147 results about "Rehabilitation evaluation" patented technology

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 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 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 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 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.

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 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 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 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.

The invention discloses a daily life activity ability assessment method and system based on a multi-branch space-time fusion network. The method comprises the steps of collecting motion data of a to-be-evaluated object, performing segmentation, noise reduction and standardization preprocessing, and extracting time-frequency domain features and motion deviation degree, motion similarity index and bilateral symmetry index provided by the invention; obtaining a uniform Bhattacharyya index scale score; and taking the motion feature data set and the clinical score as input, performing training based on a multi-branch space-time fusion algorithm, constructing a multi-task prediction model, and realizing automatic evaluation of the ADL. According to the method, the multi-branch space-time fusion network is innovatively introduced, the relevance between tasks is fully utilized, the spatial feature extraction capability is enhanced in combination with the graph neural network, adaptive fusion of the features is realized through a cross-layer interactive attention mechanism, the model prediction precision and evaluation reliability are remarkably improved, and the method is suitable for large-scale popularization and application. And an effective technical support is provided for rehabilitation evaluation and personalized treatment of patients with dyskinesia and disability and dementia.

The invention discloses an upper limb rehabilitation training device, method and system and a storage medium, and the method comprises the steps: constructing a virtual scene according to the rehabilitation condition of a patient, and combining rehabilitation paradigms from a paradigm library; high-channel electroencephalogram signals are collected, and real-time quality monitoring is conducted; extracting motor imagery features from the electroencephalogram signals and analyzing the motor imagery features into a motor intention; mapping the motion intention to a virtual limb to realize VR / AR body feedback; and behavior data such as concentration degree are calculated and displayed in real time, and personalized rehabilitation evaluation is completed. By the adoption of the technical scheme, the problems that in existing cerebral apoplexy rehabilitation treatment, the normal form has body deficiency, the training scene is single, medical intervention lacks real-time performance, rehabilitation evaluation is high in subjectivity, and individuation is insufficient are solved.

The invention relates to the technical field of respiratory function monitoring, and discloses a pulmonary ventilation blood flow distribution real-time monitoring method and evaluation system based on a sensor. The method comprises the following steps: acquiring multi-modal data such as a blood oxygen saturation waveform, an airflow rate waveform and a thoracic pressure waveform of a lung area; performing time synchronization processing on the data to generate a time alignment sequence; dividing the data into an inspiration phase, an expiration phase and a transition phase according to a preset rule; extracting time-frequency domain features from each phase segment, and constructing a multi-dimensional respiration feature matrix; identifying an abnormal breathing mode and marking an abnormal section through breathing phase switching frequency analysis; decomposing the normal segment feature matrix by using a lung pathological feature library to generate a pathological feature vector; a pathological feature vector is optimized through a ventilation and blood flow coupling model; and carrying out abnormal region positioning based on the lung pathology propagation network, and generating a lung partition function evaluation result. The method realizes non-invasive and real-time monitoring, and is suitable for scenes such as clinical monitoring and rehabilitation evaluation.

The invention belongs to a biological signal identification and data processing technology, and relates to a multi-sensor fusion action identification system for shoulder rehabilitation evaluation. Comprising a flexible strain sensor, an inertial measurement unit, a multi-channel data acquisition module, a data preprocessing module and an action recognition module, the flexible strain sensor comprises a plurality of sensing areas and connecting areas which are distributed around the rotator cuff area; the multichannel data acquisition module acquires multichannel sensing signals, and the multichannel sensing signals are preprocessed by the data preprocessing module. The action recognition module adopts a multi-modal feature fusion algorithm based on a bidirectional cross-modal interactive attention and time period specificity adaptive weight mechanism to realize dynamic correlation modeling between a strain sensing signal and inertial measurement data in a unified time sequence space, and cross-modal time sequence features are fused; capturing the global and local dependency relationship of the shoulder movement, and recognizing various rehabilitation actions. The method effectively overcomes the static limitation of the prior art, and remarkably improves the precision and robustness of shoulder rehabilitation evaluation.

The invention discloses a critical patient rehabilitation evaluation system based on artificial intelligence, particularly relates to the technical field of medical information and intelligent rehabilitation evaluation, and is used for solving the problems that in an intensive care unit, multi-source data are not synchronous, nursing and treatment events are often caused, sedation and ventilation fluctuation pollution is caused, and the problem of serious illness is solved. And thus, the problem of distortion in judgment of which level cannot move nowadays and can move to can be solved. A unified time base is established through three types of anchor points of nursing, body position and cardiopulmonary rhythm, passivity and autonomy are judged in an influence time period, and the passivity is deducted and reserved; and then sedation, circulation, respiratory support and medication are compiled into safety budget with delay, linkage evidence quantity and uncertainty adjustment release rhythm, and degradation and rollback are triggered by an anti-fact track of margin segmentation, so that an executable prescription of'activity, reachable grade and reason 'is output beside a bed, event pollution and physiological fluctuation are inhibited, and the aim of preventing and treating the patient is fulfilled. And maloperation and missed operation are reduced.

The invention relates to the technical field of biomedical engineering, and discloses a rehabilitation evaluation method based on ICF-RS. Respectively arranging myoelectricity and torque acquisition units at the target muscle group and the corresponding joints, and synchronously acquiring according to a preset frequency at a unified starting moment; performing statistical standardization and integral second on the myoelectricity, absolutely averaging the torque second, and pairing with the myoelectricity second by second; establishing a first-order linear dynamic model which takes the two sequences as input and contains a natural fallback item, and discretizing according to the step length of the whole second to obtain a rehabilitation state sequence; calculating an average recovery rate according to the end state and the evaluation duration, comparing the average recovery rate with a reference rate conversion score, and mapping the average recovery rate to walking ability and muscular endurance grades in an ICF rehabilitation scale; second-level data, states and scores are archived, and the change rate is calculated day by day to judge the trend.

The invention provides a rehabilitation assessment method and device, electronic equipment and a storage medium, and relates to the field of data processing, the rehabilitation assessment method and device are applied to a system, the system comprises VR equipment, the electronic equipment and an electrical stimulator, the VR equipment comprises display equipment and a data glove, and the method comprises the following steps: guiding a user to execute a first target task through the VR equipment, collecting first task data when the user executes the first target task, and determining a first score of the first target task according to the first task data; guiding the user to execute a second target task through the VR equipment, collecting second task data when the user executes the second target task, and determining a second score of the second target task according to the second task data; guiding the user to execute a third target task through the VR equipment, collecting third task data when the user executes the third target task, and determining a third score of the third target task according to the third task data; and determining a rehabilitation evaluation score of the user according to the first score, the second score and the third score.

This invention discloses a method and system for evaluating the effectiveness of cardiac rehabilitation, relating to the field of medical and health technology. The method includes: acquiring physiological monitoring data, individualized parameter data, comorbidity data, multidrug therapy data, pharmacokinetic parameters, and pharmacodynamic interaction parameters of the target patient; generating baseline physiological index data without drug intervention based on the individualized parameter data and comorbidity data through a pre-set individualized cardiac response baseline model; calculating drug concentration based on multidrug therapy data and pharmacokinetic parameters; calculating comprehensive pharmacodynamic data by combining pharmacodynamic interaction parameters; generating predicted physiological data under multidrug intervention; subsequently obtaining purified physiological data through adaptive filtering and frequency domain decomposition; and finally generating a rehabilitation effect score through a pre-trained long short-term memory network model. Its beneficial effects include: distinguishing between drug interference and real cardiac function improvement, and improving the accuracy of rehabilitation assessment for patients undergoing multidrug therapy.

The application belongs to biological signal recognition and data processing technology, and is a multi-sensor fusion motion recognition system for shoulder rehabilitation evaluation. The system comprises a flexible strain sensor, an inertial measurement unit, a multi-channel data acquisition module, a data preprocessing module and a motion recognition module. The flexible strain sensor comprises a plurality of sensing areas and connecting areas distributed around the rotator cuff area. The multi-channel data acquisition module acquires multi-channel sensing signals, which are preprocessed by the data preprocessing module. The motion recognition module adopts a multi-modal feature fusion algorithm based on bidirectional cross-modal interaction attention and time period-specific adaptive weight mechanism to realize dynamic correlation modeling between strain sensing signals and inertial measurement data in a unified time sequence space, fuse cross-modal time sequence features, capture global and local dependency of shoulder movement, and recognize multi-class rehabilitation actions. The application effectively overcomes the static limitation of the prior art and significantly improves the accuracy and robustness of shoulder rehabilitation evaluation.

The invention belongs to the technical field of intelligent evaluation, and provides a rehabilitation evaluation system and method based on multi-modal flexible sensing, and the system comprises a multi-modal flexible sensing glove and a data processing device. The multi-mode flexible sensing glove comprises a contact layer, a sensing layer, a protective layer and a control assembly, the contact layer, the sensing layer and the protective layer are sequentially arranged from inside to outside, the control assembly is arranged on the protective layer and located on the back of the glove, and a sensing assembly is arranged in the sensing layer; the sensing assembly is used for collecting multi-modal data around a target part, and the control assembly is used for primarily processing the multi-modal data; and the data processing equipment is used for performing rehabilitation evaluation on the target part through wavelet decomposition by using a rehabilitation evaluation model established based on a convolutional neural network and a long-short-term memory network according to the primarily processed multi-modal data to obtain a rehabilitation evaluation result. According to the scheme provided by the invention, the accuracy, reliability and feasibility of a rehabilitation evaluation link are improved.

The invention discloses an AI measuring method for orthopedic postoperative rehabilitation in combination with an auxiliary measuring tool, and the method comprises the steps: surrounding a to-be-measured limb part through an elastic measuring tool configured with a standard geometric reference object, and collecting a measurement image containing the reference object and a deformable section through a mobile terminal; performing perspective correction processing on the measured image to eliminate deformation caused by a shooting angle, and calculating a conversion coefficient of image pixels and an actual physical size based on a standard geometric reference; the contour pixel length of the deformable section in the image is extracted through an edge detection algorithm, and the actual physical elongation of the section is calculated by combining the conversion coefficient; according to the fixed base length and the actual physical elongation of the elastic measurement tool, the limb girth is automatically calculated, and a rehabilitation evaluation report containing measurement data and historical comparison results is generated. By utilizing the embodiment of the invention, non-contact and high-precision automatic limb circumference measurement can be realized, and the objectivity and convenience of rehabilitation monitoring are improved.

The invention discloses a flexible piezoresistive strain sensor and a preparation method thereof, and an action recognition method and system, the flexible piezoresistive strain sensor takes a flexible polymer material as a matrix, and a plurality of conductive fillers are compositely arranged in the matrix, so that the conductive fillers form a continuous composite conductive network structure in a dispersion manner; therefore, stable resistance change is generated under the action of external strain. Based on human body joint strain signals collected by the sensors, resistance signals are preprocessed, and a multi-layer perceptron neural network model is constructed, so that human body actions of different joints and different bending angles are classified and recognized. The structure stability of the flexible piezoresistive strain sensor and the realizability of action recognition are both considered, and the method is suitable for application scenes such as human motion monitoring, rehabilitation evaluation and human-computer interaction.

The invention discloses a refined evaluation management system for rehabilitation training of the disabled, and relates to the technical field of rehabilitation training of the disabled, the refined evaluation management system comprises an analysis module and a management module, the analysis module comprises a personnel analysis unit and a behavior analysis unit; a personnel analysis unit; performing data processing and training on the acquired information of the disabled to obtain a rehabilitation analysis set containing rehabilitation coefficients; and the behavior analysis unit is used for monitoring and evaluating rehabilitation training of the disabled to obtain a rehabilitation monitoring set containing rehabilitation evaluation values. Through the arranged analysis module, information of the disabled can be collected and analyzed, then a rehabilitation analysis set of rehabilitation coefficients of the disabled is obtained, rehabilitation training is conducted on the disabled, a rehabilitation monitoring set is obtained, and then adjustment of the disabled in rehabilitation training is obtained by analyzing the rehabilitation monitoring set. Therefore, the disabled can be evaluated and managed finely in the rehabilitation training process, and the rehabilitation effect is greatly improved.

The invention discloses a multi-parameter accelerated rehabilitation evaluation method for stomach cancer postoperative ERAS management, and belongs to the technical field of medical data processing, and the method comprises the steps: obtaining stomach cancer postoperative multi-source data of a patient, carrying out the preprocessing and fusion analysis of the stomach cancer postoperative multi-source data of the patient, and forming stomach cancer postoperative fusion data of the patient; according to the multi-parameter accelerated rehabilitation evaluation model, analyzing and identifying the gastric cancer postoperative fusion data of the patient, determining a gastric cancer postoperative multi-parameter accelerated rehabilitation evaluation result of the patient, and realizing dynamic risk evaluation of gastric cancer postoperative rehabilitation of the patient. The method solves the problem that the existing method is high in subjectivity and difficult to comprehensively consider multi-dimensional data at the same time, so that the gastric cancer postoperative rehabilitation management effect of the patient is poor. The method does not have a quantitative standard for judging whether the patient reaches the standard or not, multi-dimensional data such as laboratory indexes, vital signs, pain scores and activity amount can be comprehensively considered at the same time, and the gastric cancer postoperative rehabilitation management effect of the patient can be improved.

The invention relates to the technical field of health monitoring, in particular to a multi-device collaborative rehabilitation evaluation system and method, and the system comprises a non-contact signal receiving and transmitting device, a gait collection device, an intelligent bracelet and a central processing unit. The non-contact signal transceiver is used for monitoring gait and physiological signals in a non-contact manner, the gait acquisition device acquires plantar pressure data, the smart bracelet records exercise intensity and physiological indexes, and the central processing unit realizes multi-modal data fusion and analysis. Through cooperative work of multiple devices, the problems of single-mode sensing limitation, poor scene adaptability and the like are solved, all-weather and multi-scene health assessment is achieved, the gait detection precision is improved, and the method is suitable for rehabilitation assessment scenes.

The invention discloses a rehabilitation treatment evaluation method and system based on wearable equipment, and relates to the technical field of medical rehabilitation. The system comprises a wearable sensing module, a wireless communication module, a cloud data processing module, a man-machine interaction module and an auxiliary stimulation module. The wearable sensing module collects joint movement, muscle movement and stress parameters in a non-invasive mode. According to the method, through closed-loop processes of equipment preprocessing and personalized configuration, adaptive model training, real-time data acquisition, cloud quantitative evaluation and multi-dimensional feedback adjustment, full-chain coverage of'perception-analysis-evaluation-feedback 'of rehabilitation treatment is realized. According to the method, the defect that a cognitive movement therapy lacks quantitative indexes is overcome, the problems that traditional rehabilitation evaluation is subjective and a sensor can only detect and cannot assist in treatment are solved, rehabilitation treatment standardization, individuation and high efficiency are achieved, the labor cost is reduced, the rehabilitation quality is improved, and the method is suitable for rehabilitation scenes such as cerebral apoplexy and movement injury.

The invention discloses an experimental rat hind limb platform type gait device and a control method thereof, and relates to the technical field of experimental animal behavior training and rehabilitation evaluation.The device comprises a fore limb positioning platform, a hind limb driving platform, a safety protection assembly, a data acquisition unit and a control unit; the control unit is used for generating an optimal track and driving the hind limb driving platform to move, specifically, target track parameters are set according to the injury state of the rat, the hind limb driving platform is started, multi-target optimization is conducted on the movement track of the hind limb driving platform based on the target track parameters, and meanwhile gait data of the rat are monitored in real time. When the gait data is abnormal, the hind limb driving platform is closed, and an alarm is given; according to the invention, individualized, intelligent and safe control of a rat gait training process can be realized, so that a motion track is adaptive to gait characteristics of different repair stages, and the accuracy of rehabilitation evaluation and the reliability of an experimental result are improved.

The present application belongs to the technical field of image processing, and particularly relates to a real-time monitoring method for human rehabilitation training of a rehabilitation device, which comprises the following steps: calculating a local motion energy factor according to the distribution of instantaneous speed and instantaneous acceleration in a local trajectory segment at each time point, calculating a local ideal trajectory and a root mean square error of the local trajectory segment to obtain a local trajectory stability, combining the two with a local average speed to calculate a dynamic detail saliency score, adjusting a global basic threshold to obtain an adaptive distance threshold, applying a Douglas-Poke algorithm to process an original motion trajectory sequence to obtain an optimized trajectory sequence, calculating a training stability of a patient according to a root mean square error of an ideal motion trajectory sequence and the optimized trajectory sequence, and generating a visual rehabilitation report. The present application effectively filters random noise and smooths the motion trajectory while retaining pathological detail features that affect rehabilitation evaluation.

The invention belongs to the technical field of medical data mining and rehabilitation evaluation, and provides a skeletal muscle spasm-to-contracture evolution rule quantitative analysis method and system and application, and the method comprises the steps: preprocessing spasm contracture time sequence data, and obtaining one-dimensional feature vector data; k-means clustering is carried out, state labeling of spasm and contracture is carried out, and clustering labels are obtained; determining the change rate of the torque characteristic and the adjacent angular velocity characteristic, and generating an eight-bit pseudo-sequential sequence representing the evolution rule from the spasm state to the contracture state in combination with a state transition threshold value determined by an ROC curve; constructing a Markov chain model to calculate a transition probability matrix from a spasm state to a contracture state, and identifying torque as a key driving feature of state evolution through grouping risk ratio; and constructing a dynamic correlation model based on the key driving features to complete quantitative analysis of the evolution rule from skeletal muscle spasm to contracture. According to the method, objective division from skeletal muscle spasm to contracture state can be realized, and the transition probability and key driving factors between the skeletal muscle spasm and the contracture state can be excavated.

To provide a rehabilitation program for an orthopedic patient in a telemedicine system using a smart device.SOLUTION: To calculate a rehabilitation index for quantitatively evaluating the recovery state of a patient by a numerical scale on the basis of a recovery evaluation score calculated on the basis of a questionnaire using a health-related QOL evaluation scale. A rehabilitation program of a grade suitable for the patient is selected on the basis of the calculated rehabilitation index, exercise biological information of the patient who has executed an exercise program of the rehabilitation program of the selected grade is measured and collected by the wearable sensor device, and it is determined whether the measured and collected data satisfies an exercise target value set in the rehabilitation program of the grade, whereby the grade of the selected rehabilitation program is updated to a program of a grade in which a load more suitable for the patient is applied.SELECTED DRAWING: Figure 1

The invention provides a postoperative rehabilitation evaluation method and device for a patient, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining gait video data of a target patient; performing skeleton point extraction on the gait video data, and outputting coordinates of at least one target skeleton point; wherein the coordinates of the target skeleton point are coordinates under a real space coordinate system generated based on a mapping relation between a pixel coordinate system of the gait video data and the real space coordinate system; performing detection based on the coordinates of the at least one target skeleton point, and outputting multiple gait parameters under the real space coordinate system; and performing evaluation based on the multiple gait parameters under the real space coordinate system, and outputting a rehabilitation evaluation result of the target patient. According to the method provided by the invention, the accurate and objective quantification of the gait parameters under the physical scale is realized, and the accuracy, reliability and repeatability of postoperative rehabilitation evaluation are remarkably improved.