1073 results about "Training effect" patented technology

The invention provides an intelligent rehabilitation training method and system based on artificial intelligence and virtual reality, a user wears an intelligent wearable device to collect multi-modal data such as electroencephalogram, myoelectricity, physiological features and motion signals, the multi-modal data is preprocessed and then input into an artificial intelligence training model, and key features are extracted and fused by using a graph convolutional network and an attention mechanism algorithm. And constructing a digital twinborn model by using the fusion features, performing real-time dynamic mapping and predictive simulation, and generating a customized training scheme by means of a reinforcement learning algorithm in combination with a rehabilitation target and a physical state of the user. A user is trained in the virtual reality interaction model, the system monitors actions and physiological states in real time, the digital twin model synchronously acts, and the scene is dynamically adjusted. After training, the rehabilitation effect is evaluated according to the physiological indexes, the motion data and the twinning optimization analysis result, and an optimization training scheme and a digital twinning model are fed back. Precision, individuation and intelligentization of rehabilitation training are achieved, and the training effect and quality are improved.

The invention relates to the field of man-machine interaction training, virtual reality and intelligent evaluation, and particularly discloses a psychological exercise ability training method and system based on mixed reality and behavior analysis. The method comprises the steps that a multi-scene psychological training environment is constructed and presented in mixed reality equipment, action behavior data, eye movement path data and operation response time data of a pilot in the training process are collected, multi-modal original behavior characteristics are constructed, and then a psychological exercise ability label set is constructed; and realizing capability-task modeling through matching of a capability label and a task structure, generating a personalized training path and a capability difference atlas based on difference analysis with a preset capability standard, adjusting task parameter configuration data, dynamically updating a training scene, generating a capability evolution trajectory, and outputting a training analysis result. In this way, accurate ability modeling, task dynamic adaptation and visual tracking of the training effect can be achieved, and the individuation and intelligence level of psychological exercise ability training is remarkably improved.

The invention provides a VR-based dynamic visual function training method and device, a medium, a program product and a terminal. An initial training task type and configuration parameters are generated according to a visual evaluation result of a patient, a sighting mark and a stimulation signal for visual function training are constructed, and training is executed through a VR unit. In the training process, feedback data of a patient is collected in real time, a real-time evaluation result is analyzed and generated, task types, configuration parameters and sighting marks are dynamically adjusted, and new stimulation signals are generated to continue training, so that dynamic optimization of training content is achieved. Through real-time evaluation and dynamic adjustment, the problem that the training content is not matched with the requirements of the patient is solved, the defects of a traditional method in the aspects of individuation and adaptability are overcome, the training effect is remarkably improved, the requirements of the patient are better met, the training time is shortened, and the efficiency is improved. The device is especially suitable for rehabilitation training of various visual function defects, and has remarkable advantages in the aspect of dynamic visual function recovery.

The invention provides a physical training posture correction method based on machine vision, which comprises the following steps: acquiring an athlete training image sequence through a multi-view image acquisition system and preprocessing the athlete training image sequence, extracting three-dimensional posture key points of a human body by utilizing a depth posture recognition model, constructing a skeleton model and calculating real-time kinematics characteristic parameters, and performing multi-dimensional comparison with standard parameters to generate a posture deviation evaluation report, thereby generating a personalized multi-modal correction feedback scheme, and establishing a personal athlete movement feature database to realize adaptive standard parameter optimization. According to the method, the training postures of athletes can be accurately recognized and corrected in real time, the training effect can be improved, sports injuries can be prevented, and training individuation and scientificity are improved.

The invention relates to the technical field of spine rehabilitation training, and discloses a computer vision recognition and guidance system for spine rehabilitation training actions. The system comprises a data acquisition module for acquiring user spine motion three-dimensional dynamic image data by using a multi-camera array; the action recognition module tracks bone joint points based on an improved algorithm to generate a real-time action sequence; the posture evaluation module is used for extracting spine curvature and joint angle features through a multi-scale convolutional neural network; the guidance instruction generation module inputs the features into a reinforcement learning model to generate a personalized instruction; and the feedback execution module constructs a fuzzy self-adaptive control model to correct the motion trail of the spine. According to the system, training actions can be accurately recognized, personalized guidance is provided, real-time correction is achieved, the spine rehabilitation training effect is effectively improved, subjectivity and errors of manual evaluation are reduced, and rehabilitation requirements of different patients are met.

The invention relates to the field of medical informatization, in particular to a multifunctional acupuncture point training simulation system and an application method thereof. Simulation tissue, force feedback, an acupoint simulation database and an intelligent evaluation technology are fused, and a vivid acupuncture training environment is created. The system simulates human tissues layer by layer, accurately collects acupuncture pressure and resistance, feeds back force feeling in real time, intelligently evaluates acupuncture performance in combination with acupuncture point characteristic data, and generates a detailed evaluation report. The virtual reality display module visually displays the acupuncture process and provides all-around feedback information. According to the innovative design, the acupuncture training effect and efficiency are remarkably improved, a safe and efficient learning platform is created for students, the modernization and standardization process of acupuncture education is promoted, and new power is injected for traditional medical inheritance and development.

The invention discloses a spine load monitoring system for dynamically adjusting a rehabilitation plan after an operation, and relates to the technical field of spine load monitoring, a monitoring data acquisition module collects preoperative and postoperative multi-modal data, and provides a comprehensive basis for the rehabilitation plan; a rehabilitation training initial parameter setting module determines expected recovery effect data and initial parameters of each stage, so that rehabilitation training is more targeted; in the rehabilitation process, the recovery state evaluation module evaluates training and recovery effects in real time and discovers problems in time; the rehabilitation training initial parameter adjusting module adjusts parameters when the recovery effect does not reach the standard, it is ensured that the rehabilitation plan always conforms to the condition of the patient, and the problem that in the prior art, a personalized rehabilitation plan cannot be made accurately according to preoperative and postoperative multi-modal monitoring data of the patient is solved. The training effect is difficult to dynamically evaluate in the rehabilitation process, and rehabilitation training initial parameters are difficult to adjust in time to guarantee the effectiveness and safety of the rehabilitation process.

The invention relates to the technical field of training simulation, and discloses a VR-based gas safety emergency training system, which comprises a three-dimensional scene construction module, an interactive data acquisition module, an interactive data processing module, an examination data analysis module and an examination evaluation module, entity drilling is replaced, the same scene can be repeatedly trained for infinite times, and the training cost is remarkably reduced; the VR technology provides multi-sensory stimulation, the intuitive cognition of students on danger is enhanced, the safety awareness and the emergency response capability are effectively improved, and no danger exists; by collecting operation data of students, the system automatically calculates key coefficients such as equipment operation precision, process compliance, disposal timeliness and path planning rationality, and scientific quantification and accurate scoring of training effects are achieved; and automatic grading evaluation is carried out according to a grading result, and a customized remedy module is triggered for students with insufficient abilities, so that a'training-assessment-intervention 'closed loop is formed, and the overall training efficiency is improved.

The invention provides a personalized training plan generation method and system in meta-universe smart sports, and relates to the technical field of smart sports, and the method comprises the steps: obtaining user meta-universe scene sports data, calculating an actual sports load based on a human body biomechanical model, carrying out the time sequence analysis through a neural network, and predicting a physiological limit threshold value; and constructing a training knowledge graph, generating a stage training plan adaptive to the user through a graph neural network, and finally presenting the stage training plan in a meta-universe virtual scene form. According to the method, personalized, safe and scientific training guidance can be provided, and the training effect and the user experience are improved.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention provides a football and training assembly. The football comprises a ball body and an identification area arranged on the surface of the ball body. And the identification area comprises a ball outlet guide line, a ball placing line and a ball hitting point group which are respectively arranged on the surface of the ball body. The ball outlet guide line and the ball placing line are located on different sections. And each section passes through the center of the sphere. The ball hitting point set comprises a plurality of ball hitting datum points distributed at intervals. The section where the distribution path of the ball hitting datum points is located coincides with the section where the ball outlet guide line is located. The horizontal midline section is defined as a section passing through the center of the sphere. When the section where the ball outlet guide line is located and the section where the ball placing line is located are both perpendicular to the ground, the section of the horizontal center line is parallel to the ground, and the ball hitting datum points are distributed downwards from the section of the horizontal center line. According to the football and training assembly provided by the invention, the training efficiency and the training effect of the place kick can be improved.

The utility model relates to the technical field of sitting posture correction, and discloses a child sitting posture training device which comprises a correction assembly, the correction assembly comprises a first cushion block and a second cushion block, the first cushion block is provided with a first top face and a first bottom face which are oppositely arranged, and the second cushion block is provided with a second top face. The second cushion block is provided with a second top surface, a second bottom surface and a plurality of side surfaces, the second top surface and the second bottom surface are oppositely arranged, the side surfaces are arranged between the second top surface and the second bottom surface, at least two adjacent side surfaces are arranged at a preset included angle, the second bottom surface is detachably connected with the first bottom surface, and the size of the second bottom surface is smaller than that of the first bottom surface; when the first cushion block is located at the supporting position, a child patient can sit on the second top face and step on the first bottom face, the two legs of the child patient can abut against the two side faces arranged at the preset included angle respectively, when the first cushion block is located at the table top position, the two legs of the child patient can abut against the two side faces arranged at the preset included angle respectively in the sitting posture state, and the first top face is used for containing objects. Adjustability and use flexibility are good, operation is convenient, and the training effect is good.

The invention provides a multifunctional rehabilitation robot real-time monitoring and intelligent regulation and control system for ICU-AW patients. Comprising a trial operation data transmission self-inspection module, an initial data acquisition module, a rehabilitation data real-time monitoring module and a rehabilitation training parameter real-time regulation and control module. According to the method, the basic physiological data of the user is collected and analyzed to obtain the personalized rehabilitation characteristic coefficient, so that the training parameters of the robot are initialized, the suitability of the training parameters and the requirements of the patient is ensured, the rehabilitation efficiency is improved, and training injuries are prevented. In addition, the system also performs pilot run self-inspection, so that the data transmission quality is ensured, and the analysis efficiency and accuracy are improved. In addition, the system monitors physiological and training data in rehabilitation training in real time, provides immediate feedback, dynamically adjusts training intensity and rhythm, optimizes the training effect and reduces the training risk.

The invention relates to the technical field of vision intervention, in particular to an eye muscle regulation ability training method and a visual training graph generation method thereof.According to the eye muscle regulation ability training method, feature information of a trainee and distance information of training are obtained, and a preset image generation network model is used for generating a corresponding visual training graph; the training device is used for training the eye muscle adjusting ability of a trainee. According to the method, the initial distance point location is matched based on the eyesight data of the trainee, the corresponding vision training graph is generated according to the current distance point location and the feature data of the trainee, the training intensity is dynamically adjusted, a personalized training scheme is achieved, the eye muscle adjusting ability is helped to be improved, and the training effect is improved. The problem of poor effect or overtraining caused by inappropriate training intensity is avoided, and the technical problems that in an existing eye muscle training method, due to insufficient individual pertinence, the training effect is difficult to quantify, and training scientificity and systematicness are insufficient are solved.

The invention provides a virtual reality training system and method for fireman stress disorder adjuvant therapy, the fireman training system and method based on virtual reality are provided by utilizing the development of the virtual reality technology and the heart rate variability monitoring technology, the virtual reality environment is selected and adjusted in combination with multiple dimensions, and the fireman stress disorder adjuvant therapy effect is improved. On one hand, the fire-fighting task processing capability of the firefighter can be trained, and on the other hand, the post-traumatic stress disorder of the firefighter can be treated in an auxiliary manner, so that the firefighter is helped to overcome the fear of trauma and high pressure in the fire-fighting task, and the post-traumatic stress disorder is prevented and treated in an auxiliary manner; the cure rate of the stress disorder of the group of firemen is effectively improved, and the prevalence rate of the stress disorder is reduced. Moreover, according to the scheme, through multi-level personalized adjustment, it is ensured that the adjuvant therapy training scheme better meets the unique requirements of each fireman according to individual characteristics, individual differences, psychological states and physiological states, and the training effect is effectively improved.

A gravity center transfer trainer comprises a balance training plate, a shell, a left spring, a right spring and a balance pointer, the balance training plate is hinged to the shell, and the balance pointer is connected with the shell; during patient gravity center transfer training, a patient stands on the balance training plate, a doctor instructs the patient to train according to the condition of the patient, the gravity center transfer condition of the patient is visually displayed through the balance pointer, the patient visually views gravity center transfer data of the patient, and the patient can perform gravity center transfer training by observing whether the balance pointer is aligned with the middle zero balance point identifier or not. The patient can clearly know whether the gravity center is on the rotating axis of the human body or not, so that the patient can better cooperate with a doctor for training, and the gravity center transfer training effect is improved; the defect that a patient and a doctor cannot visually see whether the gravity center of the patient is transferred to the central axis of the human body of the patient according to data of a weight scale is overcome, and the problem that gravity center transfer of the human body cannot be visually seen when the weight scale is used for gravity center transfer training is solved.

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 discloses a neural signal-based wounded rehabilitation effect evaluation model construction method, and relates to the technical field of medical rehabilitation, and the method comprises the steps: synchronously collecting electroencephalogram, myoelectricity and motion parameters, extracting multi-source features to construct feature vectors corresponding to training actions, and according to the feature vector data of multiple training actions of a wounded, constructing a neural signal-based wounded rehabilitation effect evaluation model; and capturing dynamic changes of neural signals and action control ability in the training action sequence based on the sequence model, and constructing an evaluation model. According to the invention, time-dependent learning is carried out on the corresponding characteristics of each training action by using the sequence model, dynamic changes of neural signals and action control ability along with the training actions can be captured, an improvement trend in a rehabilitation process is embodied, and an evaluation model aiming at an action training effect is constructed; the action indexes output by the evaluation model can reflect the instant effect and gradual improvement condition of each action in training, and help to identify rehabilitation inflection points and training bottlenecks.

The invention discloses a short-term memory manipulation ability training method and related products, and the method comprises the steps: determining an initial training level based on the basic information of a target object; determining a material generation condition of current training of the target object based on the initial training level and the basic information; inputting the material generation condition into a large language model to obtain a training material; generating a training task based on the training material and the initial training level; displaying the training task to the target object, and obtaining feedback data of the target object on the training task; and training the target object for multiple times based on the feedback data and the basic information. By using the method, the challenging and interestingness of training are improved, the training effect and efficiency are further improved, and the training participation degree and the user experience are improved.

The invention provides a remote rehabilitation monitoring method and system combined with an Internet of Things intelligent device, and relates to the technical field of visual detection, and the method comprises the steps: monitoring and obtaining a rehabilitation training video and training accompanying sign data of a user through an Internet of Things sensing module; performing risk interference compensation on the reference rehabilitation training video according to the user features, obtaining an optimized reference video, and performing multi-node attitude conjoint analysis on the user rehabilitation training video to obtain a first coefficient; adaptively correcting the first coefficient based on the sign correlation correction function and the training accompanying sign data to obtain a second coefficient; and if the second coefficient does not meet the standard reaching condition, performing feedback interaction optimization on the current scheme. The technical problems in the prior art that the training effect evaluation is not accurate enough and the training scheme is difficult to dynamically optimize and adjust due to the lack of individual difference adaptation ability of a rehabilitation training visual detection mechanism are solved, the accuracy of rehabilitation training visual detection evaluation is improved, and the dynamic optimization ability of the training scheme is enhanced.

The invention relates to the technical field of image recognition, and particularly discloses a martial art action recognition method based on machine vision, which comprises the following steps: S1, recognizing skeleton points of a user in an image, and obtaining coordinates of the skeleton points; obtaining a standard point in the standard image, and obtaining the coordinate of the standard point; s2, determining an abnormal image according to the theoretical distance between the skeleton point and the standard point, and generating an abnormal point; clustering the abnormal points to obtain a clustering cluster, and obtaining the abnormal points corresponding to the center of the clustering cluster; s3, obtaining a target point, calculating the total theoretical distance between the target point and the standard point, and obtaining a standard image B corresponding to the minimum value of the total theoretical distance; obtaining a first sequence, generating a reference point according to the first sequence, and obtaining a fitting curve; and judging the training effect of the martial art action corresponding to the standard image B based on the fitted curve. The training effect of the user can be dynamically evaluated.

The invention relates to the technical field of model training sample processing, in particular to a sample distribution adaptive adjustment method and system based on model training feedback driving, and the method comprises the following steps: S1, collecting feedback indexes in a training process in real time in a model training period, and obtaining a feedback index set; s2, weak item categories are analyzed and recognized based on the feedback index set, and an optimization instruction is generated; s3, dynamically adjusting the sampling weight to form an updated sampling strategy; s4, samples are collected according to a sampling strategy, and weak item category samples are supplemented; s5, putting the optimized sample set into the next round of training, and circulating the steps S1 to S4 until the training indexes of the learning effects of all key categories meet the termination condition; and acquiring a sampling strategy and a sample set. According to the method, real-time, refined and automatic sampling adjustment can be realized by taking the actual performance of model training as a core feedback index, so that the training effect and generalization ability of the model in key areas such as weak-term categories and long-tail categories are improved.

The invention discloses a tennis racket batting data monitoring system based on an intelligent sensor, and particularly relates to a training data monitoring technology, which comprises a four-layer collaborative architecture, an intelligent tennis racket, an intelligent bracelet and a communication module, the four-layer collaborative architecture is composed of a sensor layer, an unmanned aerial vehicle collaborative layer, an edge computing layer and a cloud / user layer; a first communication module is arranged in the unmanned aerial vehicle cooperation layer and used for receiving racket data and environment perception, and the unmanned aerial vehicle cooperation layer communicates with the sensor layer in real time. According to the tennis racket ball hitting data monitoring system based on the intelligent sensor, comprehensive monitoring and analysis of tennis ball hitting data are achieved, the system can capture detailed information at the ball hitting moment, and an accurate ball hitting quality score is generated in combination with physiological data and environmental factors of players; scientific training feedback and competition strategy support are provided for players; the self-adaptive feedback control function of the system can dynamically adjust the training intensity according to the physical state of the player, and the training effect is effectively improved.

According to the modularized ultrasonic-guided lumbar puncture teaching model provided by the invention, on the basis of the detachable modularized ultrasonic-guided lumbar puncture teaching model, the pathological bony structure module for simulating the corresponding pathological conditions of the lumbar structure is introduced, so that the lumbar puncture process in different pathological states is simulated; therefore, trained medical staff can be helped to better understand the anatomical basis and operation key points of lumbar puncture, the understanding of different pathological changes is deepened, and an experimental platform is provided for studying the improvement of lumbar puncture operation, the occurrence mechanism of puncture-related complications and the like. Furthermore, better lumbar puncture operation in actual clinical work is promoted through a better teaching and training effect, and the medical service experience and personal safety of a patient are guaranteed.

The invention belongs to the technical field of shooting training assistance, and discloses a kinetic energy weapon trajectory attitude analysis and shooting time acquisition method and system. The method comprises the following steps: installing and calibrating a weapon detection terminal comprising an acceleration sensor and a gyroscope sensor on a kinetic energy weapon gun body; in the shooting process, acceleration and angular velocity data of the weapon are collected at high frequency through the terminal and wirelessly transmitted to the analysis processing equipment; the analysis processing equipment is used for performing gravity compensation and attitude calculation on the received data to obtain a real-time attitude angle and processing angle abrupt change; analyzing the data statistical characteristic change of the acceleration and / or angular velocity data, and comparing the data statistical characteristic change with a preset threshold value to accurately judge the shooting moment; and finally outputting track attitude information for evaluation. The weapon attitude can be accurately analyzed, the shooting time is accurately collected, an objective and quantitative evaluation basis is provided for shooting training, and the training effect is improved.

The invention discloses a cognitive behavior training system, method and device and a program product, relates to the field of medical rehabilitation, and is used for solving at least one problem of single training target, single task difficulty and single task element. According to the method, main training tasks of five dimensions are carried out in stages, cognitive competence corresponding to task training of each dimension is progressively carried out step by step, and centralized, continuous, selective, alternate and dispersive attention cognitive competence intervention is respectively carried out. The main training task of each dimension sequentially comprises multiple levels of sub-training tasks with gradually increased difficulty, and each level of sub-training task comprises multiple groups of segmented tasks. And respectively evaluating the training effect under each dimension. According to the method, the difference and compliance of cognitive behavior training are improved, the intervention efficiency is improved, and the long-term intervention effect is ensured.

The utility model discloses a self-service shooting machine capable of automatically correcting the position and directionally throwing a basketball after shooting. The self-service shooting machine comprises a basketball receiving guide funnel and a basketball throwing machine which is fixedly connected with a basketball frame or is matched with the basketball frame after being movably positioned. The basketball throwing machine comprises a shell seat, a concave cavity, a first ball serving wheel, a second ball serving wheel and a ball feeding wheel. A concave cavity is formed in the top of the shell base, and a first ball serving wheel, a second ball serving wheel and a ball feeding wheel are arranged in the concave cavity. The equipment provides real shooting experience similar to that of a traditional coin-inserting basketball machine through fixed connection, and is suitable for entertainment and easy training; through combination of moving cooperation and a basketball frame, a real training environment is simulated, accurate automatic correction and directional throwing functions are provided, and the training effect is remarkably improved; various installation modes enable the equipment to be flexibly used in different places, and various requirements of families, school playgrounds, gymnasiums, amusement parks and the like are met.

The invention relates to the technical field of rehabilitation training, in particular to a rehabilitation training method and system integrating a rehabilitation robot and a cloud platform. Comprising the following steps: constructing a standardized motion track data set; joint time sequence features are extracted, coordination deviation in the multi-joint coordination process is analyzed, time sequence coordination deviation distribution is determined, and when deviation exceeds a preset range, an abnormal deviation track and a compensation behavior mode are recognized; generating a personalized training protocol scheme in combination with the compensatory behavior pattern and patient personalized data, and obtaining an optimized motion guidance path through multi-joint collaborative path simulation and parameter correction; training feedback indexes are extracted based on the motion guidance path, the training effect is judged, rehabilitation robot control parameters are dynamically updated, and a continuously optimized function recovery path is formed. The problems that in existing rehabilitation training, multi-joint collaborative evaluation is insufficient, and a training scheme is difficult to dynamically adapt are solved, and intelligentization, individualization and continuous optimization of the rehabilitation training process are achieved.

The invention provides a tendinopathy rehabilitation training and monitoring system based on electrical stimulation, and the system comprises a detection unit which is used for measuring the electrical impedance of the tendon and muscular tissue of a patient before rehabilitation training, and providing a basis for the initial setting of electrical stimulation parameters; the electrical stimulation unit is used for monitoring the muscle contraction degree, the motion angle and the angular velocity, the blood oxygen metabolism condition of a tendon area and the pressure condition around muscles of the rehabilitation person under electrical stimulation in the rehabilitation training process. A muscle electrophysiological sensor, a joint angle sensor, a near infrared spectrum sensor, a motion capture sensor, a pressure pad sensor and the like are utilized, rehabilitation training conditions can be monitored from multiple aspects, a more detailed rehabilitation training report is generated in combination with various weight ratios, targeted training can be carried out on different individuals, various parameters can be adjusted, and the rehabilitation training efficiency is improved. The training effect is improved, training information can be fed back in time, and doctors can conveniently carry out whole-course coordination management on training.