383 results about "Plantar pressure" patented technology

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses a teenager physical exercise real-time monitoring system, and relates to the technical field of intelligent monitoring. An individualized health assessment model is constructed through space-time alignment processing of joint three-dimensional movement, electromyographic signals and plantar pressure and a bone age dynamic matching technology. Time sequence deviation is eliminated through unified clock marking and interpolation compensation, parameters such as bone stress deviation degree, muscle synergy asymmetry degree and plantar pressure unbalance degree are extracted, and comprehensive health indexes are generated through weighting. And in combination with a preset training path model, analyzing a health index trend in real time and triggering a grading adjustment instruction, dynamically optimizing training intensity, interval and action schemes, and correcting path model parameters through a closed-loop feedback mechanism to realize adaptive updating. The defects that traditional teenager physique monitoring data fusion is large in deviation and static evaluation hysteresis is high are overcome, real-time accurate monitoring and personalized training optimization of teenager physique exercise are achieved, and the sport injury risk is effectively reduced.

The invention relates to the technical field of biological feature recognition, and discloses a neural reflex motion feature extraction and quantitative evaluation method, which comprises the following steps that: multi-modal behavior data of a premature infant are synchronously acquired through a sensor array, and the multi-modal behavior data comprise a motion inertia signal, optical tracking data and plantar pressure distribution data; preprocessing the multi-modal behavior data, and performing dynamic feature fusion on the preprocessed multi-modal data to generate a joint feature vector; extracting a motion symmetry index and a reflection intensity grade from the joint feature vector; and inputting the joint feature vector, the motion symmetry index and the reflection intensity level into a hierarchical rule engine, and executing risk judgment of a behavior pattern. The invention also provides a neural reflex motion feature extraction and quantitative evaluation system. According to the invention, the accuracy of neural reflex motion feature extraction and quantitative evaluation can be improved.

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

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.

The invention provides an intelligent wearable rehabilitation monitoring and evaluation device after anterior cruciate ligament injury, and relates to the technical field of exercise rehabilitation and intelligent medical monitoring, the device comprises a sensor acquisition module, a signal processing module, a compensation determination module, a difference determination module and an evaluation analysis module, a force-electricity coupling flexible sensor, an IMU and a pressure sensor, the method comprises the following steps: synchronously collecting a muscle force signal, a surface electromyogram signal, an attitude signal and a plantar pressure signal at the same position, performing dynamic filtering, confidence enhancement and personalized compensation processing, extracting a difference feature vector, inputting the difference feature vector into a muscle force prediction model, and outputting a quantitative evaluation result. The portable wearable long-term dynamic monitoring is realized, the core indexes of muscles, joints and gaits are covered in multiple dimensions, the assessment is accurate and real-time, the rehabilitation scheme can be optimized, the risk of re-injury is reduced, and the rehabilitation effect and compliance of a patient are improved.

According to the human body activity distinguishing device and method, through an intelligent insole system integrating an inertial sensor and a plantar pressure sensor and in combination with a multi-scale time window analysis method, gait data of a user are accurately collected and processed, daily activity modes such as walking, running and going upstairs and downstairs and changes of the modes are recognized, and the human body activity distinguishing effect is achieved. And real-time health feedback and rehabilitation support are provided. The intelligent insole system comprises an upper computer system, sensor integrated insole hardware and a module communicating with a user terminal and a cloud server. According to the system, multi-modal data is collected through an inertial sensor and a plantar pressure sensor, data processing is achieved in combination with data fusion and a multi-scale sliding time window method, finally, a random forest classifier is used for classifying activity modes, and the functions of health monitoring, posture improvement suggestion, falling risk assessment and the like are achieved. The exercise performance of the user is improved, and personalized health management is provided.

The invention provides a gait analysis method and an early warning system based on multi-source data fusion, three types of original signals are collected through a wearable inertial sensor, a plantar pressure insole and an edge calculation camera, and multi-dimensional motion characteristic parameters are fused through wavelet denoising, low-pass filtering, interpolation alignment and data consistency correction, so that the gait analysis method based on multi-source data fusion is realized. And constructing a user individualized gait feature prototype library. The method further adopts a dynamic causal graph network to model a motion relation between joints, determines an abnormal coupling mode based on causal weight and historical reference, and combines activity context and Bayesian rules to generate a dynamic threshold value to realize risk grading early warning, so that heterogeneous data collaborative analysis precision and abnormal gait detection sensitivity are improved, and the method is suitable for large-scale popularization and application. And support is provided for gait health monitoring and personalized risk management and control under multiple scenes.

The invention discloses a psychological state assessment method and device. The method comprises the steps that firstly, based on a sensor group and a camera module, electrocardio information, face information, gait information and plantar pressure information of a target are obtained; then constructing an input feature vector based on the electrocardio information, the face information, the gait information and the plantar pressure information; and finally, inputting the input feature vector into a preset model to obtain an evaluation result. According to the embodiment of the invention, the multi-dimensional feature data can be processed, so that the psychological state is evaluated by using the multi-dimensional feature data to obtain the evaluation score, and the evaluation accuracy can be improved.

The invention discloses a wearable plantar pressure and three-dimensional gait analysis system, and relates to the technical field of gait analysis, the system comprises a wearable in-shoe plantar pressure analysis subsystem and a three-dimensional gait analysis subsystem; the wearable in-shoe plantar pressure analysis subsystem comprises a pressure sensor module, a pressure integration module, an intelligent transmission module, a detection software module, a power supply module, an instantaneous pressure and balance detection module, a standing balance detection module, a balance and gait detection module and a cloud server; the three-dimensional gait analysis subsystem comprises a hardware unit and a software unit; according to the invention, a wearable plantar pressure and three-dimensional gait analysis collaborative system is constructed, bidirectional verification compensation is realized by means of data fusion of two subsystems, and traditional single-dimensional limitation is broken; pressure sensing and a double-view reconstruction technology are fused, data are dynamically captured, and bidirectional error correction is carried out; the limitation of traditional equipment is solved, the evaluation accuracy and robustness are improved, and a quantitative basis is provided for clinical diagnosis, rehabilitation and the like.

The invention relates to the technical field of medical assistance, and discloses a flatfoot sole model generation system and device, which comprises a plurality of air bag elements, a pressure data acquisition module, a motion capture module, a static data processing module, a dynamic data processing module, an execution control module and a model generation module, through the synergistic effect of the air bag element, the pressure data acquisition module and the static and dynamic data processing module, in combination with real-time inflation adjustment and foot arch state judgment, the plantar model adaptive to the foot characteristics of the user is dynamically generated, plantar pressure distribution can be monitored and adjusted in real time, the plantar model is dynamically generated according to the foot motion state of the user, and the user experience is improved. The method has the advantage of improving the flatfoot correction effect and the user experience.

The invention is suitable for the technical field of medical instruments, and provides a pneumatic global self-adaptive ankle-foot artificial limb which comprises a rear sole and a front sole which are connected through a front and rear foot connecting piece. The pressure response assemblies are located at the bottoms of the rear sole and the front sole, and pressure changes after bearing; and the ankle self-adaption assembly is connected with the pressure response assembly through a valve communication assembly and drives the ankle to do eversion, centering and introversion motions based on pressure changes. According to the embodiment of the invention, an ankle joint inward and outward turning automatic adjusting mechanism directly driven by plantar pressure change can enable the artificial limb to adapt to complex terrains such as uneven road surfaces and lateral slopes in real time, an inward and outward turning motion mode of a physiological ankle joint in the walking process is simulated, and the gait of a user is smoother and more natural; the pure pneumatic control structure is relatively simple and light, and an external power supply pressure driving pump or a complex sensor is not needed.

The invention provides a skiing posture correction method and device, and relates to the technical field of skiing teaching, and the method comprises the steps: synchronously collecting plantar pressure distribution characteristic data and ankle joint inclination data of a skier; when the plantar pressure distribution data and / or the ankle inclination data meet the preset unbalance judgment condition, an unbalance adjusting signal is triggered; the imbalance adjustment signal includes at least one of a haptic alert signal related to a severity of the imbalance, a dynamic visual signal including a pressure distribution characteristic, and a sound indication signal having a joint angle quantitative indicator. According to the invention, the skiing posture of the skier can be intelligently and accurately corrected, and the correction efficiency of the skiing posture is improved.

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The present application relates to the field of medical devices, and provides a back plate adjustable exoskeleton, comprising: a column structure and a control module, the column structure is slidably connected with a back plate mechanism, and a lower limb exoskeleton connected with the back plate mechanism, and the back plate mechanism is provided with a back plate which can move up and down relative to the back plate mechanism; further comprising: a binding device arranged on the lower limb exoskeleton and a foot bottom exoskeleton, the foot bottom exoskeleton is provided with a pressure sensor. When the human body wears the exoskeleton and changes from a sitting posture to a standing posture, the back plate mechanism adjusts the back plate mechanism along the column structure to the height matched with the user according to different body shapes of the user; and when the user changes the posture in the walking process in the standing posture, the foot bottom exoskeleton detects the change of the foot bottom pressure, so that the control module can control the up and down movement of the back plate relative to the back plate mechanism according to the foot bottom pressure to adjust the human body posture.

The invention discloses a composite type four-foot wall climbing robot integrating a fish suction cup adsorption mechanism and lizard crawling characteristics. The problems that a traditional wall climbing robot is poor in adsorption stability and insufficient in moving flexibility on uneven and wet wall surfaces are solved. The core structure of the robot comprises an adsorption sole assembly, a multi-degree-of-freedom leg joint, a bionic spine, a micro air pump and a sole pressure sensor, and the adsorption sole assembly controls an air bag through the air pump to generate negative pressure to adapt to various wall surfaces; multiple joints of the legs are matched with the spherical joints to achieve multi-angle attachment of the foot sole, the bionic spine can be bent and buffered, lizard diagonal gaits are adopted, the rotation angle of the joints is adjusted in combination with a sensor, self-adaptive movement on the wall with the inclination angle ranging from 0 degree to 90 degrees is achieved, and the efficiency is improved by 30% or above compared with a traditional robot. The robot breaks through the limitation of a single bionic mechanism, can be applied to complex high-altitude scenes such as building outer wall detection, and reduces the manual operation risk.

The invention discloses a badminton player action posture analysis system and method based on multi-sensor fusion, and relates to the technical field of athletic training auxiliary systems.The system comprises a wearable sensing subsystem, an environment sensing subsystem and a central processing and feedback subsystem; the wearable sensing subsystem is arranged on an inertia measurement unit and a pressure sensing insole of a body to collect movement inertia and plantar pressure data; the environment perception subsystem collects global videos through multiple cameras. The central processing and feedback subsystem performs synchronous processing on multi-source data, adopts a hierarchical fusion algorithm, restrains inertia integral drift by utilizing a plantar contact state, reconstructs a three-dimensional skeleton posture and a motion trail in combination with visual key points and skeleton restraint, drives a digital twin model, compares with a standard motion model, and performs three-dimensional motion control on the three-dimensional skeleton posture and the motion trail. Quantitative evaluation results such as joint angle deviation, time sequence difference and pressure center track are generated, and visual feedback is performed through a display device and an augmented reality terminal for badminton training evaluation and technical deviation correction.

The invention relates to the technical field of lower limb rehabilitation training, and discloses an intelligent load lower limb rehabilitation training system fused with a multi-source sensor. The system comprises a multi-source sensing module, a motion intention recognition module, a self-adaptive track generation module, a real-time load regulation and control module and an actuator driving module. The multi-source sensing module synchronously collects plantar pressure, joint angles and electromyographic signals during training of a user through a plantar pressure sensor array, a knee joint angle sensor and a surface electromyographic sensor; the motion intention recognition module extracts time domain and frequency domain features from the multi-source data, and obtains a real-time motion intention through classification; the self-adaptive track generation module generates an exclusive three-dimensional motion track in combination with the real-time intention and historical rehabilitation data of the user; the real-time load regulation and control module generates a self-adaptive load control signal through a fuzzy decision algorithm; the actuator driving module adjusts the magnetic powder brake through PWM current, outputs corresponding real-time resistance torque, and assists in optimizing the rehabilitation training effect.

The invention relates to the technical field of pressure monitoring, and discloses a method and system for motion state analysis according to plantar pressure, and the method comprises the steps: receiving multi-modal biomechanical data related to a landing event; extracting a corresponding motion feature vector from the multi-modal biomechanical data; and inputting the motion feature vector into a motion state analysis model for identification to obtain a corresponding damage risk index, and outputting the corresponding damage risk index. By receiving pressure distribution data detected by the plantar pressure sensing array, shear force data detected by the shear force measuring and calculating module and a kinematics data sequence detected by the inertial measurement unit, biomechanical information in the movement process can be comprehensively captured from multiple dimensions; compared with a single data acquisition mode, the fusion of the multi-modal data can reflect the real state of the human body in motion more accurately, and a richer and more accurate data basis is provided for subsequent motion state analysis.

The invention provides a knee joint dyskinesia function evaluation method and system. The method comprises the following steps: by collecting lower limb kinematics parameters, plantar pressure distribution data and gait rhythm parameter groups of a subject in a standard gait test, generating a knee joint motion trail map comprising sagittal plane, coronal plane and rotation trail components, dividing a dynamic phase window based on the gait rhythm parameters, calculating a fluctuation feature vector of a sagittal plane track component, performing nonlinear decomposition on a coronal plane and a rotation track component to separate an adduction and abduction motion principal component and a rotation coupling component, constructing a joint stability quantitative index, and performing dynamic space registration on a bilateral knee joint track map to obtain a joint stability quantitative index; and calculating a three-dimensional trajectory similarity weight matrix, and calculating a dynamic symmetry index in combination with the cross-side difference coefficient of the joint stability quantitative index. According to the technical scheme provided by the invention, a scientific basis is provided for personalized rehabilitation treatment.

The invention discloses a sleep-aiding linkage system based on ankle recovery monitoring, and relates to the technical field of medical health and intelligent monitoring. According to the sleep-aiding linkage system based on ankle recovery monitoring, rich biomechanical parameters of an ankle area are collected in real time through a multi-mode sensor; the method comprises the steps that plantar pressure distribution, ankle joint motion trails, foot temperature and skin impedance are covered, the data are subjected to multi-dimensional fusion analysis, calculation is conducted in combination with a built-in neural network model, the ankle fatigue state and the recovery stage can be accurately analyzed, the method is different from a traditional fixed threshold value judgment mode, and the system can be dynamically adjusted according to individual differences of users and historical data; and furthermore, the sleep-aiding equipment linkage control module is used for finely adjusting operation parameters of mattress hardness, environment illumination intensity and sound wave frequency for sleep monitoring equipment and environment temperature control equipment according to an analysis result, so that an exclusive sleep environment matched with an ankle recovery process is created for a user.

The invention provides an old people falling risk monitoring analysis method and system, and the method comprises the steps: collecting multi-source heterogeneous signals, such as plantar pressure distribution, attitude trajectory output by an inertial measurement unit, electroencephalogram attention concentration ratio, galvanic skin response and the like, carrying out data synchronization and spatial normalization processing through a unified timestamp, and carrying out data synchronization and spatial normalization processing; extracting gait gravity center tracks and physiological state features; the gait deviation degree is matched by constructing an individual behavior baseline library and a dynamic time warping algorithm, the cognitive load state is evaluated in combination with a lightweight LSTM network, physical instability and cognitive risks are subjected to multi-dimensional fusion, a falling risk score is output through a fuzzy logic engine, and the system can achieve graded early warning response and emergency positioning. According to the invention, the accuracy, adaptability and early warning efficiency of fall risk assessment are significantly improved.

The invention relates to a tumble risk assessment method and system based on a multi-modal deep learning network, and relates to the technical field of human body posture recognition, and the method comprises the steps: collecting a scene motion image sequence, a plantar pressure feature sequence and personnel basic information; analyzing the scene motion image sequence by using a human body posture estimation network model to determine a human body key feature point sequence; performing feature extraction on the human body key feature point sequence and the scene motion image sequence to generate human body posture sequence features; performing feature extraction fusion on the human body key feature point sequence and the plantar pressure feature sequence to generate human body motion fusion time sequence features; performing feature extraction on the personnel basic information to generate personnel basic information features; and inputting the human body posture sequence features, the human body motion fusion time sequence features and the personnel basic information features into a multi-modal deep learning network for analysis so as to determine a fall risk score. The method and the device have the effect of improving the precision of fall risk assessment.

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.

The invention discloses a knee-ankle power-assisted exoskeleton based on a metamorphic principle and a control method of the knee-ankle power-assisted exoskeleton. The knee-ankle power-assisted exoskeleton comprises a control system, a waist driving mechanism, a waist transmission mechanism, a waist input knee joint transmission mechanism, a waist input ankle joint transmission mechanism and a plantar pressure sensing device. The waist transmission mechanism is a metamorphic mechanism and is in transmission connection with the input end of the waist input knee joint transmission mechanism and the input end of the waist input ankle joint transmission mechanism through two Bowden rope sets. And the metamorphic mechanism outputs power according to three preset configuration circulation modes. Automatic switching of different power assisting modes is achieved; and the output end of the waist input knee joint transmission mechanism is also provided with a knee joint variable stiffness mechanism. The metamorphic mechanism is adopted, the knee joint and the ankle joint are driven through a single motor, the overall weight of the exoskeleton is obviously reduced, and the power assisting efficiency is improved; intelligent switching of the exoskeleton in different gait stages can be achieved, and the power assisting mode can be dynamically adjusted.

The invention discloses an intelligent tracking method and system for the rehabilitation state process of an orthopedic patient, and relates to the technical field of medical health. By integrating the wearable device, vital signs, motion states and pressure distribution data of a patient are collected and analyzed in real time, the rehabilitation state of the patient can be accurately monitored, and dynamic progress evaluation is provided; based on a rehabilitation progress evaluation result, the system can automatically generate a personalized rehabilitation exercise scheme, exercise intensity, exercise frequency and a training plan are adjusted according to specific requirements of each patient, and high efficiency and safety of the rehabilitation process are ensured; through analysis of vital sign fluctuation, motion precision and plantar pressure balance, the system can identify potential risk factors in real time and trigger early warning in time to help a patient and nursing personnel thereof to take necessary intervention measures, and smooth proceeding of the rehabilitation process is ensured.

The invention belongs to the technical field of shoe preparation, and relates to a functional shoe with a plantar pressure detection function and a preparation method, a shoe body is composed of an upper, a composite sole and an insole, and a plantar pressure detection system is integrated in the insole. The plantar pressure detection system sequentially comprises an antibacterial contact layer, a flexible sensing layer, a buffer layer, a circuit sealing layer and an anti-skid substrate layer from a contact surface to a substrate, the composite sole is composed of a rubber outsole, an EVA secondary foaming midsole and a PE anti-puncture layer. According to the functional shoes and boots provided by the invention, the insoles are formed by compounding the PU and the PORON, so that the functional shoes and boots have good buffering and supporting performance; the patterns of the shoe sole are optimally designed, so that the shoe sole has good anti-skid performance and water and sediment discharging performance; the preparation process is simple, the use requirements in a complex environment can be met, and better protection and comfort are provided for a user.

The invention belongs to the field of medical health, and provides a preliminary sarcopenia screening method based on paired foot pressure data, which comprises the following steps: firstly, acquiring foot pressure signals of left and right feet of a subject in a walking process to obtain corresponding foot pressure characteristics of the left and right feet; fusing by adopting a multiplicative combination mode to obtain total pressure characteristics of each walking stage in each walking period; then, a GMM model is adopted to create a plantar pressure mode, plantar pressure mode features are obtained, attention mechanism fusion is adopted to obtain single-cycle fusion pressure mode features and multi-cycle fusion pressure mode features, and time sequence features are obtained through learning of an improved GRU module; and finally, sending the corresponding multi-cycle fusion pressure mode features and the time sequence features into a multi-layer perceptron, and outputting the prediction probability of the sarcopenia. The method fully considers the independence of the left and right foot pressure signal features, introduces the joint relation of the two features, more comprehensively learns the gait features, and effectively improves the accuracy of primary screening of sarcopenia.

The invention relates to the technical field of gait analysis, and discloses a Transform structure-based gait plantar pressure intelligent prediction system, which comprises a gait acquisition platform, a plantar pressure acquisition device, a gait kinematics acquisition device, a central processing server and a display terminal. The system obtains initial plantar pressure distribution through a lower limb multi-rigid-body chain type biomechanical model and foot-ground contact mechanics solution; and a gating prefix memory sliding window Transform is constructed in the central processing server, time sequence modeling and spatial correction are performed on the initial pressure matrix and the gait features, and a plantar pressure prediction result conforming to the biomechanical law is generated. The system effectively solves the problems that a traditional method is unstable in prediction, weak in generalization ability and lack of interpretability, plantar pressure prediction with higher precision is achieved, and the system can be used for gait evaluation, rehabilitation training and orthosis customization.