secondly, introducing phantom convolution and a decoupling attention mechanism into all branches of the model to serve as a basic residual model; finally, a continuous human body key point sequence is obtained through output of the GC module in up-sampling of the fourth stage; and inputting the continuous human body key point sequence into the C3D network to obtain a gait classification result.

The invention belongs to the technical field of gait recognition, and particularly relates to a human gait recognition method and system in a complex scene. And sequentially performing illumination correction, human body posture angle alignment and shielding processing on the extracted human body gait data, and performing human body gait recognition based on the gait data obtained after illumination correction, human body posture angle alignment and shielding processing. According to the occlusion processing, a comprehensive and reliable occlusion area judgment standard is set based on the connection relation between skeleton nodes, the human anatomy reference distance, kinematics constraint and the time continuity requirement, and then a missing human body area is reconstructed through optical flow compensation or bilinear interpolation based on information of visible pixels around the occlusion area. The accuracy of gait recognition in a complex scene is improved, the problem of adaptability of the complex scene is solved in a breakthrough mode, high-precision gait identity recognition is achieved, and the security and protection monitoring requirement of a real scene is met.

The invention relates to a lower limb motion intention recognition method based on surface electromyogram (sEMG) fusion features. An sEMG feature fusion model and a genetic radial neural network model are included. The sEMG is widely applied to gait analysis and robot control, and due to the fact that the accuracy rate of intention recognition is low and development of an exoskeleton robot is limited, a genetic radial neural network algorithm based on sEMG multi-feature fusion is provided to achieve accurate recognition of eight lower limb actions. An original sEMG signal is collected through a gait experiment, and time domain, frequency domain and wavelet decomposition signal characteristic parameters of the original sEMG signal are extracted and fused. And using a principal component analysis method to carry out dimension reduction on fusion features, and inputting sEMG fusion feature vectors into a radial basis function neural network optimized by a genetic algorithm to carry out training and identification. According to the method provided by the invention, the sEMG fusion features are utilized to identify the human body lower limb actions, the accurate identification of the lower limb actions is realized in combination with a high-performance neural network algorithm, and support is provided for the application of sEMG signals to an intelligent rehabilitation robot.

The invention discloses a multi-stage control method for a lower limb exoskeleton, is applied to the field of exoskeleton robots, and aims to solve the problem that the design control precision is reduced due to the uncertainty of a model in the prior art. According to the controller designed by the invention, in a high-level control layer, a human body training mode is determined by a motion intention of an operator so as to generate a reference gait track; in the middle control layer, a variable admittance controller is designed, and three training modes of human exoskeleton cooperative movement, namely a passive mode, an active mode and a passive-to-active mode, are planned. In a low-level control loop, a method with a radial basis function neural network estimation function and a fixed-time convergence controller with input dead zone compensation are provided so as to ensure that an exoskeleton joint position tracks an expected trajectory output by an admittance loop. In order to avoid the Zeno phenomenon of the designed controller, an event trigger mechanism is used to determine the execution time of sampling and transmitting signals. Finally, the effectiveness of the proposed control strategy is verified through simulation and experimental results.

A midsole or insole device for a shoe includes a first variable stimulation mechanism positioned to interface one of the metatarsal heads and the heel and a second variable stimulation mechanism positioned to interface a lateral aspect of the foot between the fifth metatarsal head and the heel. During gait-related activities, the first variable stimulation mechanism produces stimulus of an intensity greater than the second variable stimulation mechanism. At least one of the first variable stimulation mechanism and the second variable stimulation mechanism comprises two bonded layers including a resilient stimulating upper layer and a less resilient stimulus-enhancing lower layer. The upper layer includes a plurality of holes that pass through the entirety of the upper layer, and the lower layer includes a plurality of equally spaced upwardly facing projections aligned substantially perpendicular to an upper surface of the upper layer.

The invention discloses a quasi-passive knee joint exoskeleton and a design method thereof, the knee joint exoskeleton comprises a transmission energy storage module, a power-assisted regulation and control module and a wearing module, and the power-assisted state is switched according to the gait stage of a human body. The transmission energy storage module transmits joint torque through an input gear, an intermediate gear and an output gear, and a pressing rod on an output shaft is used for driving a flexible hinge to deform, so that efficient energy storage and release in a small space are realized; the assistance regulation and control module controls a cam shaft to rotate, drives a swing shaft to push an intermediate gear to move and enables the gears to be meshed in the supporting phase, motion limiting is formed through contour matching of a cam shaft body and a swing shaft matching cambered surface, mechanical self-locking is achieved, locking force does not need to be additionally provided, and walking assistance is achieved. In the swing phase, the camshaft is controlled to rotate to enable the unlocking cambered surface to turn to the swing shaft, the swing shaft is unlocked, a driving pin of the camshaft drives the swing shaft to rotate, the gear is disengaged, and free swing is achieved. Compared with the prior art, the invention has the advantages of light weight and low power consumption.

The invention relates to the technical field of medical data analysis, in particular to a spatio-temporal characteristic quantitative evaluation method for Parkinson's disease motion symptoms, which comprises the following steps: deploying an inertial measurement unit to collect three-dimensional acceleration angular velocity magnetic field data, constructing a human body connection structure to generate a connection neural network topological structure, the method comprises the following steps: calculating trajectory direction angle and angular velocity change adaptive weighting to judge stability, aggregating multiple rounds of convolution propagation of adjacent features to form a space-time fusion motion feature set, identifying tremor gait amplitude according to time sequence multi-head attention to extract a time sequence feature mode, calculating tremor gait coordination to generate a Parkinson's disease motion symptom quantitative evaluation result, and calculating a Parkinson's disease motion symptom quantitative evaluation result. According to the method, the limb coordination is captured by constructing sensor network motion data topological connection and fusing multi-dimensional part information, the remote association is captured by keeping the time sequence stable through adaptive weight attenuation and connection convolution depth aggregation features, and the multi-head attention fine recognition tremor frequency and gait change are combined. And the Parkinson's disease symptom identification and evaluation consistency is improved.

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 the field of lower limb hip and knee data analysis, and discloses a user feature data effective acquisition and analysis method for lower limb hip and knee rehabilitation, which comprises the following steps: acquiring related data for movement at the kitchen hip and knee of a user and performing data preprocessing; meanwhile, a convolutional neural network is used for conducting modeling analysis on the preprocessed related data, and a multi-channel sequence gait detection algorithm model is designed and constructed and used for presetting the next movement of the lower limbs, the hips and the knees of the user. And finally, performing model loss training optimization on the multi-channel sequence gait detection algorithm model, and introducing a scoring mechanism to enable the multi-channel sequence gait detection algorithm model to perform gait scoring on the target user. According to the invention, simultaneous input of multiple channel data can be realized, detection of the current state and gait of the human body can be realized, and the effect of still keeping recognition without switching modes under the condition that the channel data is missing can also be realized.

The invention discloses a gait recognition method fusing skeleton point action information, and belongs to the technical field of human gait recognition. Comprising the steps of gait data acquisition, skeleton point action relation modeling, dynamic spatio-temporal feature extraction and identity feature library self-evolution updating. Modeling is carried out on human skeleton actions and semantic relationships thereof through a structured semantic action graph, and identification features of key gait action fragments are adaptively extracted in combination with a variable structure space-time fusion network, so that high-precision gait identification under a complex environment and a variable view angle is realized. Meanwhile, a continuous self-evolution identity feature library mechanism is provided, gait change and new identity access can be automatically adapted, and the manual maintenance cost is reduced. According to the method, the accuracy, robustness and expansibility of gait recognition are improved, and the method has wide practical application value.

The invention discloses a human body lower limb posture monitoring system based on a flexible sensor. The human body lower limb posture monitoring system comprises an array type flexible piezoresistive sensor module, a data acquisition and processing module, a feature selection module, a pressure data color mapping module, a wavelet neural network module and a joint bending angle result output module. An array type flexible piezoresistive sensor used for obtaining initial plantar pressure distribution data is designed, a method based on gait intervals is adopted to select data points, key feature points most related to joint angle changes are screened out based on time sequence information of gait events, the dimensionality of input data is reduced, and the accuracy of the input data is improved. And the structure and the training process of the network are optimized, and high-precision prediction of the lower limb joint angle is realized.

The invention relates to the technical field of intelligent control, and discloses a power-assisted control method of a lower limb pneumatic flexible exoskeleton and a lower limb pneumatic flexible exoskeleton system.The method comprises the steps that healthy side plantar pressure data are obtained, healthy side key gait event nodes are recognized, and the power-assisted control method of the lower limb pneumatic flexible exoskeleton is obtained according to the healthy side key gait event nodes and ankle joint angle data; estimating an uninjured side continuous gait phase, and calculating an affected side target gait phase according to the uninjured side continuous gait phase; according to the target gait phase of the affected side, auxiliary torque and tension are determined; according to the auxiliary torque and the pulling force, target air pressure for supporting the airbag and shrinking the airbag is determined, an electromagnetic valve is controlled to execute deflation or inflation operation according to the target air pressure, and the internal pressure of the airbag is controlled within a target range. The invention aims to provide flexible assistance conforming to the biomechanical characteristics of the ankle joint of a human body for people with ankle joint dysfunction, improve the walking ability and improve the safety and comfort of rehabilitation training.

The invention discloses a bionic ankle joint and an artificial limb, the bionic ankle joint comprises an ankle joint base and a sole mechanism, and the ankle joint base is used for being connected with a leg component; the sole mechanism comprises a front sole component, a rear sole component and at least one elastic piece, the rear sole component is rotationally connected with the ankle joint base, the front sole component is rotationally connected with the rear sole component, one end of the elastic piece is connected with the rear sole component, and the other end of the elastic piece is connected with the front sole component; the elastic component is used for providing elastic restoring force when the front sole component rotates relative to the rear sole component. The sole fitting degree is improved through multi-joint linkage, the gait stability is enhanced, the additional burden caused by uneven stress is avoided, energy recycling is achieved through the elastic piece, energy consumption is reduced, meanwhile, the pedaling effect is improved through elastic assistance in the pedaling stage, the motion characteristic of the bionic ankle joint is closer to the natural state of the human body, and the mechanical performance of the bionic ankle joint is improved. And the matching degree of equipment and human motion characteristics is improved.

The invention provides a gait camouflage method for resisting biological recognition. The gait camouflage method comprises the following steps: wearing a gait camouflage device on a key part of a user; the method comprises the following steps: acquiring acceleration characteristics, angular velocity characteristics and magnetic field signal data of a user through a gait camouflage device, fusing the acceleration characteristics, the angular velocity characteristics and the magnetic field signal data to form a super vector, and performing time sequence coding to obtain a sample super vector of a current time slice; calculating the Hamming distance between the sample supervector and the reference supervector in the subspace, and taking the Hamming distance as a classification basis; optimizing an electrical stimulation intervention strategy based on the gait recognition model; through functional electrical stimulation regulation and control of specific lower limb muscles, joint motion angles are influenced, space-time and kinematics parameters of gaits are changed, and thus gait characteristics with a camouflage effect are realized. Electrical stimulation is applied to lower limb muscles according to different gait stages, so that human gait features are effectively changed, and the gait camouflage target is achieved; by analyzing gait parameter differences under different camera visual angles, an intervention mode of maximizing gait changes is selected, and gait features can be effectively camouflaged.

The invention discloses a multi-dimensional lower limb parameter detection method and system fusing spatial-temporal characteristics, and the method comprises the steps: collecting the lower limb movement information of a wearer through a wearable device; the lower limb movement information comprises position information, posture information and sole multi-dimensional stress information; estimating lower limb space-time parameters and gait events according to the lower limb movement mechanical model and the lower limb movement information; based on the plantar multi-dimensional force measurement model and the lower limb movement information, plantar multi-dimensional force data fused with the spatio-temporal characteristics are obtained. The method can accurately estimate lower limb space-time parameters and human body tracks, and realizes plantar three-dimensional force space resolving and mapping.

The invention discloses a human body gait recognition method and device based on multi-modal fusion, and belongs to the field of human body gait assessment, and the method comprises the steps: collecting electromyographic signals of muscles at different movement positions of lower limbs; collecting near-infrared brain function imaging data of the brain in a gait image and an idle state; collecting pressure information of the planta pedis in the movement process; the electromyographic signals, the near-infrared brain function imaging data and the plantar pressure information are input into a multi-modal fusion model, feature extraction is conducted on the electromyographic signals, the near-infrared brain function imaging data and the plantar pressure information, and different features are fused through a cross attention mechanism; the classification algorithm identifies the fused features and performs gait evaluation, and as the electromyographic signals, the pressure information of the planta pedis and the near-infrared brain function imaging data are synchronously generated with the human body movement, the human body gait identified through the data is low in hysteresis quality and high in accuracy.

The invention relates to an Informer-based human lower limb gait phase personalized prediction method, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring human lower limb gait motion information through a data acquisition device and forming a data set; training the pre-model; storing the trained pre-model; collecting individual lower limb data of the user; performing personalized model training based on the pre-model; storing the personalized model; acquiring a human lower limb right thigh swing angle through a data acquisition device; and performing model use based on the real-time data of the wearer. Different from a traditional gait phase prediction method, the method increases the model complexity, avoids the use of a large number of sensors, and increases the model precision on the basis of reducing the use of hardware equipment, so that a corresponding controller structure and control parameters are more accurately set according to specific prior information such as man-machine coupling characteristics and the like; and the wearing comfort of the exoskeleton user in the man-machine coupling cooperative walking movement is effectively improved.

The invention discloses a wearable tactile biological gait feedback system and method for a Parkinson's patient, and belongs to the field of human gait rehabilitation, and the system comprises the following modules: a posture sensing module which obtains the information of an accelerometer, a magnetometer and a gyroscope at the positions of two feet by using an inertial sensor IMU bound to the surfaces of the two feet; the data processing module is used for receiving the data information of the posture sensing module, estimating the posture of the two feet, estimating the gait phase of the two feet by using the posture of the two feet, and finally outputting a signal according to a gait feedback stimulation strategy; and the tactile feedback module is used for receiving the stimulation signals of the data processing module and providing vibration stimulation signals at the back and thigh gastrocnemius muscles. According to the wearable tactile biological gait feedback system and method for the Parkinson's patient, related posture data provided by an IMU can be utilized, double-foot posture estimation can be completed through multi-rate multiple-correlation-entropy Kalman filtering, double-foot phases are estimated through a self-adaptive oscillator algorithm, and the wearable tactile biological gait feedback system and method for the Parkinson's patient are obtained. And implementing vibration stimulation based on a set gait feedback strategy.