274 results about "Plantar pressure" patented technology

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.

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 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 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 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.

The invention discloses a child gait anomaly recognition feedback method and system based on plantar pressure data, and relates to the technical field of gait anomaly recognition, and the method comprises the steps: collecting the plantar pressure data of a target wearer through a sensor array, and carrying out the feature extraction of multi-dimensional gait feature parameters; comparing and analyzing the model with a child gait development standard model, and recognizing a gait abnormal mode; carrying out risk assessment on the target shoe wearer and dividing risk levels; performing gait analysis on the target wearer to generate an individualized gait analysis report; and performing gait correction analysis on the target wearer according to the individualized gait analysis report, and matching to generate a correction training course scheme. The technical problems that in the prior art, child gait evaluation lacks quantitative standards, detection intervention is disjointed, and personalized analysis based on a dynamic development model is lacked are solved, and the technical effects of achieving objective, accurate and intelligent recognition of child gait abnormity and improving the child gait health management level are achieved.

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

The invention discloses a therapeutic footwear system comprising a footwear body (101) with insole (102), midsole (103), and outsole (105), integrated with a multi-modal sensor array (110) including a plantar pressure matrix (111), inertial measurement unit (112), photoplethysmography sensor (113) and temperature sensor (114). An embedded processor (120) houses an adaptive sampling module (121), an edge-AI inference engine (122), a federated learning client (123), and memory (124) for local analysis. A feedback interface (130) with zonal haptic actuators (131) under medial forefoot (132), lateral forefoot (133), medial heel, and lateral heel (135) delivers corrective prompts. A power subsystem (140) comprising a battery management circuit (141), energy harvesting elements (142), and battery (143) sustains operation. The integration reduces latency, conserves energy, preserves privacy, and enables closed-loop therapeutic intervention during ambulation.

The invention relates to the technical field of training evaluation, in particular to an evaluation method and system based on functional actions and physical fitness, and the method comprises the steps: collecting an image when a user does an evaluation action; the action quality score after the user completes the assessment action is calculated, the action and physical fitness of the user are assessed according to the action quality score, and the action quality score is in positive correlation with the action execution score for completing the assessment action and the balance coefficient for completing the assessment action. The balance coefficient reflects the stability of plantar pressure distribution when the user completes the evaluation action. The method not only improves the accuracy and individuation level of posture evaluation, but also can flexibly adjust scoring key points according to different users and action types. Finally, the intelligent mirror has higher adaptability, real-time performance and guidance in scenes such as rehabilitation evaluation and physical fitness detection, and the training effect of the user is remarkably improved.

The invention provides a self-adaptive assistance system and method for adjusting an exoskeleton based on a sweat sensor, and the system comprises a sweat amount detection chip which is embedded into the surface of wearable flexible clothes, is in contact with the skin of a human body, and is used for converting the detected sweat amount into an electric signal, and further converting the electric signal into a control signal; the knapsack comprises a control system, a driving system and a sensing system, and is worn on the trunk of the human body after being connected with the wearable flexible clothing; the power assisting device comprises a power assisting boot which is worn on the foot of the human body and connected with the driving system through a Bowden cable; and the specific output value of the power assisting device is controlled through the control signal. Dynamic matching of exercise intensity and mechanical assistance is achieved through an amplification control mechanism driven by sweat amount, gait phase compensation fed back by plantar pressure is combined, the energy utilization efficiency is improved by 20% or above, the problem that a traditional exoskeleton physiological-physical interface is disjointed is effectively solved, and the energy utilization rate is increased by 20% or above. The assisting synchronism is improved, and meanwhile the wearing comfort degree and the motion freedom degree are remarkably improved.

The invention relates to the technical field of foot health and motion monitoring, in particular to a multi-motion-scene-oriented foot motion data acquisition and intelligent analysis guidance method, which comprises the following steps of: acquiring foot three-dimensional static form and plantar pressure data through a foot correction shaping box, and establishing a foot geometric model with an anatomical mark; then, a nine-axis inertial measurement unit and a pressure sensor at a shoelace knob in the shoe are used for collecting time sequence foot motion data of multiple motion scenes; extracting multi-modal features after fusion, inputting the multi-modal features into a pre-training multi-task model, and outputting exercise habit classification, technical action scoring and injury risk assessment; finally, a sports biomechanics knowledge base is adjusted, and personalized suggestions of posture correction, muscle strength training and equipment adjustment are generated. The method is suitable for various exercise scenes, accurate in data acquisition, comprehensive in analysis dimension and high in guidance suggestion pertinence, the exercise normalization of the user can be effectively improved, and the exercise injury risk can be reduced.

The invention relates to the technical field of disease monitoring, in particular to a multi-device collaborative disease risk prediction system. The system comprises a non-contact type signal receiving and transmitting device, a gait collecting device, an intelligent bracelet and a central processing unit, the non-contact type signal receiving and transmitting device captures macroscopic movement data of a user through a millimeter wave signal transmitting and receiving module, and the macroscopic movement data comprises periodic characteristics of gaits, movement tracks of four limbs and posture changes; the gait acquisition device acquires plantar pressure distribution data of a user through a pressure sensor array; the smart bracelet collects motion data of a user through an accelerometer and a gyroscope; and the central processing unit carries out space-time alignment on data of the non-contact signal transceiving device, the gait acquisition device and the intelligent bracelet through a timestamp synchronization algorithm, so that the disease risk is predicted according to the analysis data. The system supports all-weather and multi-scene monitoring, is suitable for disease risk assessment, and can early warn cardiovascular events and other health problems.

The invention relates to a data acquisition method and system based on a rehabilitation device, and relates to the field of data acquisition and processing.The method comprises the steps that man-machine interaction force data and plantar pressure distribution data are acquired; obtaining a man-machine interaction force value based on the man-machine interaction force data; if the human-computer interaction force value exceeds a preset human-computer interaction force threshold value, an active auxiliary mode of the rehabilitation device is triggered; if the human-computer interaction force value does not exceed the preset human-computer interaction force threshold value, obtaining plantar pressure distribution characteristics from the plantar pressure distribution data; generating a control feedback signal in combination with the human-computer interaction force value and the plantar pressure distribution characteristics; and controlling the rehabilitation device to perform auxiliary operation based on the control feedback signal. The training effect evaluation method and device have the effect of improving the accuracy of training effect evaluation.

The invention discloses a training mode determination method and system for a lower limb exoskeleton rehabilitation robot. The method comprises the steps that angle data, joint torque data and plantar pressure data when a patient executes multiple target actions are collected; according to the angle data and the joint torque data, calculating to obtain a joint motion range, and according to the angle data and the plantar pressure data, calculating a joint output torque; acquiring a pain score of the patient, and calculating a training mode score according to the joint motion range, the joint output torque and the pain score of the patient; and according to the training mode score, determining a training mode of the lower limb exoskeleton rehabilitation robot, wherein the training mode comprises a passive mode, an auxiliary mode and an anti-resistance mode. The motion range of the hip, knee and ankle joints can be accurately quantified, the joint motion range can be dynamically evaluated, the optimal training mode is dynamically recommended in combination with the training score calculated in real time, therefore, the rehabilitation efficiency can be improved, and the method can be widely applied to the technical field of rehabilitation training.

The invention discloses an intelligent exoskeleton bone auxiliary conduction hearing aid system capable of being remotely controlled and having an electroencephalogram control function, and relates to the technical field of intelligent rehabilitation robots and hearing aid devices. The wearable mechanical part comprises a flexible actuator, an elastic fabric base material, an inertial measurement unit IMU, a plantar pressure sensor array, a surface myoelectricity EMG sensor and an electroencephalogram EEG sensor. And the information processing communication part comprises a central controller, a bone conduction hearing aid module, a multi-mode sensor, an embedded SoC, a DSP / AI accelerator, a 4G / 5G / Wi-Fi module and a power management unit. The flexible exoskeleton walking aid system and the bone conduction hearing aid module are integrated, the electroencephalogram EEG sensor is additionally arranged to achieve the electroencephalogram control function, and cooperation of the three functions of movement assistance, auditory perception and neural intention recognition is achieved. A user can directly control the exoskeleton action mode through electroencephalogram signals, meanwhile, intelligent assistance of lower limb joints is obtained in the walking process, and environment sounds and voice prompts are clearly received in a bone conduction mode. According to the system, the perception ability, the control flexibility and the social participation degree of old people or rehabilitation patients who are inconvenient to move and accompanied with hearing impairment in a complex environment are remarkably improved, and more natural and convenient man-machine interaction experience is provided for users.

The invention discloses a falling risk assessment method and system based on a plantar pressure insole, equipment and a medium, and the method comprises the steps: obtaining plantar pressure data of a subject synchronously collected in a standardized TUG test process, and obtaining a clinical scale assessment result of the subject; the method comprises the following steps: preprocessing plantar pressure data of a subject, and extracting plantar pressure features and TUG time features; associating the extracted feature vector with a tumble risk tag evaluated by a subject scale, and constructing a data set with a tag; training a machine learning classification model by using the constructed data set; and inputting the feature vector of a to-be-evaluated subject into the trained classification model to obtain the fall risk level of the subject. According to the method, the falling risk can be objectively, quantitatively and comprehensively assessed in natural actions, clinical convenience and data scientificity are both achieved, TUG full-cycle biomechanical information can be completely captured in conventional environments such as communities and clinics, and a more accurate and interpretable assessment result is provided.