638 results about "Electromyography" patented technology

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The embodiment of the invention provides a joint torque estimation method and device based on electromyographic signals of lower limb exoskeletons, computer equipment, a storage medium and a computer program product, and relates to the technical field of exoskeletons. The method comprises the following steps: acquiring an initial filtering electromyographic signal of a target object; obtaining an electromyographic signal envelope corresponding to the initial filtering electromyographic signal according to the initial filtering electromyographic signal; performing normalization processing on the electromyographic signal envelope to obtain an electromyographic signal value corresponding to the electromyographic signal envelope; according to the electromyographic signal value, obtaining an independent source signal corresponding to the initial filtering electromyographic signal; and obtaining a joint torque value of a target exoskeleton matched with the target object according to the independent source signal. According to the method, the accuracy and efficiency of joint torque estimation based on the electromyographic signals are improved.

The invention relates to a neuromuscular electrical stimulation multi-objective self-tuning optimization method, which comprises the following steps: dynamically establishing a framework for muscle response in a time window sliding modeling mode, and describing short-term excitability change and fatigue accumulation by the framework according to historical stimulation frequency density and total energy integral accumulation variable; introducing a short-time response curve recognition mechanism: after stimulation, according to micro changes of an electromyogram and a force output curve, judging whether a nerve and muscle coupling state is in a deviation trend or not; when the output trend is evolved towards the target improvement direction, current strategy fine tuning is kept; if the trend is stagnated or reversed, the weight combination or the step length is actively adjusted; constructing a multi-target co-integration matrix for analyzing whether the current response changes of different targets are in a consistent direction; when it is detected that two certain targets have an inverse correlation change, the system carries out conflict decomposition processing once; interference compromising is carried out on conflict indexes, and short-term power down-regulation is carried out; and forming a dynamic strategy framework.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

The invention discloses an intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus, and relates to the technical field of electrical stimulation rehabilitation therapeutic apparatuses, the intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus comprises an electroencephalogram signal acquisition module, an electromyographic signal acquisition module and a mixed signal processing module, the mixed signal processing module is connected with the electroencephalogram signal acquisition module and the electromyographic signal acquisition module; the method is used for preprocessing electroencephalogram and electromyogram signals, fusing multi-modal features and classifying motion intentions. By installing the mixed signal processing module, preprocessing, multi-modal feature fusion and motion intention classification of electroencephalogram and electromyogram signals are achieved, a preprocessing unit can effectively remove ocular artifacts and power frequency noise, meanwhile, independent component analysis and a dynamic baseline calibration strategy are adopted, the signal quality is further improved, and the accuracy of motion intention classification is improved. According to the multi-mode signal processing mode, the limitation of single signal processing in the prior art is overcome, and the accuracy and effectiveness of rehabilitation treatment are improved.

The invention relates to a motion evaluation method, device and equipment based on multi-modal physiological data and a medium, and the method comprises the steps: solving the problem of space-time mismatch of the multi-modal data through sampling timestamps of a hardware clock protocol for multi-source physiological signals such as a makeup rate, myoelectricity, blood lactic acid and the like; equipment interference and motion artifacts are eliminated, and the signal quality is improved; dynamic characteristics such as heart rate variability, myoelectricity root mean square and blood lactic acid gradient in the sliding window are calculated; dividing exercise intensity intervals based on the individually calibrated heart rate percentage and the myoelectricity activation degree threshold, and detecting conversion candidate points; and recognizing a motion intensity critical state in real time through a self-adaptive threshold model driven by historical data, and generating a comprehensive evaluation result containing a thermodynamic diagram and an early warning report. According to the method, the limitation of a traditional fixed threshold model is broken through, multi-modal data deep fusion and individual dynamic adaptation are achieved, the exercise intensity critical point detection precision is improved, and real-time decision support is provided for training load optimization and rehabilitation progress evaluation.

The invention provides a multi-signal fusion massage glove teaching system and a control method, and the system comprises a glove body, and a pressure sensing module, a motion sensing module and a myoelectricity sensing module which are disposed on the glove body, and is in electric signal connection with the pressure sensing module, the motion sensing module and the myoelectricity sensing module through a control unit. The control unit is used for analyzing the collected massage pressure, motion trail and electromyographic signals, and synchronous and in-situ collection of force-motion-physiology multi-dimensional information of massage manipulation in a real clinical environment is achieved. The design overcomes the limitation that in the prior art, a real operation scene is separated, and only single-aspect measurement can be carried out, and a hardware foundation is laid for comprehensive and objective quantitative evaluation of the massage manipulation. The integrated structure greatly facilitates the use of an operator, and the system can evaluate the manipulation quality online and give guidance immediately, thereby greatly improving the teaching efficiency and the training effect.

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to a multi-modal signal fused fine exercise rehabilitation evaluation, regulation and control method and system, and each evaluation comprises the following steps: collecting a pressure distribution signal, a surface electromyogram signal and an electroencephalogram signal of a hand fine exercise; performing preprocessing, feature extraction and normalization processing on the three signals to obtain a pressure feature value, a surface myoelectricity feature value and an electroencephalogram feature value; calculating a conversion coefficient of a current rehabilitation stage based on the number of days that the fine motor function of the patient is damaged, and calculating weights of a pressure characteristic value, a surface myoelectricity characteristic value and an electroencephalogram characteristic value based on the conversion coefficient; performing weighted summation on the pressure characteristic value, the surface myoelectricity characteristic value and the electroencephalogram characteristic value to obtain an evaluation score; rehabilitation training parameters are regulated based on the assessment score and the number of days of impaired patient's fine motor function. According to the method, the limitation of traditional single-mode physiological signal evaluation can be broken through, and the comprehensiveness, the accuracy and the personalized adaptation capability of rehabilitation evaluation can be improved.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

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.

A wearable device includes an accelerometer, at least three electrodes to detect electromyography signal, a communication circuitry, and a processor circuitry. A user wears a primary and a secondary wearable device on two limbs. When the user performs a two-hand gesture, the wearable devices detect a primary and a secondary movement pattern. The primary device recognizes the two-hand gesture from the primary and secondary movement pattern.

The invention provides an intelligent scoring method and system for joint movement. The method comprises the following steps: acquiring six-degree-of-freedom inertial motion data and surface electromyogram signals, and establishing a motion sequence topological structure containing a space-time reference based on a dynamic sampling strategy; secondly, decomposing inertial data into rigid body and physiological tremor components, and generating a three-dimensional biomechanical model containing joint rotation center drift, multi-plane coupling errors and trajectory curvature; secondly, constructing a time-varying mapping relation between the surface electromyogram signals and the exercise load, segmenting the signals based on phase mutation points, analyzing phase synchronism of an active muscle group and an antagonistic muscle group, and extracting a muscle cooperation mode; and finally, generating a motion feature tensor in a four-dimensional space-time coordinate system through technologies such as space-time coupling and a dynamic transfer function, analyzing an evolution mode of the motion feature tensor in real time to construct a scoring function cluster, and generating a composite scoring value. According to the technical scheme provided by the invention, the accuracy and reliability of joint movement quality evaluation are remarkably improved.

The invention relates to the technical field of power-assisted robots, in particular to a self-adaptive control lower limb hydraulic power-assisted exoskeleton robot system which comprises a waist supporting device, a leg supporting device, a foot supporting device, a hydraulic power-assisted assembly, an attitude sensor, a lower limb force sensor, a wearable electromyogram sensor, a step obtaining module and a data analysis module. The system can intelligently adjust the output power of the hydraulic power assisting assembly according to the motion posture of the exoskeleton, the pressure data of the foot supporting device and the wearing target muscle state so as to provide proper walking assisting power and bearing support, and the system can further conduct secondary adjustment and fatigue alarm by monitoring the pace frequency and the muscle fatigue degree in real time. The exoskeleton assisting power output power in the carrying process is gradually adjusted according to the comparison result between the weight of the wearing target and the weight of the to-be-moved object, so that assisting power provided by the exoskeleton robot is effectively matched with the weight of the to-be-moved object when the wearing target carries the object.

The invention discloses an old man gait abnormity monitoring system based on body surface electromyographic signals, and relates to the technical field of gait abnormity monitoring, and the system comprises a configuration module which is used for obtaining a foot bearing device set of a user, and constructing three groups of foot bearing device test scenes; the inertial measurement module is used for collecting gait data of a user and establishing gait key parameters; the myoelectricity data acquisition module is used for establishing a myoelectricity test data set; the labeling module is used for establishing a gait cycle window and establishing a myoelectricity-gait fusion fingerprint spectrum; and the early warning module is used for reading the real-time acquired data and outputting an early warning signal. The technical problems that in the prior art, monitoring of the gait abnormity of the old man is not accurate enough, abnormity is difficult to find in time and early warning is difficult, and consequently the old man faces a high falling risk and cannot be protected in time when walking are solved, the accuracy and timeliness of monitoring of the gait abnormity of the old man are improved, and the safety of the old man is improved. Therefore, the walking safety of the old people is guaranteed.

Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and / or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and / or gestures of the user to provide input or control of the system.

The invention discloses an LLM-based scheduling management system, which belongs to the field of industrial and commercial enterprise scheduling management systems, adopts a data acquisition technology driven by bio-electricity signals, enriches data acquisition forms, can acquire various bio-electricity signals such as electroencephalogram, electrocardio, myoelectricity and the like, and improves the scheduling efficiency. And through a multi-channel parallel data transmission architecture and an advanced signal coding technology, the accuracy and stability of data transmission are ensured, key data in a human physiological state or a special environment are reflected, and some application scenes with relatively high requirements on data comprehensiveness and accuracy are met. The LLM-based scheduling module generates an optimal scheduling scheme by means of the powerful analysis and reasoning capability of a large language model in combination with a genetic algorithm and a simulated annealing algorithm, quantitative evaluation is carried out through a multi-objective optimization algorithm, various complex constraint conditions can be fully considered, and efficient scheduling is realized.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention belongs to the field of rehabilitation medical treatment, and particularly relates to an intelligent rehabilitation glove system and method based on a noninvasive motor nerve interface, and the method comprises the steps: a wearable electromyographic signal collection unit collects an electromyographic signal at an arm according to a collection instruction sent by a control unit, packages the signals in the same time period, and sends the packaged signals to the control unit; the control unit is used for sending a collection instruction to the wearable electromyographic signal collection unit; meanwhile, the received original electromyographic signals are processed, feature values are obtained, feature vectors are formed, a gesture classifier is trained, the gesture category of the healthy side of the patient is recognized according to the gesture classifier, a motion instruction signal corresponding to the gesture category is output to the flexible pneumatic rehabilitation glove, the gesture identical to the instruction is executed, and then the hand is driven to move. And active rehabilitation training is realized. By optimizing the layout structure, the system is completely integrated on the diseased side arm of the patient, and active and convenient rehabilitation training of various actions of the diseased side hand of the hemiplegic patient is achieved.

The invention relates to the technical field of electric acupuncture apparatuses, and discloses a method and a system for automatically adjusting stimulation parameters of an electric acupuncture apparatus. According to the method, body surface electromyographic signals, skin impedance and capillary hemodynamic parameters of a user are collected in real time through a sensor set, feature extraction and analysis are conducted through a time sequence prediction model of a Transform architecture, and physiological feature data are generated. And then, constructing a user physiological state characterization model by adopting a multi-modal neural perception fusion algorithm, and carrying out collaborative optimization on the electrical stimulation parameters through an adaptive quantum particle swarm optimization algorithm to generate a stimulation parameter combination matched with the real-time neuromuscular response characteristics of the user. The system drives electrical stimulation output according to the parameter combination, monitors physiological data changes in real time, realizes closed-loop dynamic balance of stimulation parameters and biological feedback signals through the dynamic parameter compensation module, and improves the treatment effect and safety.

The invention discloses a self-adaptive closed-loop functional electrical stimulation system based on muscle coordination, and the system can adaptively adjust an electrical stimulation signal at a next moment based on the response of human muscles to an electrical stimulation signal at a moment, thereby achieving the self-adaptive adjustment of the electrical stimulation signal at the next moment. The monitoring and control of the electrical stimulation signal are realized; due to the fact that closed-loop control is executed according to the electromyographic signals and the motion signals, and the electromyographic signals and the motion signals actually comprise personalized feature information of the object human body, the self-adaptive closed-loop functional electrical stimulation system based on muscle collaboration can establish evaluation and feedback of the electrical stimulation intervention effect. Therefore, the effectiveness and the safety of electrical stimulation are supervised by utilizing the self-adaptive regulation and control values of the electromyographic signals and the motion signals, and self-adaptive regulation electrical stimulation signal parameters conforming to the individual muscle motion characteristics of the object can be stably output. The device is widely applied to the technical field of rehabilitation training.

The invention provides a gait detection and anomaly recognition system based on multi-sensor fusion, and the system comprises a sensor integration module which is used for deploying a thin film pressure sensor and a lightweight surface myoelectricity sensor and carrying out the data collection; the data preprocessing module is used for preprocessing the collected data; the feature extraction and fusion module is used for extracting and fusing features of the collected data to obtain a multi-modal feature vector; the gait monitoring module is used for constructing a recognition model; the abnormal recognition module is used for carrying out recognition and real-time feedback on abnormal gaits by utilizing a recognition model; and the doctor end interaction module is used for designing a mobile end APP and a doctor end management platform. According to the invention, through the multi-modal data and the portable wearable device, the diversity of detection modes is increased, the detection precision is improved, the method can be applied to multiple scenes, and the environmental limitation is broken; and meanwhile, by deploying a deep learning model, the intelligent degree of gait anomaly recognition is improved, and comprehensive technical support is provided for rehabilitation of the patient.

The invention provides a tumor target pose real-time tracking and predicting method and system, equipment and a medium. An abdominal respiration monitoring device of the system is used for detecting abdominal respiration deformation; the multi-channel array flexible acquisition sensor has deformability and is used for detecting back myoelectricity and pressure change signals; the body surface information acquisition device is used for acquiring chest and abdomen point cloud data of a human body; and the main controller is used for processing data acquired by the abdominal respiration monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device to obtain a predicted position of an active tumor target region. In order to solve the problem of respiratory movement in the radiotherapy process, back pressure myoelectricity information, abdomen respiratory deformation information and binocular vision information respiratory movement evaluation are adopted, and a mapping relation from in-vitro movement to in-vivo tumor movement is constructed through in-vitro multi-modal data and 4D-CT information in combination with a big data model. And in-vivo tumor pose prediction and evaluation are realized.

The invention relates to a sports injury real-time early warning and health optimization system based on multi-mode infrared thermal imaging and three-dimensional skeleton posture fusion, and belongs to the field of sports health management. The problems of motion analysis lagging, damage identification missing and cross-scene data splitting of a traditional monitoring means are solved. According to the technical scheme, the system comprises a multi-modal motion data sensing module which synchronously collects human thermodynamic distribution, three-dimensional skeleton postures and surface electromyogram signals through an infrared thermal imaging camera, an RGB-D camera and a bioelectric sensor; an AI driving training optimization engine is used for generating an adjustment instruction in real time based on a muscle thermal gradient and joint trajectory deviation degree dual-threshold model; and the full-scene intelligent management platform executes millisecond damage early warning, metabolic optimization and school-family health data closed-loop management. The technical effects are that the exercise safety guarantee is fundamentally improved, the training is scientifically optimized, the metabolism management accuracy is broken through, and the teaching resource allocation is intensive.

The invention discloses a scoliosis assessment method and system based on multi-modal data. The method comprises the steps that plantar pressure static features and plantar pressure dynamic features are extracted according to plantar pressure signals of a to-be-assessed target; extracting surface electromyogram static features and surface electromyogram dynamic features according to the obtained surface electromyogram signals; ultrasonic image static features and ultrasonic image dynamic features are extracted according to the obtained ultrasonic image; inputting the plantar pressure static feature, the plantar pressure dynamic feature, the surface myoelectricity static feature, the surface myoelectricity dynamic feature, the ultrasonic image static feature and the ultrasonic image dynamic feature into a preset scoliosis feature fusion model for fusion to obtain a static fusion feature and a dynamic fusion feature; and determining the scoliosis grade of the to-be-evaluated target according to a comprehensive evaluation score and a preset scoliosis grade table, wherein the comprehensive evaluation score is obtained by calculating the static fusion features, the dynamic fusion features and a preset weight coefficient. According to the invention, the scoliosis evaluation accuracy is improved.

A muscle probe is provided for obtaining electromyography data and optical spectroscopy data from muscle tissue. The muscle probe comprises an elongate needle having an outer wall surrounding a needle interior, the needle interior comprising: a core electromyography electrode; and one or more optical fibres; wherein the needle is arranged to be inserted into a muscle, and further arranged to detect electrical activity from the muscle; and wherein the one or more optical fibres are arranged to direct incident light from a light source toward a target area of the muscle, and further arranged to receive scattered light from the target area. The present disclosure aims to provide a muscle probe to improve the diagnostic pathway for patients with neuromuscular disorders, by developing a minimally invasive bedside test of muscle health.

The invention discloses a pelvic floor muscle early screening training method based on data analysis, and the method comprises the steps: a user completes a specified rating action, and obtains SEMG (Surface Electromyography) data; the method comprises the following steps: processing original data of a surface electromyographic signal, calculating an evaluation index of the electromyographic signal, obtaining index information in a time domain aspect, carrying out fast Fourier transform on the signal to obtain frequency domain related information, and calculating the evaluation index of the electromyographic signal according to the frequency domain related information. The related information comprises a median frequency MF and an average power frequency MPF; comparing and screening the obtained signal index with an expected index; according to comparison with a preset value, a pelvic floor muscle state report is produced according to a rating result; the pelvic floor muscle function evaluation method has the technical advantages that subjectivity and uncertainty possibly existing in a traditional evaluation method can be avoided, and the real function state of the pelvic floor muscle can be comprehensively reflected.

The invention relates to a chin electrical stimulation combined upper airway muscle group training system and method based on bioelectric feedback, and belongs to the field of upper airway muscle group training. Comprising a pressure detection module used for detecting a pressure value in an upper respiratory tract muscle group training process; the myoelectricity acquisition module is used for detecting myoelectricity numerical values in the training process of the upper respiratory tract muscle group; the electrical stimulation output module is used for stimulating partial nerve excitation and muscle contraction of the muscle group of the upper respiratory tract of the trainee; the signal conditioning module is used for carrying out preprocessing and analog-to-digital conversion on signals transmitted among the modules; and the control module is used for controlling signal interaction in the whole training process. And the display interaction terminal is used for interacting with the trainee. Based on bioelectricity feedback, a new individualized electrical stimulation and upper airway muscle group training treatment mode is newly established, accurate electrical stimulation and training intensity are formulated, and fatigue and injury are avoided.

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 invention relates to the field of medical instruments, in particular to a percutaneous microflow acupuncture system closed-loop regulation and control method and system based on multi-channel acupoint impedance, and the method comprises the steps: obtaining a first surface electromyogram signal in a human body action period, and constructing a relation between an action behavior and surface electromyogram; determining a multi-channel implementation path of the percutaneous microflow acupuncture system according to the relationship; impedance characteristics of acupuncture points along the multi-channel implementation path are obtained, and percutaneous microflow electrical stimulation operation is set according to the impedance characteristics; acquiring a second surface electromyogram signal during the implementation of the electrical stimulation operation, and adjusting the electrical stimulation operation according to the first surface electromyogram signal and the second surface electromyogram signal; and acquiring a response signal for adjusting the multi-channel implementation path association region during the implementation of the electrical stimulation operation so as to change the multi-channel electrical stimulation mode. According to the invention, path calibration of multiple acupoint associated stimulation areas is implemented through multiple channels, feedback closed-loop stimulation regulation is formed by combining acupoint impedance and electromyographic signals formed during stimulation, and the stimulation efficiency is improved.