282 results about "Lower limb exercises" patented technology

The invention discloses a lower limb motion data real-time analysis method and system applied to rehabilitation guidance, and the method comprises the steps: calling a personalized model according to user information, converting real-time motion data into historical motion feature vectors, inputting the historical motion feature vectors into a real-time prediction module, and generating multiple frames of future motion feature vectors through coding and decoding; calculating a deviation value between each future motion feature vector and a preset threshold range to obtain a motion deviation value, calculating a motion risk proportion of each future motion feature vector, performing weighted fusion to obtain a motion risk probability, and generating an equipment parameter adjustment value and pose adjustment information through reverse mapping; and finally, according to risk probability assessment training stage promotion, synchronously displaying correction information and adjusting equipment operation parameters. According to the method, the dynamic model is constructed through pre-training, real-time prediction of future motion features is realized in combination with the lightweight LSTM, and the predictability and personalized adaptation ability of rehabilitation guidance are improved.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention discloses a lower limb muscle fatigue assessment method and system based on multi-modal physiological signals, and relates to the technical field of biomedical engineering, target muscles are selected, sEMG signals, AUS signals and respiratory flux VE data are synchronously collected, and RPE is recorded; preprocessing various signals to remove interference and abnormal points; extracting sEMG time-frequency domain features and AUS muscle thickness features; fusing features and constructing vectors in a standardized manner; dividing five fatigue grades; and a data set is divided according to stratified sampling, a model is optimized through five-fold cross validation, and real-time features are input to output a fatigue level. Multi-modal physiological signals are fused, fatigue evaluation accuracy is improved, different fatigue stages are finely adapted, muscle load and energy consumption are reduced through personalized adaption and a dynamic adjustment strategy, rehabilitation training safety and comfort are improved, and efficient recovery of lower limb movement functions is assisted.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention relates to an assessment method and system for a stroke recovery stage, and belongs to the technical field of medical assessment, the method comprises the following steps: obtaining demographic information of a stroke patient, and obtaining motion video data of the stroke patient, the motion video data comprising upper limb motion data, lower limb motion data and / or hand motion data; key points are extracted from the motion video data of each type based on a key point recognition algorithm, corresponding motion posture data in the motion video data of each type are calculated based on the positions of the key points, and the motion posture data comprise angle data and displacement data; and determining preset motion attitude data based on a Brunnstrom six-stage theory and demographic information, comparing the motion attitude data with the preset motion attitude data, and obtaining a recovery stage of the stroke patient based on a comparison result. Real-time monitoring and intelligent evaluation of the rehabilitation process of the stroke patient are realized, and the evaluation accuracy and diagnosis efficiency are improved.

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 a rope-driven ankle-foot exoskeleton robot and a control method, the ankle-foot exoskeleton robot comprises a two-way driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module and a rope-driven driving module, the lower leg binding module is arranged on the lower leg, and the ankle-foot fixing module is worn on the foot. The two-way driving module transmits power to the shank binding module and the ankle and foot fixing module in sequence through a rope transmission device, and therefore ankle joints are driven to do dorsal flexion or plantar flexion motion. The ankle-foot exoskeleton robot is driven by a rope and has the advantages of being light in weight, easy to deploy, good in flexibility and the like, modular design is adopted for the assemblies, the relative positions of all the modules are easy to adjust, and the ankle-foot exoskeleton robot can adapt to the difference between individuals in the wearing process; the position of the rope transmission device on the shank binding structure is easy to adjust, and rapid conversion between plantar flexion and dorsal flexion can be achieved on the basis that the freedom degree of movement of the lower limbs of the patient is not interfered.

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

The invention discloses a brain-computer interface algorithm system based on lower limb motor imagery, relates to the technical field of brain-computer interfaces, and is technically characterized in that a high-precision lower limb motor imagery brain-computer interface system based on self-adaptive time frequency-Riemannian geometry fusion is adopted, and through optimal frequency band selection and covariance manifold classification, a high-precision lower limb motor imagery brain-computer interface algorithm is obtained. Robust decoding of the motion intention of the patient suffering from knee joint pain is achieved, and reliable technical support is provided for clinical rehabilitation.

The invention discloses a control method, and the method comprises the steps: collecting the data of a centroid state and an MPC optimal ground acting force in a plurality of motion states of a robot, designing a full-connection residual network, taking the centroid state information as the input, taking the ground acting force as the output, and designing a loss function based on priority. Upper and lower bounds of a friction cone and a sole reaction force are constrained, and the network is trained offline to fit MPC optimization quantity, so that an efficient mode of online reasoning is realized; the joint angle and the angular velocity are fused with the action at the previous moment, the centroid linear velocity and the angular velocity and the foot-ground acting force fitted through a residual network, a bionic reference trajectory is generated through a human motion public data set, different weights are given to a reward function group according to a dominant function value obtained through calculation, and the control performance is improved; symmetrical loss constraint is added to lower limb movement, and the algorithm convergence speed is increased. And finally, training to obtain a whole-body control strategy based on model priori knowledge and a human motion reference trajectory.

The invention discloses a weight loss gait test evaluation and training device and method, and the device comprises a weight loss control module which is used for weighing the weight of a user in real time and automatically adjusting the air pressure of an air bag according to a set weight loss proportion; the gait information acquisition module is used for synchronously acquiring plantar pressure distribution, lower limb joint kinematics parameters and double-view video data; the multi-modal gait evaluation grading model module is used for processing plantar pressure distribution, lower limb joint kinematics parameters and double-view video data and outputting lower limb dyskinesia grades; and the training scheme generation model module is used for automatically generating a training scheme including a weight reduction proportion, a treadmill speed, single exercise time and training times per week according to the lower limb dyskinesia level and the personal information of the user.

Disclosed in the present invention is a lower-limb-powered exoskeleton device for rehabilitation assistance, comprising ankle-joint sagittal-plane driving units, knee-joint sagittal-plane driving units, hip-joint sagittal-plane driving units, a hip-joint horizontal-plane driving unit, a joint driver connecting frame, a lower-limb fixing support, sensors, a controller and a power supply. Antagonistic-drive-type linear driving units are used in the sagittal plane of a hip joint so as to enable the bending and extension movements of the hip joint, and a linear driving unit having the function of self-locking when powered off is used in the horizontal plane of the hip joint so as to enable the turning movements of the exoskeleton; and a linear driving unit in cooperation with a magnetorheological brake is used in the sagittal plane of each knee joint, thereby significantly reducing the power consumption of a knee-joint driver while lowering the control difficulty. The powered exoskeleton device can help patients with an impaired lower-limb motor function to carry out walking rehabilitation training.

The invention relates to the technical field of cardiovascular function enhancement, and provides a cardiovascular function enhancement device for preventing cardiovascular diseases in order to solve the problems that an existing cardiovascular function enhancement device can only carry out one type of exercise, and the balance weight cannot be adjusted according to personal requirements during training. Comprising a base and a bottom plate arranged on the base, and further comprises a supporting seat arranged on the bottom plate; the load mechanism is arranged above the supporting seat, penetrates through the supporting column and is used for providing a resistance load; the pedal mechanism is arranged in the middle of the base and is used for the patient to tread to generate force to simulate lower limb movement; and the pulley mechanism is used for connecting the pedal mechanism and the load mechanism to realize synchronous matching between the pedal mechanism and the load mechanism. According to the cardiovascular function training device, simulation of multiple motion modes can be provided, and the resistance load can be flexibly adjusted according to personal requirements, so that precise and personalized cardiovascular function training is achieved.

The invention discloses a lower limb movement monitoring and guiding method. A hardware part comprises a flexible induction sheet array, a smart phone, a built-in RGB-D camera of the smart phone and an edge calculation module. The flexible induction sheet array comprises five flexible induction sheets, each flexible induction sheet comprises a skin contact layer, a sensor layer, a flexible FPC substrate and a protective layer, the sensor layer is provided with a three-axis MEMS acceleration sensor, a PVDF piezoelectric film sensor, a flexible FPC substrate integrated signal conditioning module and a Bluetooth communication module; the flexible induction sheet is provided with an anatomical positioning mark; the lower limb movement monitoring and guiding method comprises the following steps: S1, cleaning; s2, a step of attaching a flexible induction sheet; s3, an automatic positioning calibration step; s4, a data transmission step; s5, a data fusion and calculation analysis step; s6, a normalization evaluation step; and S7, a feedback and optimization step. Quantitative assessment of lower limb movement can be effectively achieved, and the monitoring and guiding effect is good.

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.

A lower limb motion intention prediction method based on multi-source signals comprises the following steps: firstly, analyzing motion characteristics of lower limbs of a human body, screening to-be-detected muscles, collecting electromyographic signals of a plurality of periods, and preprocessing the electromyographic signals; then linear features and nonlinear features of the electromyographic signals are extracted; the linear features and the nonlinear features of the electromyographic signals and motion capture information are fused through a series splicing method; a random forest method (RF-Bagging) is adopted to carry out feature selection on the feature matrix with the higher dimension; and finally, inputting the selected feature matrix by using a long short-term memory network (LSTM), performing joint continuous motion amount regression prediction according to the amputation type, and judging the motion intention recognition performance through various regression indexes. According to the method, joint continuous quantity prediction with a good effect under different amputation body types is realized.

The invention discloses a lower limb exercise load assessment method and system based on multi-mode wearable equipment, and the method comprises the steps: 1) collecting exercise signals, three-dimensional track information and actual ground reaction force when a testee runs at different set speeds, and synchronizing the exercise signals, the three-dimensional track information and the actual ground reaction force through timestamps; 2) calculating the tibia force of the testee; 3) training a tibia force and vertical ground reaction force estimation model; 4) inputting the motion signals collected in real time into the tibia force and vertical ground reaction force prediction model to obtain predicted tibia force and vertical ground reaction force; and 5) calculating to obtain a lower limb exercise load evaluation index. The system comprises a pressure insole device, a pressure leg sleeve device, a surface myoelectricity device, an inertial sensor device, a training data acquisition module, a tibia force prediction model construction module, a vertical ground reaction force prediction model construction module, a vertical ground reaction force prediction module, a tibia force prediction module and a lower limb exercise load evaluation module. Compared with other methods, the method provided by the invention has higher systematicness and scientificity.

The invention belongs to the field of motion prediction, and provides a cerebral apoplexy patient lower limb motion intention prediction method and system based on deep learning, and the technical scheme is as follows: obtaining a surface electromyogram signal before motion and an inertial measurement unit signal; obtaining a motion intention recognition result by combining the surface electromyogram signal before motion, an inertial measurement unit and the trained motion intention recognition model; the construction process of the motion intention recognition model comprises the following steps: extracting a plurality of discriminative features from a surface electromyogram signal before motion according to a preset discriminative rule; re-weighting the importance of the discriminative features in combination with a channel attention mechanism, and capturing key features related to biomechanics of each channel according to the importance degree; extracting a time sensing feature from the inertial measurement unit signal; and fusing the key features related to biomechanics and the time perception features of each channel to obtain fused features, and converting the fused features into motion probabilities. And accurate recognition of the motion intention is realized.

The invention discloses a multi-sensor motion evaluation method and system for special training of lower limbs. The method comprises the following steps: step S100, collecting data of various sensors of the lower limbs; step S200, based on the collected data, carrying out gait and support state identification; step S300, judging the state of foot varus and valgus; step S400, calculating a foot stability index; step S500, calculating a lower limb exercise ability index; step S600, carrying out special exercise evaluation; and step S700, generating training feedback. Compared with an optical system, the core advantages of the system in training are reflected in that the system can be used for real training instead of a forced performance type test; the device is suitable for long-distance and continuous special training; the technical stability and injury risk can be directly reflected; and the training evaluation cost and the use threshold are obviously reduced.

The invention relates to a lower limb motor function recovery system and method based on a motor imagery brain-computer interface, and the system comprises a noninvasive electroencephalogram signal collection module which is used for collecting an electroencephalogram signal when a patient carries out a lower limb motor imagery task; the signal processing and intention recognition module is used for carrying out feature extraction and classification recognition on the collected electroencephalogram signals and outputting the motor imagery intention of the patient; the multi-sensory feedback module is used for providing multi-sensory feedback information including visual sense, auditory sense, tactile sense and proprioceptive sense for the patient; the lower limb rehabilitation training module is used for executing lower limb rehabilitation training actions corresponding to the motor imagery intention; the central control module is configured to receive the recognition result of the motor imagery intention and generate a control instruction according to the recognition result to drive the multi-sensory feedback module and the lower limb rehabilitation training module to work cooperatively. According to the system, a multi-sensory collaborative feedback mode is adopted to simulate multi-sensory interaction, and the identification degree and training compliance of a motor imagery task of a patient are improved.

The invention relates to the technical field of rehabilitation medicine, and discloses an array type pneumatic contact lower limb movement function identification and evaluation device which comprises a base, an adjustable seat is arranged at the top of the base, a fixed seat is fixedly connected to the top of the base, an auxiliary mechanism is arranged at the top of the fixed seat, an identification mechanism is arranged at the top of the base, and a pneumatic contact is arranged at the top of the identification mechanism. The identification mechanism comprises mounting boxes, the bottoms of the two mounting boxes are fixedly connected to the top of the base, follow-up assemblies are arranged in the mounting boxes, the other ends of the follow-up assemblies are rotationally connected with fixing rings, hook-and-loop fasteners are arranged at the two ends of the fixing rings, and a plurality of air cylinders are fixedly connected to the interiors of the fixing rings. According to the invention, through the design of the array type pneumatic contacts, the detection precision is improved, errors caused by looseness or missing detection of the contacts are avoided, and making of a personalized rehabilitation scheme, such as targeted training for a patient with asymmetrical muscle strength, is supported.

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 a closed-loop control method suitable for personalized lower limb exercise training, belongs to the technical field of exercise rehabilitation, and designs a closed-loop control method for dynamically adjusting lower limb training load according to muscle activation degree by combining surface myoelectricity characteristics with a personalized training model and a closed-loop control algorithm. According to the method, the resistance load can be adjusted in real time according to the muscle state, and finally the muscle activation level in the training process is maintained in an expected range. In the pre-training stage, sEMG data are collected to establish a personalized mapping model, and response characteristics of individual muscles are quantified; in the formal training stage, the muscle state is monitored in real time, the reference load is predicted through the model, and a closed-loop control algorithm is adopted to generate dynamic adjustment amount to adjust resistance output in real time. The invention further provides a closed-loop control system suitable for personalized lower limb exercise training.

The application provides a kind of rehabilitation auxiliary device after vascular intervention, including seat plate and exercise mechanism, and the exercise mechanism is symmetrically arranged on both sides of the seat plate, and the exercise mechanism includes pedal, resistance mechanism and protection mechanism.By treading pad, the first spring is compressed and shrunk, and the lock rod is separated from the currently matched lock hole by the first transmission assembly, at this time, treading pad and pedal can be driven to move away from the seat plate to resist the resistance mechanism to pedal and exert the thrust to move close to the seat plate, to achieve the purpose of lower limb exercise, when the foot accidentally slips off from the pedal or the heel is raised, the pressure on the pad disappears, the reaction force of the first spring makes the pad move away from the pedal and drives the lock rod to be arranged in the currently matched lock hole to fix the pedal, so that when the foot accidentally slips off from the pedal or needs to be terminated due to accident, the pedal can be fixed in time, to avoid the pedal rebounding and causing harm to the user, and to improve the safety of the device.

The invention discloses a lower limb multi-joint torque intelligent prediction method and related equipment, and relates to the technical field of joint torque prediction.The method comprises the steps that original surface electromyography sEMG, kinematics data and ground reaction data of a subject under different lower limb movements are collected; kinematics data and ground reaction force data are used as input, and multi-degree-of-freedom angle data and moment data of three joints of the lower limb are obtained through inverse kinematics and inverse dynamics calculation; constructing a lower limb multi-joint torque prediction model through a deep learning technology, and performing model training by using the training set; and inputting the test sample set into the trained lower limb multi-joint torque prediction model to obtain a joint torque prediction result of the subject. According to the method, the problems of low joint torque prediction precision, high calculation cost and the like in the prior art are solved, the method has the advantages of being easy to implement, high in generalization, capable of processing large-scale data and the like, and prediction results are expected to be applied to three-dimensional compliance and modular control of the exoskeleton robot.

The present disclosure provides a movement system. The movement system includes a foot-plate exercise machine and a chair. The foot-plate exercise machine implements repetitive lower limb movement accompanied by extension and flexion of at least a knee joint and a hip joint. The chair is a component for a user to sit on while the user uses the foot-plate exercise machine to perform the lower limb movement. The chair has a seating portion for the user to sit on, and a support mechanism that supports the seating portion in a manner that allows the seating portion to rock laterally. By the seating portion rocking laterally during the lower limb movement, a movement chain of the lower limb and the torso is generated.

The present invention discloses a real-time analysis method and system for lower limb motion data applied to rehabilitation guidance. The method calls a personalized model based on user information, converts real-time motion data into historical motion feature vectors, inputs them into a real-time prediction module, and generates multiple frames of future motion feature vectors through encoding and decoding. The deviation value of each future motion feature vector from a preset threshold range is calculated to obtain a motion deviation value, and the motion risk ratio of each future motion feature vector is calculated. The motion risk probability is obtained through weighted fusion, and the device parameter adjustment value and posture adjustment information are generated through reverse mapping. Finally, the training stage is advanced based on the risk probability assessment, and correction information is synchronously displayed and the device operating parameters are adjusted. This method constructs a dynamic model through pre-training and combines it with a lightweight LSTM to achieve real-time prediction of future motion features, thereby improving the predictability and personalized adaptation capabilities of rehabilitation guidance.

1. Name of the Design Product: Limb Movement Rehabilitation Instrument. 2. Use of the Design Product: For lower limb movement rehabilitation. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective View 1.

The invention provides a control method of an upper and lower limb integrated robot, which comprises the following steps of: realizing motion control of lower limbs according to data of force sensors on two sides of a hip joint; in the lower limb movement process, left and right leg recognition is performed; according to the left and right leg identification information and the lower limb movement speed, the angular velocity of upper limb shoulder and elbow joint movement is estimated, so that upper limb movement is controlled, and upper and lower limb movement coordination control is realized. Left and right leg recognition is carried out in the lower limb movement process, the angular speed of upper limb shoulder and elbow joint movement is estimated according to left and right leg recognition information and the lower limb movement speed, accordingly, upper limb movement is controlled, the upper limbs and the lower limbs of a patient can be trained at the same time, upper limb and lower limb coordinated movement is achieved, and a better rehabilitation training effect is achieved.