153 results about "Gait phase" patented technology

The invention discloses a foot drop assisting method based on a flexible ankle joint exoskeleton, and belongs to the field of rehabilitation medical assistance. According to the method, an unhealthy side ankle joint angle is input into a self-adaptive oscillator to learn related parameters in the self-adaptive oscillator, and an affected side ankle joint angle is predicted; meanwhile, a CNP model is adopted to predict the ankle joint angle of the shank on the affected side according to the gait phase estimated by the self-adaptive oscillator, two prediction results of the ankle joint angle of the shank on the affected side are subjected to weighted fusion to finally obtain the expected angle of the ankle joint on the affected side, and the fused ankle joint angle prediction output is more stable and continuous. And a weighted fusion result is smoothed. According to the method, the normal walking mode based on the unhealthy side ankle joint angle of the patient can be dynamically learned online in real time, and personalized affected side ankle joint angle reference tracks at each moment are generated for different patients, so that the abnormal motion mode of the patient is inhibited, and the patient is guided to reach the normal gait in the rehabilitation training process.

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 discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

A gait recognition method based on surface electromyographic signals and transfer learning comprises the steps that a surface electromyographic signal collecting system is designed and manufactured, and electromyographic signals of eight muscles of the legs of a subject are collected through an electromyographic sensor and data collecting equipment; the method comprises the following steps: recording gaits of a subject through motion capture equipment, carrying out timestamp alignment on electromyographic signals and gait information, carrying out label signal labeling on the electromyographic signals, and carrying out filtering and feature processing to obtain a training data set; an LDA-LSTM gait phase recognition algorithm is constructed; constructing an improved MCD transfer learning framework; and carrying out training optimization on the constructed transfer learning framework parameters to enable the recognition and classification effect of the transfer learning framework parameters in the target domain to be optimal, and storing the trained model as a whole model containing the training weight of the target domain again for recognizing the gait phase of the target domain. According to the method, the cross-individual identification accuracy of the model is improved, and the problems of difficulty in sample label labeling, high cost and the like when different individuals are trained are solved.

The invention relates to the field of gait analysis, and discloses a gait analysis method and system based on intelligent shoes, and the method comprises the steps: obtaining pressure distribution data captured by a plantar pressure sensor, dividing gait stages according to a pressure mode, and recognizing gait phases; the method comprises the following steps: acquiring three-dimensional acceleration, angular velocity and magnetic field data of an inertial measurement unit, and converting the three-dimensional acceleration data from an IMU coordinate system to an earth coordinate system; performing integral calculation on the three-dimensional acceleration data based on the earth coordinate system to obtain speed data; describing the time stage of the gait through the pressure mode of the pressure distribution data, detecting to obtain a zero speed moment, correcting the speed data by applying a zero speed updating algorithm according to the zero speed moment to obtain the corrected speed data, and further performing integral calculation to obtain the motion trail of the foot; through a principal component analysis algorithm, decoupling the displacement in the step length direction and the step width direction in the foot motion trail so as to obtain step length and step width parameters; the plantar pressure distribution data cloud picture and the foot movement track are combined and visualized, visual reproduction of the foot posture in the gait process is completed, and a convenient mode is provided for a patient to monitor and evaluate the gait health condition of the patient.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

The invention belongs to the technical field of lower limb exoskeleton control, and particularly relates to a flexible lower limb exoskeleton assistance control method based on multi-source information fusion. The method comprises the following steps: firstly, acquiring plantar pressure, hip joint acceleration and angular velocity and knee joint acceleration and angular velocity, and fusing the hip joint acceleration and angular velocity and the knee joint acceleration and angular velocity respectively to obtain hip and knee joint angles; performing normalization processing on hip and knee joint angles and plantar pressure to form a multi-source information matrix; inputting the multi-source information matrix into a gait phase recognition model for gait phase recognition to obtain a gait phase; then, according to the gait phase, the joint angle and the joint torque, a Bowden line playing a power assisting role is judged; calculating expected Bowden line change lengths under the assistance of hip and knee joints respectively; and finally, converting the expected change length of the Bowden cable under the assistance of the hip and knee joints into expected rotation angles of hip and knee joint motors, and controlling the hip and knee joint motors by adopting fuzzy PID control according to the rotation angles. The method solves the problems that in lower limb exoskeleton control, the gait phase recognition accuracy is low, and precise power-assisted control is difficult to carry out.

The invention discloses a lower limb exoskeleton control system and method based on phase recognition and model prediction control. The system comprises a kinematics modeling module, a gait database module, a data processing and feature extraction module, a gait phase recognition module, a dynamics modeling module, a model prediction controller and an actuator control unit. By recognizing the gait phase and dynamically adjusting and controlling the target, precise assistance of the natural gait of the wearer is achieved. The system calculates the trajectory of the ankle joint relative to the hip joint in real time based on a kinematic model, and constructs a database in combination with gait data collected under multiple working conditions to provide an expected trajectory and reference parameters for control. In the dynamic modeling process, motor driving torque and man-machine interaction torque are introduced, a system dynamic model is established in a unified mode, and control characteristics under different gait phases are reflected.

The invention relates to an AR projection interaction treadmill virtual scene generation method and device, equipment and a medium. The method comprises the following steps: acquiring motion inertia data and running belt motion parameters of a user on a running machine; respectively carrying out motion state extraction and gait phase extraction according to the motion inertia data and the running belt motion parameters to obtain a current motion state vector and gait phase information of the user; inputting the motion state vector and the gait phase information into a pre-trained motion state prediction model to obtain a preset predicted motion state of a plurality of frames in the future; based on the predicted motion state, performing layered scene generation through a scene generator to obtain a corresponding virtual scene; and performing projection mapping transformation processing on the virtual scene, and obtaining real-time AR projection data in combination with low-delay compensation. By adopting the method, a highly immersive and interactive virtual training environment can be provided in an AR projection mode.

The invention discloses a real-time detection method and system for the lower limb binding state of an exoskeleton robot. The method comprises the following steps: acquiring a human body gait in a reversing motion state at present, performing feature extraction on the human body gait, and judging a current gait phase through the feature extraction; and then the lower limb binding state of the corresponding gait phase is detected in real time. The system comprises a state observer module, a gait phase recognition module, a binding detection module, an active disturbance rejection controller module and a CMSIS-DSP library module. Through a split-phase dynamic detection strategy, the real-time performance and reliability of binding and falling identification are remarkably improved.

The invention relates to the technical field of point cloud map construction, in particular to a humanoid robot dense point cloud map construction method and system, and the method comprises the steps: obtaining a video image collected when a humanoid robot walks in real time; the gait phase of the humanoid robot in the current frame image in the video image is judged; estimating the camera pose of the current frame image based on the gait phase of the humanoid robot of the current frame image; judging whether the current frame image is a key frame or not based on the camera pose of the current frame image; and performing local mapping and loopback detection according to the key frame to obtain a global dense point cloud map. According to the method, the fuzzy and jittering conditions of the acquired data during mapping of the humanoid robot are effectively improved, a more accurate environment map can be obtained, the operation precision during task execution of the robot is improved, and the collision risk in the advancing process of the humanoid robot is reduced.

The invention relates to the technical field of intelligent control, and discloses a hip joint exoskeleton control method and system based on gait mode prediction and gait phase estimation, a terminal and a storage medium, and the method comprises the steps: constructing and training an initial unsupervised terrain detection model, and obtaining a target unsupervised terrain detection model; inputting the environment binary image into a target unsupervised terrain detection model to obtain a terrain detection result, and obtaining a gait mode prediction result based on a majority voting strategy; according to the hip joint angular velocity, target gait phase estimation is obtained based on a self-adaptive oscillator system; on the basis of the expected auxiliary torque curve, net auxiliary torque is obtained according to the gait mode prediction result and target gait phase estimation, and hip joint exoskeleton assistance modes are switched according to the net auxiliary torque. According to the method, marking of a large amount of data is avoided through the unsupervised terrain detection model, terrain detection and exoskeleton control are combined, and the terrain self-adaptive walking capacity of the exoskeleton is enhanced.

The present invention discloses a humanoid robot gait analysis and robot control method, and relates to the field of robot control technology. The collected gait data is used to pre-train a gait analysis and recognition model, which is used to evaluate the gait parameters of the humanoid robot. The gait parameters include: gait symmetry, gait consistency, movement, gait phase, and gait health; gait parameters estimated in real time based on the pre-trained gait analysis and recognition model. Afterwards, the pre-trained gait analysis and recognition model is deployed into the humanoid robot control strategy; the gait parameters estimated in real time by the gait analysis and recognition model are added to the state space of the humanoid robot control strategy, and the control strategy generates and executes corresponding behavioral actions. The present invention evaluates the symmetry and consistency of the humanoid robot during movement, and simultaneously identifies the robot's movements and gait phases to quantify the robot's gait parameters, so as to improve the robot's performance during the learning process.

Various examples are provided related to gait kinematics. A methodology for human in the loop optimization (HILO) for use of a hip exoskeleton is presented. In one example, a method includes monitoring a gait phase of an exoskeleton and controlling switching time between admittance parameters associated with actuator control of the exoskeleton, where the switching time is controlled based upon the monitored gait phase. The admittance parameters can be predetermined and can be user specific. The time of the switching can be determined from use of the exoskeleton and can be determined using reinforcement learning.

The invention relates to the technical field of exoskeletons, and discloses a lower limb assistance exoskeleton gait recognition and assistance control system and method. The system comprises a joint motion information acquisition module used for measuring joint motion information; the gait phase estimation module is used for carrying out gait phase judgment according to the joint movement information so as to determine a current gait phase; the joint angular position control module is used for adopting a joint angular position control mode to control the exoskeleton system when the current gait phase is in the supporting phase; and the joint torque control module is used for adopting a joint torque control mode to control the exoskeleton system when the current gait phase is in the swing phase. According to the system, independent control over the swing phase gait and the supporting phase gait is achieved through the system, active assistance can be achieved through the independent control mode under different gait phases, the man-machine following performance can be improved, then the wearing comfort of the exoskeleton is improved, and therefore the system adapts to the outdoor electric power overhaul occasion; and the burden of an electric power overhaul worker is reduced.

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 provides a knee joint exoskeleton multi-action torque auxiliary control method based on activity classification and phase detection. The knee joint exoskeleton multi-action torque auxiliary control method comprises a two-stage artificial neural network and three human body dynamic models. The first stage of the two-stage artificial neural network is composed of a network used for activity classification, the second stage of the two-stage artificial neural network is composed of three independent gait phase estimation regression networks, and the three human body dynamic models comprise a single-support human body model, a double-support human body model and a swing human body model which are divided according to human body gait phase features. The model outputs the real biological joint torque at the current moment, and the controller proportionally outputs an auxiliary torque curve required by the exoskeleton. According to the method, four activity types of standing, walking, going upstairs and going downstairs can be accurately classified, the gait phase percentage under the non-rhythm gait condition is accurately estimated, and meanwhile, the corresponding auxiliary torque curve is generated based on the current gait type and gait phase.

The invention relates to a stroke gait phase recognition method and system based on a multi-stage model, and the method specifically comprises the following steps: employing a self-adaptive sliding window algorithm based on index-standard deviation fusion, carrying out the dynamic fragment interception of a gait signal of a stroke patient, and obtaining the original stroke gait data; preprocessing the original stroke gait data to obtain original gait features; constructing an integrated feature enhancement unit (IFEU), inputting original gait features, and extracting time context features of the stroke gait signals; constructing a hierarchical gait feature aggregation unit HGFAU, inputting time context features, performing gait feature extraction of each level, and obtaining multiple fusion features after fusion; and inputting the multiple fusion features into a linear layer classifier, and classifying the input features to obtain a stroke gait phase recognition result. According to the invention, gait phase recognition can be realized, and objective walking state evaluation and abnormity monitoring can be carried out on rehabilitation treatment assistance of a patient.

The application relates to the technical field of intelligent control, and discloses a hip exoskeleton control method and system based on gait pattern prediction and gait phase estimation, a terminal and a storage medium, the method comprising the following steps: constructing an initial unsupervised terrain detection model and training the model to obtain a target unsupervised terrain detection model; inputting an environment binary image into the target unsupervised terrain detection model to obtain a terrain detection result, and obtaining a gait pattern prediction result based on a majority voting strategy; obtaining a target gait phase estimation based on an adaptive oscillator system according to a hip joint angular velocity; obtaining a net auxiliary torque based on an expected auxiliary torque curve, a gait pattern prediction result and the target gait phase estimation, and switching a hip exoskeleton assistance mode according to the net auxiliary torque. The unsupervised terrain detection model avoids labeling a large amount of data, the terrain detection is combined with the exoskeleton control, and the terrain self-adaptive walking capability of the exoskeleton is enhanced.

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.

The invention relates to the technical field of intelligent walking assisting equipment, and discloses a blind person intelligent guiding walking assisting method and system based on deep learning. The intelligent guiding power-assisted walking method for the blind is applied to guiding power-assisted electronic equipment, and specifically comprises the following steps: S101, receiving a starting signal sent by a user terminal, starting a multi-source sensor array to collect environment data in parallel, executing a Kalman-Transform space-time alignment algorithm, establishing a multi-modal data space-time unified coordinate system, and establishing a multi-modal data space-time unified coordinate system; when gait phase changes are collected and detected, a multi-source sensor array is activated and dynamically triggered for high-precision scanning, and a threat evaluation model is generated to calculate an obstacle collision probability point cloud matrix; and S102, processing the obstacle collision probability point cloud matrix through Point VoxelNet to generate a three-dimensional environment skeleton. The cross-modal attention mechanism of the millimeter wave radar and the vision improves the rain and fog weather recognition accuracy, and in addition, the dynamic threat evaluation module is combined with the double-threshold early warning strategy, so that the collision false alarm rate is effectively reduced.

The invention relates to an exoskeleton control method and system and an exoskeleton system. The exoskeleton control method comprises the steps that hip joint angle sensor information is obtained, and hip joint angle information is obtained; according to the hip joint angle information, an exoskeleton movement mode is judged; if the exoskeleton motion mode is a static mode, comparing the hip joint angle information with a preset angle information threshold value, and judging an exoskeleton motion state sequence number corresponding to the static mode; if the exoskeleton movement mode is a periodic movement mode, an exoskeleton movement state sequence number corresponding to the periodic movement mode is judged according to the hip joint angle information; judging whether the exoskeleton motion state sequence number accords with a preset assisting gait phase or not, and if yes, generating an exoskeleton torque control instruction according to the exoskeleton motion state sequence number; the system has the advantages that the exoskeleton control accuracy is improved, the system complexity is reduced, and wearing is convenient.

The invention discloses an exoskeleton driving method based on real moment constraint and continuous gait phase modeling, which comprises the following steps: acquiring multi-modal signal data in the gait process of a subject by using a multi-sensor synchronous acquisition system, including IMU signals, EMG signals, plantar pressure signals and optical motion capture data; preprocessing the multi-mode signal data to obtain a preprocessed signal; performing continuous gait phase prediction and gait pattern recognition according to the preprocessed signal; estimating a joint instantaneous moment by using a pre-trained moment estimation network based on the predicted gait phase and gait mode; and converting the obtained instantaneous moment of the joint into a driving signal of an exoskeleton actuator. According to the method, discrete event triggering is replaced by continuous gait phase modeling, so that the smoothness and robustness of control are ensured; meanwhile, a mechanical real constraint and individualized moment estimation mechanism is introduced, stable man-machine cooperative control is achieved, and the adaptability and use experience of the exoskeleton in a complex environment are remarkably improved.

The invention discloses a method and system for driving and controlling a lower limb exoskeleton through brain-computer interface intention confidence, and relates to the technical field of medical rehabilitation. The method comprises the following steps: firstly, identifying an original electroencephalogram signal to obtain intention confidence, and mapping an impedance parameter group containing joint stiffness and a damping coefficient in combination with gait phase information; secondly, extracting a motion position and an interaction force error, comparing an intention confidence coefficient with an actual execution state by using a sliding time window to obtain a nerve matching error, and fusing the three into a total composite error; meanwhile, the human-machine coupling compliance is evaluated based on the human body joint angle and the exoskeleton torque variation, and a safety gain coefficient is discriminated and generated; and finally, performing gain operation on the total composite error based on the impedance parameter group, and performing safety gain coefficient correction to obtain a target control torque to be output to a driving actuator. In this way, a nerve-force-motion three-closed-loop framework is constructed, and compliant self-adaptive on-demand assistance and high-safety man-machine collaborative rehabilitation with defense protection are achieved.

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.

This disclosure relates to the field of exoskeleton technology, and discloses an exoskeleton control method, storage medium, and exoskeleton. The exoskeleton control method includes: acquiring gait feature values; inputting the gait feature values ​​into a fuzzy neural network model to obtain a target gait output value; determining candidate gait phases and previous gait phases based on the target gait output value, wherein the gait output value of the previous gait phase is temporally adjacent to the target gait output value; determining the target gait phase based on the candidate gait phases and the previous gait phase; and controlling the exoskeleton based on the target gait phase. This embodiment identifies gait phases by comprehensively considering previous gait phases and candidate gait phases, which helps improve the accuracy and reliability of gait phase identification, thereby enabling the exoskeleton to more effectively drive the user's limbs, thus providing human-machine compliance.

The invention provides a gait reference angle generation method and device, a robot, equipment and a medium, and relates to the technical field of robot assistion.The method comprises the steps that target gait auxiliary physiological parameters and target walking speed of a target user are obtained; inputting the target gait auxiliary physiological parameters and the target walking speed into a hip joint gait reference trajectory generation model to obtain a hip joint gait reference trajectory output by the hip joint gait reference trajectory generation model; acquiring a real-time gait phase generated when the target user wears the exoskeleton robot, and according to the real-time gait phase and a hip joint angle fitting polynomial, generating corresponding hip joint gait reference angles of the target user at different moments; wherein the hip joint angle fitting polynomial is constructed according to the hip joint angle value corresponding to each moment in the hip joint gait reference trajectory. According to the invention, the precision and individuation degree of rehabilitation training are improved.

The present invention proposes a visual gait recognition method and system based on deterministic learning. The method collects a gait sequence consisting of multiple frames, extracts three-dimensional gait variables from each frame of the gait sequence, forms a three-dimensional gait variable sequence, and plots a three-dimensional gait phase space trajectory. The three-dimensional gait variable sequence is input into a trained RBF neural network to obtain a gait dynamic feature sequence and time-invariant knowledge. The time-invariant knowledge is used to construct a gait pattern library, and the dynamic trajectory is plotted. The three-dimensional gait variable sequence of the gait sequence to be identified is extracted, and a state estimator is constructed using the time-invariant knowledge in the gait pattern library. The dynamics of the gait sequence to be identified are quickly compared with the dynamics of each gait in the gait pattern library to obtain the final gait recognition result. The present invention can accurately reflect the spatiotemporal characteristics of each individual, keenly capture changes in gait patterns, improve the accuracy of gait recognition, and has strong robustness across perspectives.