153 results about "Muscle activation" patented technology

The embodiment of the invention provides a humanoid robot self-adaptive control method and system, electronic equipment and a storage medium, and belongs to the technical field of robot intelligent control. The method comprises the steps that electroencephalogram signals and electromyographic signals of a target object are collected; determining a first time-frequency domain feature of the electroencephalogram signal according to the electroencephalogram signal, and determining a second time-frequency domain feature of a muscle activation state according to the electromyographic signal; fusing the first time-frequency domain feature and the second time-frequency domain feature by adopting a dynamic weighting algorithm to obtain a multi-modal feature; inputting the multi-modal features into an action intention recognition model to obtain a predicted action intention; and determining a robot control instruction according to the predicted action intention and the current state of the robot so as to control the humanoid robot. The precision of self-adaptive control of the humanoid robot can be improved.

The invention discloses an osteoarticular disease rehabilitation training optimization system, and relates to the field of medical training, and the system comprises an authority control module which is used for connecting an electronic medical system, obtaining the reading authority of historical and current medical data of a patient, and obtaining the instruction control and data reading authority of each functional module; the multimode acquisition module is used for acquiring pathological bone joints, associated musculoskeletal data and personal basic data through the authority control module, and converting original data into feature vectors of muscle activation degree, bone joint activity degree and skeleton stress distribution of a corresponding area of a patient body; by integrating medical data, musculoskeletal features and economic condition information of a patient, a personalized model is constructed to output an optimal adaptive training item and specific data thereof, the compensatory load of a non-ill-conditioned joint is evaluated, and timely intervention is performed through a fusing mechanism when abnormity is monitored in real time, so that the safety is ensured.

The invention provides a musculoskeletal disease risk assessment and early warning method and system based on multi-source data, and relates to the technical field of assessment and early warning of muscles and skeletons. Joint angles, myoelectricity and plantar load data are collected, a joint feature vector sequence consistent in time sequence is constructed, the limitation of a traditional single monitoring means is effectively made up, and the risk assessment and early warning of musculoskeletal diseases is achieved. K-means clustering is carried out on a joint angle sequence to form a standard attitude set with physiological significance, and a transition edge weight based on angle change, muscle activation and load fluctuation weighted calculation is constructed, so that quantitative expression of physiological costs of different attitude transfer paths is realized, a risk transition region is conveniently identified, and the risk transition rate is improved. According to the method, the sudden transition behavior in the movement process can be captured by calculating the deviation between the predicted value and the true value of the latent variable, and effective early warning of the musculoskeletal disease risk is realized by combining the edge weight strength of the sudden transition behavior in the directed graph and considering the transition strength and the abnormal behavior degree at the same time.

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

The invention relates to the technical field of physical training intellectualization and man-machine interaction, in particular to an interactive physical education method and system based on augmented reality driving, and the method comprises the following steps: S1, collecting three-dimensional motion track data and muscle activation time sequence data; s2, generating kinematic deviation parameters and physiological deviation parameters; s3, generating an AR correction instruction set containing a joint pose correction vector and a muscle activation intensity adjustment suggestion based on the kinematics deviation parameter and the physiology deviation parameter; s4, projecting the AR correction instruction set to the field of view of the trainee in real time through AR display equipment; and S5, generating a physical education efficiency report containing the action precision improvement index and the neuromuscular control optimization scheme. According to the intelligent physical education system, by fusing multi-source motion perception, deviation analysis and AR visual correction, synchronous optimization of motion precision and neuromuscular control ability is realized, and an intelligent physical education system with real-time interaction characteristics is constructed.

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 relates to the technical field of electromyographic signal monitoring, and discloses a guided monitoring method and system based on a wireless surface electromyographic analysis feedback instrument. The method comprises the following steps: acquiring electromyographic signal time sequence data and limb movement track space coordinate information when a target muscle group moves through a multi-channel wireless surface electromyographic sensor; muscle activation intensity and fatigue change features are extracted, a joint activity angle change sequence is calculated, and a muscle-joint collaborative activity feature matrix is generated through space-time alignment fusion; analyzing signal transduction delay and amplitude matching degree in the matrix, and evaluating the nerve driving coordination level; according to the deviation from the preset standard mode, generating a real-time biological feedback signal containing an action correction instruction and a muscle activation intensity adjustment parameter; and converting into a tactile vibration mode and a visual guidance mark to be output. According to the method, synchronous acquisition and fusion analysis of multi-dimensional motion information are realized, and visual guidance is provided through multi-modal feedback.

The invention relates to the field of swimming physical training analysis, and particularly discloses a swimming underwater physical training effect analysis system based on training data, which is characterized in that video data, motion data and physiological data of a swimmer are acquired by integrating an underwater camera, an inertial measurement unit, a myoelectricity sensor, a heart rate sensor and a blood oxygen sensor; space-time synchronous analysis of multi-modal data is realized; on the basis of joint angle change consistency and muscle activation synchronism, whether the action of the swimmer is standard or not is judged, AR real-time feedback is conducted, and real-time correction of the swimmer in training is facilitated; the training load of the swimmer is evaluated based on the underwater speed, the stroke, the stroke frequency, the swimming distance and the swimming duration, and overrun early warning is carried out, so that making of a personalized training plan is facilitated, and meanwhile, overtraining is avoided; by monitoring the heart rate, the oxyhemoglobin saturation, the muscle activation duration and the joint activity condition of the swimmer, whether the swimmer has the injury risk or not is judged, early warning is triggered, and the injury occurrence rate is reduced.

The invention discloses a hip replacement postoperative gait dynamics prediction method based on discrete optimal control, and belongs to the field of clinical medical auxiliary prediction. The implementation method comprises the steps that kinematics data are collected through a preoperative gait motion capture experiment, muscle activation signals are collected through a surface myoelectricity system, and on this basis, a neuromuscular control strategy of a patient is obtained through a neural network; predicting the gait motion characteristics of the postoperative patient based on discrete mechanics and an optimal control method, and predicting the postoperative muscle activation degree by using the neuromuscular control strategy; by comparing the muscle activation degrees before and after the operation, the influence of the replacement operation on the muscle force change of the hip joint of the patient is investigated. According to the method, discrete optimal control and the neural network are combined, the learning characteristics of human nerve-muscle control are considered while the physical characteristics of the system are not deviated, personalized muscle control strategies better conforming to different patients can be found, and personalized prediction of postoperative gait dynamic characteristics is achieved.

The invention discloses a control method and system of a lower limb exoskeleton power assisting device for iron tower climbing. The method comprises the steps that electromyographic signals are processed, and muscle activation degree indexes are calculated; the two-channel LSTM neural network realizes motion intention online prediction; iron tower cross arm structure edge identification and safe climbing path generation; designing an asymmetric impedance function and optimizing joint stiffness damping parameters; solving a metabolic consumption evaluation model and a minimum energy consumption assistance curve; and real-time calculation is realized through three-stage control period setting and hardware acceleration. Based on the method, all the modules of the exoskeleton power assisting device operate cooperatively; data are accurately collected through the sensor, the power and execution module is flexibly adjusted according to instructions, efficient operation is ensured through the control and operation module, stable electric power is provided through the energy module, and therefore the safety and efficiency of iron tower climbing operation are remarkably improved, and the burden of operators is relieved.

A rehabilitative and corrective system, a strength building system, a recovery system, a muscle activation system, and methods of use are presented. The invention provides a stable platform with multiple equipment attachments, an adjustable set of pillars with a stable crossbar, and a storage compartment. The invention is collapsible for movement and storage. The invention is also adjustable to fit the needs of various individuals and exercises. The systems and methods of use provide for a variety of rehabilitative and human performance movements which provide for a health professionals intent and musculoskeletal needs. The stable platform provides for vertical and horizontal adjustment. A center bar of the invention includes pillar cams and can be adjusted to rotate or remain stable. The invention provides for space savings and can be used to increase joint mobility, muscle activation, release, and strength, and other individual needs and goals.

The invention discloses a physiotherapy instrument control system and method based on artificial intelligence, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting an electromyographic signal in real time, carrying out band-pass filtering and TK energy operator processing, and outputting a muscle activation feature vector; based on the muscle activation feature vectors, spatial-temporal features are extracted through a convolutional neural network, and a physiotherapy parameter combination is constructed; a PID controller is adopted to adjust the physical therapy parameter combination in real time, and optimized physical therapy parameters are output; performing electromagnetic thermal coupling simulation on the optimized physiotherapy parameters through finite element analysis to obtain a physiotherapy instrument control instruction; and updating the weight of the convolutional neural network by adopting a stochastic gradient descent algorithm according to the physiotherapy efficiency evaluation report, and outputting an optimization execution instruction. PID parameters are optimized through a quantum annealing algorithm, stimulation parameters are dynamically adjusted in combination with myoelectricity beta wave band phase locking, and treatment parameters and physiological response are synchronized.

The invention discloses a wavelet enhancement cooperative activation method for FES multi-target time sequence regulation and control, and relates to the technical field of biomedical engineering. According to the model, an overlapped sliding window is analyzed through a multi-channel surface electromyographic signal, a time-frequency energy significant feature matrix is constructed by combining an energy ratio, and meanwhile, an inter-muscle co-activation matrix is constructed in the multi-channel electromyographic signal sliding window; constructing a multi-channel high-dimension matrix by using each single-channel time-frequency energy significant feature matrix and the multi-channel inter-muscle co-activation matrix, and performing data dimension reduction on the multi-channel high-dimension matrix to obtain a multi-channel multi-dimension time-frequency energy-inter-muscle co-activation matrix; and a muscle activation time sequence vector is screened by using a self-adaptive threshold logic combination rule, and finally an activation time sequence is applied to an FES experiment. According to the method, the movement of the hand on the sagittal plane is accurately predicted by analyzing the multi-channel surface electromyogram signals and the multi-target muscle co-activation characteristics, functional electrical stimulation FES parameters are dynamically adjusted, and accurate control over the muscle activity stage is achieved.

The invention belongs to the technical field of exercise rehabilitation, and particularly discloses an exercise rehabilitation effect quantitative evaluation method which comprises the following steps: S1, a data acquisition unit acquires data of a patient in an exercise process through fusion of a multi-source sensor; s2, muscle electrical activity signals in the movement process of the patient are collected through bio-electricity signals, and the activation degree and the fatigue condition of muscles in rehabilitation movement are known; s3, acquiring muscle electrical activity signal data according to the acquired multi-source fusion data and the bio-electricity signals, and performing rehabilitation evaluation on the model; s4, according to the output of the evaluation model, a quantitative rehabilitation effect evaluation report is generated, and the report content comprises numerical values of various quantitative indexes, comparative analysis of rehabilitation targets and comprehensive scores of rehabilitation effects; an exercise rehabilitation effect quantitative evaluation system comprises a data acquisition system and a rehabilitation evaluation module. The method solves the problems that a traditional evaluation method is high in subjectivity and single in data, and has the advantages of being objective, comprehensive and accurate in quantification.

The invention discloses an intelligent positioning system capable of conveniently adjusting the standing posture of a patient and a method thereof, and relates to the technical field of intelligent medical treatment. Comprising the following steps of initial posture evaluation and skeleton modeling, gravity center and supporting point distribution calculation, spine curvature and inclination detection, intelligent laser-assisted posture adjustment, dynamic muscle activation feedback monitoring, pelvis and lower limb alignment correction, posture balance prediction and guidance, upper limb posture and shoulder blade stability correction and real-time adjustment of biological feedback driving. And carrying out long-term attitude optimization and habit remodeling, and carrying out system construction based on the method. According to the intelligent positioning system convenient for adjusting the standing posture of the patient and the method thereof, accurate detection, real-time adjustment and long-term optimization of the standing posture problem of the patient are realized by setting and combining multi-modal data acquisition, a dynamic feedback mechanism and biomechanical analysis. The problems of low static evaluation precision, lack of dynamic feedback and poor long-term effect in the prior art are effectively solved.

The invention relates to the field of robots, in particular to a robot, a walking control method and device thereof and a program product. The method comprises the steps that the stability of the robot in different states is collected, and balance evaluation index items of the robot are determined according to the stability of the robot in different states; determining a target function of the robot according to the balance evaluation index in combination with a predetermined robot metabolism energy consumption rate item, a muscle activation level item, a joint acceleration item, a passive joint torque item and an arm driving excitation item; performing optimization simulation on the target function to determine an angle change curve of each joint of the simulation robot; and controlling the robot to walk according to the angle change curve. The objective function comprises the balance evaluation index item and the walking smoothness index item, so that the walking stability and smoothness of the robot can be effectively improved in the optimized walking control process, and the performance of the robot in practical application is improved.

The invention relates to the technical field of medical rehabilitation equipment, in particular to a surgical postoperative rehabilitation nursing auxiliary device and method based on the Internet of Things, and the device comprises a bearing part, a driving part, a base, a monitoring part and a remote interaction part, the driving part comprises a first driving assembly for providing relative resistance for a using object and a second driving assembly for driving the thigh and the shank to move relatively. The method comprises the steps that in a single training period, the passive muscle activation degree and the active muscle activation degree are determined to determine the rehabilitation degree of the muscle function of a using object; according to the limb movement track and the muscle strength balance condition of the feature movement part, the movement control ability of the using object is determined, and a rehabilitation training scheme of the using object is adjusted according to the rehabilitation degree or the movement control ability. According to the invention, comprehensive monitoring and remote transmission of the rehabilitation process of the use object are realized, and the rehabilitation process can be accurately controlled by medical personnel.

The invention discloses an action state evaluation method and system based on a time-varying muscle synergistic effect. According to the method, a muscle synergistic effect matrix of standard walking actions, a standard reference range of a scaling coefficient and a time delay parameter and a classifier for action state recognition are constructed, then real-time surface electromyogram signals of a target individual are collected in a real-time stage, and the muscle synergistic effect matrix is extracted; calculating a second similarity index with a muscle synergistic effect matrix of a standard walking action; and inputting the second similarity index into the classifier, if the second similarity index is identified as a'moving 'state, performing an action state evaluation process, otherwise, re-collecting the real-time surface electromyogram signal of the next window. According to the method, the time-varying muscle synergistic effect model is adopted, and the time dimension is introduced, so that the dynamic change in the whole muscle activation process can be more accurately captured; meanwhile, the muscle activation process is modeled from the source, whether the action is completed or not is reflected, and more attention is paid to physiological rationality and control strategies.

The invention discloses an anti-resistance exercise training system for old people based on intelligent wearable equipment, and relates to the technical field. The problems that in the prior art, a dynamic action monitoring error correction mechanism is lacked, exercise intensity adjustment is not personalized and real-time, interaction and data feedback functions do not exist, and equipment portability and wearing adaptability are insufficient are solved. The system collects data in real time through the whole body position intelligent wearable device, corrects non-standard actions through the action recognition feedback module, dynamically regulates the intensity through the exercise intensity evaluation module, reduces the risks of muscle strain, cardiovascular burden and the like, takes standardized action guidance, personalized intensity adaptation and data visualization as the core, ensures target muscle activation, and improves the training efficiency. The training effect is quantified, the operation is simple, the use threshold is reduced through voice guidance, the boring feeling is relieved through personalized plans and summary reports, the training achievement feeling and compliance of the old people are enhanced, the muscle strength can be improved through whole body position training, and the anti-resistance exercise requirement of the old people is met.

The invention discloses an intelligent lower limb rehabilitation training device based on multi-modal feedback. Comprising an adjustable exoskeleton support capable of dynamically adjusting exoskeleton driving torque, and a multi-mode feedback system installed outside the adjustable exoskeleton support and capable of capturing the motion intention of a patient. The central controller can calculate the optimal motion trail of the patient according to the data fed back by the multi-mode feedback system; the force feedback actuator can provide assistance or resistance in real time according to the muscle force of the patient; by arranging the multi-mode feedback system, the action starting of a patient can be predicted, and the action intention and the muscle activation intensity of a user can be recognized; the hip, knee and ankle joint angles and plantar pressure of the patient during movement are measured in real time, the training intensity is dynamically adjusted according to the muscle force state and the movement intention of the patient, and the training individuation is optimized; compared with traditional single-machine training, the device can avoid secondary injury of muscles and reduce the risk of muscle injury.

The invention discloses an elbow joint rehabilitation training evaluation method and system, and the method comprises the following steps: S1, collecting multi-cycle motion data and surface myoelectricity data when a patient executes a specified rehabilitation motion in a standard state through an inertia measurement unit and a surface myoelectricity sensor which are disposed on the upper limb of the patient, based on this data, establishing a personal motion baseline template comprising a standard angle-time curve and a standard muscle activation level; s2, in the rehabilitation training process, motion data and surface myoelectricity data are collected in real time, and the following operations are executed: S21, a joint motion range and a motion smoothness index are extracted from the motion data; s22, muscle activation intensity and collaborative shrinkage are extracted from the surface myoelectricity data; according to the method, individuation of evaluation standards, diversification of monitoring dimensions and dynamic weight adjustment are achieved, the accuracy and safety of rehabilitation training can be effectively improved, and the method has important clinical application value.

The invention provides a muscle activation judgment method, system and device for electromyographic signal processing and a method for establishing a model of the muscle activation judgment method, system and device. The muscle activation judgment method comprises the steps of acquiring original electromyographic signal sample data, filtering, extracting features, identifying a model and judging the muscle activation degree and state. By the adoption of the muscle activation judgment method and system device and the establishment method of the muscle activation judgment model, noise and interference can be reduced, power consumption can be reduced, the accuracy and reliability of detecting muscle activation from the electromyographic signals are remarkably improved, and safety is further improved.

The invention relates to the technical field of rehabilitation medical treatment, in particular to a hand rehabilitation training system based on virtual reality and gesture interaction, and the system comprises a gesture sensing module which collects a hand movement track, a muscle electric signal, grip pressure and a joint movement angle; the VR rehabilitation scene construction module constructs a rehabilitation training virtual scene, simulates a hand interaction behavior and a force feedback effect through an adaptive physical engine and a neural action mapping algorithm, and outputs simulation data; the intelligent evaluation center evaluates the action standard degree and the muscle activation efficiency based on an improved CNN-GRU model, identifies an action compensation mode and abnormal force generation, and outputs an evaluation report; the interactive training unit renders a motion state and a training process in real time, and generates personalized training guidance in combination with voice, visual and tactile prompts; and the personalized regulation and control unit predicts a rehabilitation trend and a stage target through a reinforcement learning algorithm, and dynamically adjusts training parameters and scene difficulty. Therefore, the problems of low interaction precision, incapability of sharing and the like in the prior art are solved.

The invention discloses a muscle state analysis method for analyzing muscle strength based on electromyographic signals, belongs to the technical field of neuromuscular physiology and data processing, and solves the problem that traditional electromyographic analysis lacks activation degree and fatigue degree multi-dimensional dynamic evaluation ability. Through multi-channel electromyographic signal collection and feature extraction and in combination with a calculation model, the muscle activation degree and the fatigue state are accurately evaluated. The functional state of the target muscle under the specific action can be dynamically monitored, and a reliable scientific basis is provided for movement safety; and moreover, the defects in the aspect of muscle state analysis in the prior art are overcome, and the method has important clinical application value in the aspect of improving exercise rehabilitation and training effects.

The present disclosure describes devices, methods and systems for modifying or altering gait kinematics via sensory augmentation and / or modifying muscle activation patterns via augmented motor learning to slow the progression of and / or reduce the pain associated with knee OA, particularly during gait (e.g., walking, running, stair climbing, etc.). Sensors can be used to measure gait parameters and characteristics and muscle activation patterns. Stimulation can be provided to the individual in order to promote learning new gait kinematics and muscle activation patterns.

A lower limb exoskeleton includes a controller employing a modular multi-task energy shaping framework for any lower-limb joint configuration, using a convex formulation of a multitask, data-driven optimization problem. In addition to being modular and convex, the framework offers adjustment of the torque goal, distributed processing across devices, and smooth transitions between stance and swing that are incorporated into the stability analysis. The framework can reduce overall muscle activation when considering multiple tasks among multiple individual users.

The invention discloses an interaction control method and system based on virtual reality, and relates to the technical field of monitoring analysis, and the system comprises a multi-mode motion capture module which is used for capturing limb space coordinate data, joint rotation angle data and muscle activation signals corresponding to a target user; the action intention analysis module comprises a time sequence convolutional neural network model, the time sequence convolutional neural network model comprises an input layer and an output layer, the input layer is used for receiving the skeleton joint point sequence, the angular velocity data and the myoelectricity feature vector of the multi-modal action capture module, and the output layer is used for extracting spatial and temporal features through three-layer expansion causal convolution. Connecting a classifier to generate initial action data corresponding to the target user; the correction module is used for constructing a human body joint angle constraint library, judging the initial action data corresponding to the target user and outputting a correction instruction; and the environment adaptation module is used for adjusting the environment in the virtual scene. The method has the effect of improving the interaction efficiency.

The invention belongs to the technical field of medical rehabilitation instruments and biological signals, and particularly relates to a multi-mode fusion intelligent rehabilitation evaluation system based on EMG and PPG. The system comprises a flexible multi-mode sensor array module used for collecting surface electromyogram signals EMG and photoelectric volume pulse wave signals PPG and capturing muscle activation intensity and blood perfusion dynamics in real time; the self-adaptive contact enhancing module is used for driving the airbag assembly to dynamically adjust the contact pressure between the embedded pressure sensor and the skin through the micro air pump; the edge calculation unit module is used for carrying out localization processing on the collected original signals, quantitatively evaluating the collaboration of muscle activity and blood flow fluctuation and judging whether muscle contraction drives local blood flow change or not or whether vessel limitation reversely inhibits neuromuscular activation or not; and the lightweight classification model module is used for processing the time-frequency characteristics of the EMG and the waveform fragments of the PPG, and outputting rehabilitation state labels after screening key collaborative characteristics through an attention mechanism.

The invention relates to the technical field of exoskeleton gait classification, in particular to an exoskeleton auxiliary gait recognition method and system based on neuromuscular signals, and the method comprises the steps: carrying out the signal segmentation of an electroencephalogram signal and an electromyographic signal according to a periodic gait time period set, and obtaining an electroencephalogram signal segment set and an electromyographic signal segment set, and calculating a power spectral density feature value set of the electroencephalogram signal fragments, calculating a muscle activation time period set and a root mean square value set of the electromyographic signal fragments, and inputting the exoskeleton torque data, the power spectral density feature value set and the muscle activation time periods into a gait classification model for gait classification to obtain a predicted gait. The human gait recognition accuracy of the exoskeleton gait assisting technology can be improved.

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