354 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 discloses a lower limb movement gait recognition method and system based on feature fusion, and belongs to the technical field of gait recognition. Comprising the following steps: acquiring a surface electromyogram signal and a motion inertia signal of a to-be-predicted object; carrying out feature extraction on the surface electromyogram signal and the motion inertia signal by utilizing wavelet transform, obtaining gait features and screening an optimal feature combination; and processing the optimal feature combination through the trained gait recognition model to obtain a gait recognition result. Complex multi-modal data can be effectively processed and analyzed, so that high-precision gait recognition and evaluation are realized; the problem that the accuracy of gait evaluation needs to be improved due to the fact that single signal processing is difficult to comprehensively reflect a complex gait dynamic process in the prior art is solved.

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 rehabilitation lower limb exoskeleton electroencephalogram decoding method based on integrated learning, and relates to the technical field of electroencephalogram decoding, and the method comprises the steps: obtaining an EEG signal of a motor cortex of a user through an electroencephalogram device; performing shallow layer processing on the EEG signal to obtain a first type of features; constructing a deep processing network model of the EEG signals, and performing deep processing on the EEG signals to obtain a second type of features; combining the first type of features and the second type of features, training a classifier, and decoding the motion intention of the EEG signal; and performing visual feedback on the user according to the decoded motion intention, and then executing the decoded motion on the lower limb exoskeleton equipment to obtain proprioceptive feedback. The superficial neural network and the deep neural network are combined to improve the performance of lower limb movement intention, continuous motor imagery experiments are carried out on lower limb exoskeleton equipment, continuous EEG electroencephalogram signals are collected, and an experiment normal form of online vision and proprioceptive feedback is designed.

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 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 lower limb exoskeleton control weight distribution prediction method based on artificial intelligence. Comprising the following steps that a, biomechanical data, including but not limited to joint angles, muscle activities and ground counter-acting force, of the lower limbs of a wearer are collected through a plurality of sensors; b, a deep learning model is utilized, and the model is trained through a large amount of lower limb movement data in advance to recognize weight distribution characteristics in different movement modes; c, according to the collected biomechanical data, the model predicts the weight distribution borne by each part of the lower limb in real time; and d, according to a prediction result, intelligently adjusting the supporting force of each joint of the exoskeleton so as to optimize the gait of the wearer and reduce energy consumption. Energy consumption and exercise efficiency are optimized, specifically, according to the predicted weight distribution, the supporting strategy of the exoskeleton is intelligently optimized, the energy consumption of the wearer in the exercise process is reduced, and the exercise efficiency is improved. And by reducing unnecessary energy waste, the service life of the battery is prolonged, and the practicability of the exoskeleton is enhanced.

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 discloses an electrical stimulation signal control method and system for a foot drop nursing and rehabilitation training device, and the method comprises the steps: obtaining an uninjured-side electromyographic signal and an uninjured-side kinematics signal of a target object, carrying out the signal filtering processing of the uninjured-side electromyographic signal and the uninjured-side kinematics signal, obtaining a filtered electromyographic signal and a filtered kinematics signal, and transmitting the filtered electromyographic signal and the filtered kinematics signal to the target object; performing signal generation processing according to the filtered electromyographic signal to obtain an initial electrical stimulation signal, obtaining an affected-side kinematics signal of the target object under the initial electrical stimulation signal, performing comparison processing on the affected-side kinematics signal according to the unaffected-side kinematics signal, and performing adjustment processing on the voltage amplitude of the initial electrical stimulation signal according to a comparison result to obtain the target object. And obtaining a target electrical stimulation signal. The embodiment of the invention can stimulate lower limb muscles at a proper time, improve the movement quality of lower limbs, improve the accuracy of electrical stimulation signal control, and can be widely applied to the technical field of signal processing.

The invention belongs to the technical field of surgical instruments, and particularly relates to a multifunctional neurology patient rehabilitation device. The invention provides a multifunctional neurology patient rehabilitation device. The multifunctional neurology patient rehabilitation device comprises an exercise bed and an upper limb exercise mechanism. A lower limb exercise mechanism; and a hand exercise mechanism. According to the multifunctional rehabilitation device for the neurology patient, the upper limb exercise mechanism can assist the patient in arm joint rotation, the lower limb exercise mechanism can assist the patient in leg joint movement, the multi-directional operation effect is achieved through the multi-dimensional rotation function, the movable dimension of the joints of the patient is wider, and the rehabilitation effect of the patient is improved. The hand exercise mechanism is arranged, the patient can be assisted in moving all joints of the fingers, the continuous sectional type movement effect is achieved, manual work is completely replaced through the scheme, the cost can be saved, the effect of simultaneous, accurate and continuous movement of the multiple joints can be achieved, and then the rehabilitation speed of the patient is increased.

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 discloses a lower limb motion function evaluation method, system and device based on deep learning image processing and a storage medium, and the method comprises the steps: shooting a lower limb motion process of a patient through a common mobile device, and collecting a motion image; performing image preprocessing to obtain a preprocessed action image data set; inputting the motion image data set into the trained progressive mode model, extracting feature points of a joint three-dimensional space, and calculating parameters related to a lower limb motion function according to the feature points; the parameters obtained through real-time calculation and set parameters are subjected to fitting calculation to calculate the overlap ratio, rehabilitation training is evaluated in real time according to the overlap ratio, and an optimized motion curve is given according to the real-time evaluation result to guide a patient to conduct rehabilitation; an image processing algorithm is optimized, and a line-by-line processing mode is adopted to replace a current full-image processing mode, so that evaluation and analysis of lower limb movement under shooting of a common camera in a mobile device are realized, and remote medical diagnosis is completed quickly and efficiently.