6736 results about "Rehabilitation training" 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 provides a rehabilitation training action evaluation method and device based on multi-view vision. According to the method, a multi-view camera is adopted to synchronously collect a rehabilitation training image sequence, two-dimensional coordinates of key points of a human body are extracted by improving an HRNet deep learning model, three-dimensional reconstruction is performed in combination with a Gaussian process, motion feature data are extracted, and rehabilitation training quality is evaluated. Accurate motion capture without wearing mark points by the patient is realized, the training constraint feeling of the patient is effectively reduced, the rehabilitation evaluation accuracy is improved, and a personalized rehabilitation scheme can be generated.

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention discloses a patient rehabilitation training data acquisition method and system based on visual identification, and relates to the technical field of rehabilitation training data acquisition, and the method comprises the steps: carrying out the image segmentation of a patient rehabilitation training video through a deep learning model, and separating a patient motion feature region from the patient rehabilitation training video; marking key skeleton nodes in the patient motion feature region, and establishing a three-dimensional coordinate system of the key skeleton nodes; calculating a motion angle and displacement data between adjacent skeleton nodes according to the three-dimensional coordinates of the key skeleton nodes; and performing matching degree analysis on the motion angle and displacement data and a preset rehabilitation standard to generate a rehabilitation training score. According to the method, the deep learning model is adopted to perform image segmentation on the rehabilitation training video, the motion feature region of the patient is accurately extracted from the image, and key skeleton nodes are marked and analyzed in combination with the three-dimensional coordinate system, so that accurate acquisition of rehabilitation training data of the patient is realized.

The invention relates to the technical field of medical data processing, in particular to an integrated rehabilitation training system based on motion capture and myoelectricity feedback, which comprises a motion data acquisition module, a motion evaluation and analysis module and a control module, and is specifically integrated on a training garment through inertial motion capture equipment and a surface myoelectricity sensor. The motion state and muscle activation condition of a patient are monitored in real time, actual motion data and actual electromyographic signal data of the patient are collected, and whether the motion state of the patient meets the standard or not is judged by comparing the data deviation condition between the actual motion of the patient and the standard motion. And according to the deviation analysis result, the electrical stimulation parameters can be dynamically adjusted through closed-loop control, so that personalized and precise spinal cord electrical stimulation rehabilitation training is realized.

The present invention discloses a dyskinesia rehabilitation training method and system based on electroencephalogram signal recognition; the method includes: the convolutional neural network is used to recognize the electroencephalogram signal, analyze the patient's motor intention, convert the motor intention into a control instruction for the exoskeleton device, drive the patient's limb movement by controlling the movement of the exoskeleton device, and assist the patient to complete the movement disorder rehabilitation training. In this method, the self-attention mechanism and self-distillation training were used to establish an electroencephalogram signal recognition model, analyzing the patient's motor intention to help motor nerve remodeling, which broke the passive and single problem of traditional rehabilitation methods, realized active rehabilitation of patients, and significantly improved the rehabilitation efficacy.

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

The invention discloses a multi-modal rehabilitation data intelligent evaluation method and system based on a large model, and relates to the field related to data processing, and the method comprises the steps: carrying out the data collection based on a plurality of data sources, obtaining and integrating multi-modal data collection results, obtaining a multi-modal data set, synchronizing the multi-modal data set to an information extraction model, and carrying out the retrieval, a rehabilitation training scheme is executed to track the rehabilitation progress of a target user in real time; rehabilitation effect data is obtained and synchronized to the rehabilitation evaluation large model; a multi-dimensional evaluation result is obtained to perform feedback optimization on the rehabilitation training scheme; and a rehabilitation optimization training scheme is determined. The technical problem that traditional rehabilitation evaluation depends on professionals and lacks enough professional knowledge and experience, so that the accuracy and efficiency of evaluation are affected is solved, and the technical effects of performing intelligent personalized evaluation on rehabilitation training and improving the precision and efficiency of rehabilitation evaluation are achieved.

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 relates to the technical field of training strategy optimization, in particular to an optimization method and system based on a rehabilitation training strategy. The method comprises the following steps: carrying out real-time monitoring on physiology and training performance of a patient in a rehabilitation training process through wearable equipment, and carrying out evaluation analysis on an emotional state and athletic ability to obtain emotional feeling state data and athletic ability data of the patient; performing rehabilitation target prediction analysis according to the emotion feeling state data and the exercise ability data to obtain a prediction target of rehabilitation training of the patient; performing target feedback difference comparative analysis on the predicted target and an expected target to obtain a target difference feedback result; performing strategy optimization analysis on the current rehabilitation training strategy to generate a rehabilitation training strategy optimization scheme; and performing strategy dynamic optimization adjustment on the current rehabilitation training strategy to obtain an optimized rehabilitation training strategy. The rehabilitation training efficiency and effect of the patient can be improved.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

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 invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to a cervical vertebra rehabilitation system based on virtual reality. The cervical vertebra rehabilitation system comprises a virtual reality display module, a cervical vertebra action detection module, a central processing module, a rehabilitation training control module and a data recording and analyzing module. The virtual reality display module provides an immersive rehabilitation environment for a patient through a head-mounted display device, and the cervical vertebra motion detection module captures cervical vertebra motion data of the patient in real time through an accelerometer, a gyroscope and a magnetometer. And the central processing module fuses the multi-dimensional data through a Kalman filtering algorithm, accurately estimates the cervical vertebra posture of the patient, and synchronizes the action of the patient to a virtual environment. The rehabilitation training control module dynamically adjusts the training content according to the actual rehabilitation progress of the patient, provides a personalized rehabilitation scheme, and enhances interactivity and interestingness. The data recording and analyzing module records training data of the patient in real time and generates a rehabilitation report to help doctors to perform scientific evaluation.

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

The invention belongs to the field of remote virtual rehabilitation training, and particularly relates to a remote rehabilitation management method and system based on virtual reality. The method comprises the steps that firstly, the current health condition, the expected rehabilitation target and the needed period of a rehabilitation patient are predicted through a comprehensive evaluation algorithm and machine learning, secondly, a personalized training plan is intelligently recommended from a virtual rehabilitation training strategy library according to an evaluation result, and in the training implementation process, a sensor continuously monitors physiological indexes and micro-expression response of the rehabilitation patient; the rehabilitation progress is analyzed and evaluated through real-time data and fed back to a training strategy library in time, the training intensity and period are dynamically adjusted, and it is ensured that training is closely matched with the actual health state of a rehabilitative person; by fusing electronic medical record processing, virtual reality technology, machine learning prediction and visual emotion recognition, precise and intelligent management of rehabilitation training is realized, and rehabilitation efficiency and experience are remarkably improved.

The invention discloses a rehabilitation training evaluation system for dealing with schizophrenia patients, and relates to the technical field of data processing, the evaluation system comprises a multi-modal sensing module, an edge intelligent processing module and a dynamic graph network evaluation module; according to the technical key points, neurophysiology, behavior tracks, cognitive functions and environmental parameters are fused to form a'microscopic neural activity-mesoscopic behavior performance-macroscopic environment interaction 'full-dimension evaluation network, for example, the recessive decoupling phenomenon of'reduced brain oxygen metabolism but normal autonomic nerve function' of a negative symptom patient can be synchronously captured, and the accuracy of the evaluation network is improved. The method comprises the following steps of: firstly, quantifying the coordination and causality of data of different dimensions through a dynamic graph network, disclosing a dynamic association path of insufficient activation of a forehead cortex, social attention distraction and cognitive task error rate increase, and providing a visual basis for mechanism research and intervention target selection.

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 system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

The invention relates to the technical field of use of medical instruments, and discloses a household pelvic floor muscle rehabilitation training scheme intelligent generation method and system.In the rehabilitation training process, selection operation of a pelvic floor muscle dynamic model and a local scheme generation model is introduced, on one hand, the pelvic floor muscle dynamic model is used for elastic reaction simulation, and on the other hand, the pelvic floor muscle dynamic model is used for local scheme generation; on the other hand, a local scheme generation model matched with the real situation of the target training stage is selected, so that the real situation of the target training stage can be evaluated through the matched local scheme generation model; according to the accurate and credible local stage evaluation data and stage test data of the target training stage, a more matched targeted training scheme is obtained, accurate and high-quality data dependence is provided for adjusting a global rehabilitation training scheme, and the intelligent level of training scheme generation can be improved; therefore, the household rehabilitation training effect of pelvic floor muscles is effectively improved.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention discloses a neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.

The invention relates to the technical field of rehabilitation training detection, in particular to a rehabilitation training detection method and system based on artificial intelligence, a standard action library is constructed through standard action videos shot at multiple angles, and track and angle features of key joints are extracted for user training comparison; the skeleton key points of the user are extracted in real time through a MoveNet network, and efficient posture recognition in a home scene is achieved; analyzing position difference, angle change and acceleration characteristics by combining a space-time sequence matching algorithm, generating a dynamic matching degree index, and positioning a deviation joint to generate a correction prompt; introducing an attention mechanism model, learning the contribution degree of each joint to cycle recognition, dynamically selecting a dominant joint for action counting, recognizing starting and ending points of an action cycle through an acceleration curve, and finishing effective action statistics in combination with a dynamic threshold value, so that the counting accuracy and the self-adaptive capability are improved; therefore, the training cost is reduced, the evaluation credibility is enhanced, and accurate statistics and analysis of rehabilitation training data are realized.

The invention discloses an intelligent rehabilitation guidance system based on machine learning, and relates to the technical field of virtual rehabilitation guidance, and the system comprises an integrated multi-source sensor, collects the physiological and motion data of a user in real time, and constructs health data; fusing multi-modal data, adjusting the resistance difficulty according to the condition of the patient through virtual reality, and calculating a comprehensive health score and an action offset early warning index; setting a rehabilitation cooperation particle swarm algorithm, combining the comprehensive health score and the risk early warning index, combining the resistance difficulty and the muscle-joint cooperation coefficient to compensate action compensation, optimizing rehabilitation action parameters, and returning to the upper layer; and analyzing an abnormal root based on a structural causal model, and generating targeted rehabilitation suggestions. The problem that the risk of secondary injury is increased due to lack of dynamic regulation and control of training intensity and muscle compensation behaviors of a patient in traditional rehabilitation training is solved.

The invention discloses a foot drop assisting method based on a flexible ankle joint exoskeleton, and belongs to the field of rehabilitation medical assistance. According to the method, an unhealthy side ankle joint angle is input into a self-adaptive oscillator to learn related parameters in the self-adaptive oscillator, and an affected side ankle joint angle is predicted; meanwhile, a CNP model is adopted to predict the ankle joint angle of the shank on the affected side according to the gait phase estimated by the self-adaptive oscillator, two prediction results of the ankle joint angle of the shank on the affected side are subjected to weighted fusion to finally obtain the expected angle of the ankle joint on the affected side, and the fused ankle joint angle prediction output is more stable and continuous. And a weighted fusion result is smoothed. According to the method, the normal walking mode based on the unhealthy side ankle joint angle of the patient can be dynamically learned online in real time, and personalized affected side ankle joint angle reference tracks at each moment are generated for different patients, so that the abnormal motion mode of the patient is inhibited, and the patient is guided to reach the normal gait in the rehabilitation training process.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention discloses an intelligent rehabilitation training evaluation method, device and equipment based on multi-modal information fusion and a computer readable storage medium, and the method comprises the steps: synchronously collecting user action video data and electromyographic signal data for a user performing rehabilitation training; aligning the user action video data and the electromyographic signal data based on the first timestamp and the second timestamp, and respectively generating a synchronous user posture feature sequence and a synchronous user electromyographic feature sequence; generating a comprehensive rehabilitation evaluation report by applying a feature layer fusion dynamic time warping algorithm; updating the personalized evaluation benchmark when the benchmark updating condition is met; when the stage promotion condition is met, determining that the user enters a new rehabilitation stage; on the basis of the comprehensive rehabilitation evaluation report and the new rehabilitation stage, targeted rehabilitation training guidance is generated and output. The method has the advantages of accurately identifying and deeply diagnosing compensatory actions and providing a personalized and intelligent adaptive rehabilitation process.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.