217 results about "Rehabilitation evaluation" patented technology

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 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 relates to the technical field of medical rehabilitation, and particularly discloses an anorectal postoperative rehabilitation nursing evaluation method and system based on a sensor. According to the scheme, the system comprises a multi-source physiological information sensing module, a data preprocessing and feature extraction module, a multi-modal data fusion analysis module, a dynamic risk assessment and early warning module and a man-machine interaction and decision support module, multi-dimensional physiological signals are continuously collected, a rehabilitation state index is generated through fusion, the complication risk is assessed in real time, and the rehabilitation effect is improved. Early warning and accurate nursing decision support are realized, and comprehensiveness and timeliness of rehabilitation evaluation are improved.

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

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention discloses a medical device and rehabilitation software information system based on the Internet of Things, and particularly relates to the technical field of communication transmission management, which constructs event-anchored linear alignment on an edge side, takes a real-time channel quality vector as input, and generates a compression strategy through a Pareto multi-arm bandit, so that the compression strategy is compressed, and the communication transmission efficiency is improved. Meanwhile, a dynamic compression offset index is used as a safety gate, so that a self-adaptive compression decision considering distortion and time delay is realized, and the technical problem that the time sequence consistency and the diagnosis integrity of the multi-modal physiological signal are difficult to guarantee simultaneously under the conditions of clock drift and bandwidth limitation is solved; a cross-modal alignment quality monitoring module is arranged and used for recognizing and restraining cross-modal dislocation false correlation in real time, so that it is ensured that rehabilitation evaluation and short-period diagnosis are established on a reliable time sequence corresponding relation, and the dislocation problem existing in electrocardiosignals and action signals is solved.

The invention relates to the technical field of physiological parameter monitoring, in particular to a construction method of a cardiology rehabilitation evaluation model, which comprises the following steps: monitoring a systolic pressure peak value, a diastolic pressure valley value and duration time of a postoperative patient, constructing a waveform parameter set, calculating a peak sequence amplitude change rate through a moving average method, and calculating a peak sequence amplitude change rate. Marking a form abnormal period and a structure drift region; dividing an active phase and a passive phase to extract wave crest characteristics; analyzing time sequence difference to generate a three-dimensional rhythm vector; constructing a regression model; according to the method, heart pressure change is continuously monitored, a waveform structure is identified, compression period change is dynamically captured, a structure drift area is determined and positioned by combining an amplitude change rate and a difference value, multi-dimensional heart rhythm characteristics are extracted, day and night differences are analyzed, and multi-day deviation values are integrated to generate a scoring system; the abnormal recognition, rhythm quantification and trend prediction capabilities are improved, and a high-adaptability quantification basis is provided for rehabilitation evaluation.

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

The invention relates to the technical field of data processing, and discloses a gait rehabilitation evaluation method and system based on spatial-temporal characteristics of multi-modal data, a terminal and a storage medium, and the method comprises the steps: synchronously collecting myoelectricity, electroencephalogram and exoskeleton robot IMU signals of a dyskinesia crowd in a certain motion normal form in a specific period interval; a motion function scoring result of a doctor on a patient according to a scale is synchronously recorded, data preprocessing, channel selection and feature extraction are performed on myoelectricity, electroencephalogram and exoskeleton robot IMU signals, a model is established based on a neural network, motion evaluation is performed according to a fused feature array, and a motion function quantitative score is obtained; and the motion function state of the evaluated person is graded according to the score. According to the method, the multi-modal synchronous data rehabilitation evaluation model of the movement function of the dyskinesia crowd is constructed according to multiple information mining, and the movement function state of the evaluated person is rapidly evaluated, predicted and graded.

The invention relates to the technical field of knee joint rehabilitation evaluation, in particular to a knee joint rehabilitation evaluation method based on a human skeleton by using a space-time diagram convolutional network, and the method comprises the following steps: obtaining a rehabilitation evaluation gait video set; extracting human body key point skeleton data, and slicing the human body key point skeleton data into gait samples according to the number of time frames; preprocessing the gait sample, and extracting joint, skeleton and joint angle features; a gait-link method is adopted to divide a space gait skeleton diagram, a space-time attention mechanism is added, and an improved CTR-GCN network model is constructed; training the improved CTR-GCN network model by using the gait sample to obtain a gait evaluation model; and processing a gait video to be evaluated, and inputting the processed gait video into the gait evaluation model to obtain a rehabilitation evaluation result of the patient. According to the method, the human body key point skeleton in the walking video of the patient is extracted and input into the space-time diagram convolutional network for training, the rehabilitation evaluation model is obtained, the rehabilitation condition of the patient is evaluated by using the model, and the requirement of rehabilitation evaluation is met.

The invention belongs to the technical field of artificial intelligence, and discloses an operation behavior characteristic-driven rehabilitation training effect dynamic evaluation method, which comprises the following steps of: extracting compensatory behavior characteristics and recessive behavior mode characteristics by acquiring multi-dimensional operation behavior data and physiological sensor data of a patient on a simulated driving device; and constructing a behavior pattern migration feature set. And calculating a behavior pattern migration index based on the compensatory behavior characteristics in the characteristic set, and accurately identifying the progressive transformation process of the patient from the compensatory behavior to the normal behavior. Further constructing a rehabilitation capability dynamic evaluation model, generating a real-time rehabilitation capability score, dynamically adjusting the complexity of a simulated driving scene, collecting adaptive behavior data, constructing a behavior mode migration trend curve and a rehabilitation effect prediction model, and realizing closed-loop optimization of a personalized training scheme. According to the method, subjectivity and static limitation of traditional rehabilitation evaluation are broken through, and the accuracy and effectiveness of neural rehabilitation are remarkably improved.

The invention relates to an electrical stimulation detection system and method for evaluating rectum sensation and anal sphincter reflex functions. The system comprises a stimulation unit which comprises a program-controlled electrical stimulator and an intra-rectum stimulation electrode used for applying monopulse electrical stimulation to the rectum wall; a recording unit including an anal sphincter response recording device for capturing anal sphincter reflex activity; and the central control and data processing unit is used for coordinating electrical stimulation, reaction recording and data analysis. The method comprises the following steps: arranging a stimulating electrode and recording equipment; applying monopulse electrical stimulation to the rectum wall; synchronously recording the reflective reaction of the anal sphincter; and performing quantitative analysis on the stimulation parameters and the reaction signals to determine a reflection threshold value, an incubation period, an amplitude and duration time. According to the system and the method, objective evaluation on the integrity of the reflex arc of the anorectum and the afferent sensitivity of the rectum feeling is realized through accurately controlled electrical stimulation and objective reaction quantification, and a reference basis is provided for diagnosis and rehabilitation evaluation of related dysfunctions.

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 relates to the technical field of intelligent medical rehabilitation evaluation, and discloses a biological index detection system for orthopedic rehabilitation evaluation. According to the system, biological signals of a patient are collected through a multi-sensor array of a biological signal sensing module to generate an original data stream; the quality self-adaptive evaluation module carries out real-time quality analysis on the data and generates a quality parameter set; the signal intelligent processing module dynamically adjusts a signal processing strategy according to the quality parameter set and then outputs optimized signal data; the feature depth extraction module is used for mining biological features from the optimized signal data and generating feature weight distribution; the rehabilitation dynamic scoring module calculates a rehabilitation evaluation index based on the feature weight distribution; and the intervention strategy generation module automatically adjusts an evaluation strategy according to the rehabilitation evaluation index and outputs a personalized evaluation report. According to the invention, through real-time quality monitoring and adaptive signal processing, the accuracy and reliability of orthopedic rehabilitation evaluation are improved.

The invention provides an electromyographic signal fatigue detection method based on time window analysis, and the method comprises the steps: carrying out the preprocessing of collected electromyographic signals, and carrying out the self-adaptive time window segmentation based on a sliding overlapping mechanism; performing spectral analysis on each time window signal by using a Wilch method, and extracting frequency domain features such as median frequency, average power frequency and power spectral density ratio; a normalized composite fatigue index is constructed, and smoothing processing is carried out through multi-time scale moving average; finally, a self-adaptive multi-level threshold judgment mechanism is introduced, and fatigue state level recognition and dynamic updating are achieved. The fatigue evolution process can be continuously and quantitatively reflected, and the practicability and the intelligent level of electromyographic signal analysis under the scenes of training monitoring, man-machine work efficiency, rehabilitation evaluation and the like are improved.

The invention provides a bone fracture rehabilitation dynamic management system and method based on multi-source data analysis. The method comprises the steps that multi-modal physiological data and biomechanical indexes of a target patient in the bone fracture rehabilitation training process are collected; constructing a multi-modal evaluation model based on a space-time convolutional neural network, and inputting the multi-modal physiological data into the multi-modal evaluation model to obtain a multi-modal rehabilitation evaluation score of the target patient; extracting a drift rate of a joint moment center and a chaos characteristic quantity of gait phase deviation in the key biomechanical indexes, performing deviation calibration on endogenous state parameters of the multi-modal evaluation model through the drift rate and the chaos characteristic quantity, and outputting rehabilitation deviation characteristics of a target patient; and generating a digital monitoring report and a personalized adjustment suggestion for bone fracture rehabilitation training of the target patient through the rehabilitation deviation characteristics and the multi-modal rehabilitation evaluation score. Based on the scheme, personalized stage adjustment of digital bone fracture rehabilitation training can be realized.

The invention provides a motor imagery-based rehabilitation evaluation method and system and a storage medium, and the method comprises the steps: obtaining electroencephalogram signal data collected by a subject during the execution of a motor imagery-based rehabilitation task, and extracting the frequency domain features of the electroencephalogram signal data; inputting the frequency domain characteristics of the electroencephalogram signal data into a pre-trained encoder for encoding, and outputting to obtain embedded representation of the electroencephalogram signal data; inputting the embedded representation of the electroencephalogram signal data into a natural language decoder, and outputting to obtain a rehabilitation evaluation result of the subject; wherein in the process of training the signal encoding module to obtain the encoder, the signal encoding module is updated through encoding loss, so that the embedding representation of the label text and the embedding representation of the corresponding electroencephalogram signal data realize cross-modal alignment. According to the method, the end-to-end generation from the original electroneurographic signal to the multi-modal evaluation information can be realized.

The invention discloses a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention relates to the technical field of medical informatization, in particular to an intelligent rehabilitation assessment and data analysis method and system.The method comprises the steps that multi-source heterogeneous rehabilitation assessment data collected by a wearable sensor when a patient executes a preset rehabilitation action is obtained in real time; based on a preset time window, processing the multi-source heterogeneous rehabilitation evaluation data into a series of time sequence dynamic graphs; inputting the series of time sequence dynamic graphs into a pre-trained time sequence dynamic graph neural network model, and generating a quantized rehabilitation evaluation result based on the output of the time sequence dynamic graph neural network model. According to the method, deep fusion and time sequence calibration of the multi-source heterogeneous rehabilitation data are realized, the precision of an evaluation model and the recognition capability of a complex motion mode are remarkably improved, the robustness of the model to noise and data artifacts is enhanced through adaptive weighting of the data quality, and the accuracy of the evaluation model is improved. And the reliability and the stability of a rehabilitation evaluation result in a real clinical environment are ensured.

A rehabilitation evaluation system for a patient with central nervous system dysfunction comprises the following steps of S1, collecting videos, motion data and pressure data of a user through a data collection layer, S2, uploading the video data to an edge computing server, performing preliminary posture estimation, and S3, performing rehabilitation evaluation. The method comprises the following steps: S1, carrying out edge calculation on the data, S3, transmitting the data subjected to edge calculation to a cloud server for deep modeling analysis of gait and balance parameters, S4, carrying out abnormal mode identification and comparison after parameter analysis, S5, uploading the analyzed data after comparison to a decision application layer, S6, transmitting each parameter and a corresponding processing scheme to a doctor end for a doctor to check, and S5, carrying out data processing. The method has the advantages that through full-link innovation of multi-mode lightweight acquisition, edge cloud hierarchical processing, double-model intelligent diagnosis, reinforcement learning dynamic optimization and visual remote decision, central nervous rehabilitation evaluation extends to a daily life scene from a laboratory, and core breakthrough of precision improvement, efficiency multiplication and personalized enhancement is achieved.

The invention relates to the technical field of intelligent medical rehabilitation and computer vision, in particular to a muscle-bone rehabilitation posture motion analysis system based on visual recognition. Comprising a visual analysis management center, a motion feature extraction unit, a dynamics inversion unit, a compensation coupling analysis unit, a time sequence conduction unit and a rehabilitation evaluation decision unit. Geometric compliance is analyzed and discriminated through dominant kinematics characteristics; if so, performing visual moment inference consistency analysis by using a dynamic inversion unit; in response to moment discretization, acquiring and analyzing a compensation coupling coefficient and kinematic chain conduction delay respectively; and finally, calculating a compensation evaluation index through multi-dimensional compensation quantitative matching analysis, and generating a recessive compensation or effective driving signal. According to the method, a layered filtering mechanism is constructed, implicit problems are dominated, and the blank of monitoring of the internal force exerting mode in non-contact rehabilitation evaluation is filled up.

The invention discloses a lumbar postoperative functional recovery evaluation method based on gait analysis, and the method comprises the steps: collecting a multi-modal gait multi-source signal, and building high-quality space-time synchronous multi-modal gait data through standardization processing and abnormity elimination; further extracting multi-point features such as an action sequence, pressure and myoelectricity, and realizing deep association between gait features and clinical evaluation indexes by adopting graph structure fusion and knowledge graph mapping; a graph neural network and an attention mechanism are introduced, the causal relationship between gaits and lumbar function recovery is reasoned, self-supervised dynamic optimization is performed according to individual differences, the objectivity, interpretability and adaptive ability of rehabilitation evaluation can be improved, and the rehabilitation accuracy is improved. And reliable data basis and causal traceability are provided for medical decision and personalized rehabilitation.

The invention relates to a colorectal cancer postoperative patient intelligent follow-up visit management system which comprises a center module composed of a B / S framework, a database server and a user interface and further comprises a patient information management module in shared connection with the center module. The system is further provided with a function module and a support module which are connected with the patient information management module. The function module comprises a follow-up plan making module, a multi-channel follow-up execution module, a rehabilitation evaluation module and a health propaganda and education module. The support module comprises a data statistics and analysis module, a medical resource coordination module and a family member support module. According to the intelligent follow-up visit management system, through the electronic patient information management module, a patient information database, a dynamic health file and a multi-terminal access function are set, and centralized management and real-time sharing of medical data are achieved.

The invention belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation. A multi-dimensional skeleton key point confidence evaluation mechanism is established by integrating three types of sensing data of visual skeleton tracking, an inertial measurement unit and pressure distribution, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. And a multi-modal fusion correction model is adopted to carry out accurate compensation on error recognition, missing data are reconstructed in combination with a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm, and a continuous and complete all-day skeleton motion sequence is generated. On the basis of the calculation, the time-phased activity level index and the rehabilitation action completion degree score of the patient are calculated, and an objective and quantitative rehabilitation evaluation basis is provided. According to the invention, the stability and continuity of skeleton tracking in a complex rehabilitation environment are improved, so that a medical team can obtain a real and complete all-day activity portrait of a patient, accurately position a rehabilitation bottleneck, formulate a personalized treatment scheme and promote functional recovery of the patient.

The present application relates to the field of medical information technology, in particular to a fracture postoperative rehabilitation evaluation system based on data feedback. It comprises: a data acquisition module for real-time acquisition of multi-modal data of patients in real-world scenarios; a feature extraction module for synchronous, denoising and normalization processing of multi-modal data, and calculation of multi-modal fusion feature vectors; a time series dynamic model module for dynamically estimating rehabilitation stress tolerance boundaries according to the multi-modal fusion feature vectors; a rehabilitation load instruction generation module for generating personalized rehabilitation load instructions according to the rehabilitation stress tolerance boundaries. The system realizes dynamic quantification and personalized guidance of rehabilitation evaluation; the scheme instantaneously collects multi-modal data of patients in real-world scenarios through wearable devices, and establishes a closed-loop feedback mechanism, overcoming the problems of subjective judgment, information lag and lack of personalization in traditional rehabilitation evaluation, making rehabilitation guidance precise and instant.

The invention discloses an elderly lower limb rehabilitation training assessment method and system based on enhanced training, and relates to the technical field of intelligent exercise rehabilitation assessment, and the method comprises the following steps: S100, building an individual rhythm observation system, continuously collecting lower limb muscle contraction rhythms and stride rhythms, generating a rhythm data set, and storing the rhythm data set; a complete rhythm fingerprint band is extracted from the rhythm data set to serve as a benchmark reference for frequency regulation and control; and S200, performing micro-amplitude frequency sweeping based on the rhythm fingerprint band, analyzing rhythm data changes in real time, identifying fine vibration peak characteristics in rhythms, calibrating a phase overlapping region, and generating a rhythm segment. According to the method, through the steps of rhythm fingerprint construction, risk rhythm recognition, buffering and energy dissipation introduction and the like, dynamic regulation and control of the lower limb rehabilitation training rhythm of the old people are achieved, high-frequency resonance is avoided, the injury risk is reduced, and the training safety and the individual rhythm adaptability are improved.

The invention discloses a kinematics monitoring method for rehabilitation after hip replacement, and relates to the technical field of kinematics monitoring. Comprising the following steps that monitoring elements are arranged, IMU sensor groups are arranged at hip joints and thighs, and a power module, a processor and a Bluetooth communication module are worn on a human body; data acquisition: the IMU sensor group acquires vector data of hip joints and thighs and transmits the vector data to the processor, the processor transmits the data to the PC terminal through the Bluetooth communication module, the PC terminal calculates a quadruple of hip bone angles and a quadruple of leg bone angles at the same time, then subtracts the quadruple of hip bone angles and the quadruple of leg bone angles to obtain a quadruple of relative included angles, and calibrates the quadruple of relative included angles; a plurality of standard poses in rehabilitation evaluation after hip replacement are selected as calibration references. A corresponding monitoring scheme is designed based on the multiple IMU sensors, a software and hardware system is formed, the hip joint angle of the patient is measured and calculated in real time, and a doctor is assisted in evaluating and guiding the postoperative rehabilitation exercise intensity of the patient.

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

The invention relates to a lower limb function rehabilitation evaluation method based on a multi-dimensional feature fusion network, and belongs to the technical field of intelligent evaluation of lower limb function rehabilitation conditions. The method comprises the following steps: acquiring multichannel surface electromyogram signals of a stroke patient and preprocessing the multichannel surface electromyogram signals to construct a data set; constructing a lower limb function rehabilitation evaluation model which comprises a time domain feature extraction branch, a frequency domain feature extraction branch, a first cross attention module, a second cross attention module and a classifier; inputting samples in the data set into a lower limb function rehabilitation evaluation model, and training the model; optimizing the model by adopting a loss function, updating parameters by using an AdamW optimizer, and minimizing loss through a back propagation algorithm until the model converges to obtain a trained model; and preprocessing the to-be-evaluated surface electromyogram signal, and inputting the preprocessed to-be-evaluated surface electromyogram signal into the trained model to obtain an evaluation result The accuracy of lower limb function rehabilitation evaluation can be improved.