458 results about "Stroke patient" patented technology

The invention discloses a post-stroke cognitive impairment prediction method based on multi-modal feature fusion. The method comprises the steps that firstly, multi-modal information of a stroke patient is collected, wherein the multi-modal information comprises a three-dimensional brain MRI image, an EEG electroencephalogram signal and clinical medical record information; secondly, converting MRI into a tensor, and inputting the tensor into a multi-scale spatial-temporal feature extraction backbone network to obtain MRI modal features; the EEG electroencephalogram signals are subjected to electroneurographic signals and are combined with Transform, and EEG modal features are obtained; the clinical medical record information of the stroke patient is converted into semantic sentences, the semantic sentences are input into a two-channel semantic encoder for encoding extraction, and clinical medical record information features are obtained. And finally, inputting the MRI modal features, the EEG modal features and the clinical medical record information features into a three-modal fusion device to obtain fusion features, and outputting probability prediction through a classifier. The post-stroke cognitive impairment prediction method achieves accurate prediction of post-stroke cognitive impairment, and significantly improves robustness and medical interpretation.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

The invention discloses an intelligent assessment method and device for body rehabilitation of a stroke patient, and relates to the technical field of rehabilitation intelligent assessment, and the method comprises the steps: connecting a medical information management system, and obtaining the stroke medical record information of the current patient; performing identification according to the stroke medical record information; the multi-modal sensing module is connected with the rehabilitation device, performs rehabilitation motion sensing on the limb on the affected side according to the multi-modal sensor, and outputs a multi-modal sensing data set; collecting a multi-modal sensing sample set of a healthy limb corresponding to the limb on the affected side; a rehabilitation evaluation model is constructed, the rehabilitation evaluation model evaluates the multi-modal sensing data set, and rehabilitation level indexes are obtained; the rehabilitation level indexes are fed back to the rehabilitation device for training mode optimization. The technical problems that in the prior art, body rehabilitation evaluation depends on subjective judgment, the limb function recovery condition of the patient is difficult to comprehensively reflect, rehabilitation evaluation efficiency is poor, and the result is inaccurate are solved, and the technical effect of improving rehabilitation efficiency and evaluation accuracy is achieved.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

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 belongs to the field of rehabilitation medical treatment and intelligent computing, and particularly discloses a cerebral apoplexy rehabilitation personalized treatment recommendation system based on artificial intelligence. Comprising a multi-modal data acquisition module, a feature extraction and clustering modeling module, a personalized path making module and a recommended path generation and feedback optimization module, and can realize dynamic monitoring and intelligent analysis of rehabilitation data of a stroke patient; a multi-modal aggregation neural network model is constructed by fusing multi-dimensional data such as images, biomarkers, clinical behaviors and cognitive emotions, a rehabilitation path is formulated according to a medical data clustering result, and a recommended path is dynamically adjusted and optimized in combination with treatment feedback of a patient; compared with a traditional method, the method has the advantages that scientificity and adaptability of personalized treatment are improved, the method has the advantages of real-time updating of rehabilitation paths, closed-loop management of a system, high model robustness and the like, rehabilitation intervention efficiency and accuracy can be remarkably improved, and technical support is provided for intelligent development of rehabilitation medicine.

The invention discloses a deep learning-based stroke prognosis prediction method and system. The method comprises the steps of obtaining a head magnetic resonance medical image data set of a stroke patient before treatment and a corresponding mRS score of stroke prognosis; segmenting a core infarction focus image from the obtained magnetic resonance medical image by adopting a DWI sequence; establishing a six-score cSVD load scoring standard, scoring the core infarction focus image, and constructing a model data set; 3, constructing a prediction model for predicting the stroke prognosis mRS score, dividing the model data set in the step 3 into a training set, a verification set and a test set, training the prediction model by adopting the training set, verifying the prediction model on the verification set, testing by adopting the test set, screening out the prediction model of which the precision meets the requirement, and determining the stroke prognosis mRS score. And predicting the mRS score of the stroke prognosis of the to-be-evaluated patient by adopting the screened prediction model. According to the method, the prediction capability of the image features obtained by MRI (Magnetic Resonance Imaging) flat scanning of the LAA-AIS patient on prognosis is fully excavated, and a new reference direction is provided for prognosis evaluation of the LAA-AIS patient.

The invention relates to the field of cerebral apoplexy rehabilitation management, in particular to a cerebral apoplexy patient rehabilitation training management system and method based on an internet platform. The stroke patient rehabilitation training management system based on the internet platform comprises a training plan generation module, a training time dynamic adjustment module and a training intensity dynamic adjustment module. According to the method, personalized exercise priority indexes and cognitive priority indexes are calculated according to the specific exercise function recovery degree and cognitive function recovery degree of each cerebral apoplexy patient, the fields in which the patient needs more attention and training are determined, a targeted exercise function training plan and a targeted cognitive function training plan are generated according to the fields, and the training efficiency of the cerebral apoplexy patient is improved. Therefore, the allocation of rehabilitation resources is optimized; the personalized rehabilitation training plan can better meet individual differences of patients, the rehabilitation efficiency is improved, and the recovery progress is accelerated.

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

The invention discloses a cerebral apoplexy patient upper limb rehabilitation evaluation method based on electroencephalogram and electromyographic signals. The method specifically comprises the following steps that signals are collected and preprocessed; electroencephalogram and electromyographic signal decomposition; performing multi-scale processing; calculating transmission spectrum entropy; and upper limb rehabilitation grade evaluation: mapping a fitting relationship between the MSTSE significance area value of each patient and a Brunnstrom evaluation scale based on a Spearman correlation coefficient, analyzing an FCMC characteristic index and a change trend which are significantly related to the grade of the Brunnstrom evaluation scale, and performing quantitative evaluation on the disease grade of the stroke patient. According to the technical scheme, a binary variational mode decomposition-multi-scale transfer spectrum entropy method is used for representing the FCMC coupling relation between the brain motor cortex and the corresponding muscular tissue, and in combination with a Brunnstrom rating scale, objective quantitative evaluation of the rehabilitation level of the upper limbs of the stroke patient is achieved.

The invention provides an interpretable ML method and system for predicting the death risk of an ICU stroke patient in a hospital, and belongs to the technical field of medical care informatics and electric digital data processing. The ICU cerebral arterial thrombosis patient in-hospital death risk prediction method comprises the steps of S1, inputting variables, related to the in-hospital death risk of the ICU cerebral arterial thrombosis patient, of a current patient; s2, performing in-hospital death risk prediction on the current patient by adopting a pre-constructed optimal prediction model for the in-hospital death risk of the ICU cerebral arterial thrombosis patient to obtain an in-hospital death prediction probability; and S3, performing explanatory analysis on the optimal prediction model by using an SHAP method, and analyzing the importance of variables to a hospital death risk prediction result. According to the method, the hospital death risk of the ICU cerebral arterial thrombosis patient can be quickly and accurately predicted, and an explanatory analysis can be provided for the prediction result, so that a clinical decision is guided.

The invention provides a cerebral stroke patient traditional Chinese medicine rehabilitation nursing strategy recommendation method based on RNN monitoring, and the method comprises the steps: recognizing cerebral stroke image features in a cerebral stroke medical image of a cerebral stroke patient through a cerebral stroke RNN detection model trained through big data, and outputting a symptom period, an onset risk level and a corresponding traditional Chinese medicine rehabilitation nursing strategy; writing into a medical record file in the HIS system; and pushing the medical record file to the APP terminal of the stroke patient. The medical image interpretation of the stroke patient is realized, and the pre-diagnosis report of the stroke patient is generated, so that doctors and patients can know the morbidity state of the stroke patient, and the treatment and recovery conditions of post-hospital stroke can be tracked in time. Patients do not need to go to hospitals to take reports and know the stroke development state at home, and convenience is provided for the patients. And an adaptive traditional Chinese medicine rehabilitation nursing strategy can be quickly recommended to the patient, so that the patient realizes good cerebral apoplexy rehabilitation nursing based on traditional Chinese medicine rehabilitation dialectical treatment and a progressive rehabilitation nursing mode, and the treatment effect is improved.

The invention relates to the technical field of medical equipment, provides a non-intrusive closed-loop ear vagus nerve stimulator for improving cerebral apoplexy and a use method, and aims to improve the symptoms of a patient by stimulating the ear vagus nerve in a conchae area. The device integrates an electrical stimulation electrode, a host and a closed-loop feedback module, monitors brain blood flow changes and other physiological parameters of a patient in real time, and automatically adjusts stimulation parameters through an intelligent algorithm to realize personalized treatment. A microprocessor, a multi-mode monitoring unit and a data fusion analysis unit are arranged in the host, and accurate and efficient treatment is ensured. In addition, the equipment supports remote intelligent monitoring and guidance, a treatment plan can be remotely adjusted, and the treatment effect is improved. A user interaction interface is friendly, and operation of patients and medical staff is facilitated. The stimulator is light in design, the conductive silica gel electrode is adopted, through real-time monitoring and intelligent adjustment, accurate and efficient treatment is provided for a patient, the comfort level and treatment convenience of the patient are ensured, and a safe and effective rehabilitation scheme is provided for the stroke patient.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention relates to a cerebral apoplexy patient gait analysis and personalized rehabilitation correction method and system based on a big data model, and relates to the technical field of medical rehabilitation, a patient feature data set is generated by integrating multi-modal data including gaits, electroencephalogram, myoelectricity and the like, a rehabilitation critical period is determined by using real-time nerve regulation and control and brain-computer interface data, and the rehabilitation accuracy is improved. The system constructs a personalized rehabilitation scheme according to the optimized feature data set and rehabilitation training parameters, the rehabilitation effect is tracked through a rehabilitation progress evaluation module, the scheme relates to data fusion, dimension reduction, correlation analysis and dynamic adjustment of the rehabilitation scheme, the personalization and effectiveness of rehabilitation training are ensured, and the rehabilitation effect is improved. The invention aims to improve the rehabilitation efficiency and life quality of stroke patients.

The invention relates to an assessment method and system for a stroke recovery stage, and belongs to the technical field of medical assessment, the method comprises the following steps: obtaining demographic information of a stroke patient, and obtaining motion video data of the stroke patient, the motion video data comprising upper limb motion data, lower limb motion data and / or hand motion data; key points are extracted from the motion video data of each type based on a key point recognition algorithm, corresponding motion posture data in the motion video data of each type are calculated based on the positions of the key points, and the motion posture data comprise angle data and displacement data; and determining preset motion attitude data based on a Brunnstrom six-stage theory and demographic information, comparing the motion attitude data with the preset motion attitude data, and obtaining a recovery stage of the stroke patient based on a comparison result. Real-time monitoring and intelligent evaluation of the rehabilitation process of the stroke patient are realized, and the evaluation accuracy and diagnosis efficiency are improved.

The invention belongs to the technical field of cerebral apoplexy diagnosis, and particularly relates to a cerebral apoplexy patient tongue picture automatic identification system and method based on multi-task learning, and the method comprises the steps: carrying out the multi-label classification of a tongue picture, dividing the tongue picture into a training set and a test set, and carrying out the data enhancement; constructing an LGAN model by using the tongue picture features, extracting picture features, capturing correlation between the features, and generating a classification result of each label; and optimizing model parameters by calculating a loss value between a prediction score vector and a real label, and evaluating a model performance index. According to the method, the problems that in the prior art, standardization and objectification of tongue picture recognition of a stroke patient are insufficient, diagnosis is inconsistent and accuracy is poor are solved, and by combining multi-task learning and an attention mechanism, accuracy and robustness of feature extraction are improved, dependence on doctor experience is reduced, and standardization and automation of tongue picture diagnosis are achieved.

The invention provides a fall detection algorithm model for a wearable device of a stroke patient, and relates to the technical field of health monitoring and posture detection, and the technical key points are as follows: a construction method of the model is as follows: S1: obtaining posture information of a wearer by using an inertial sensor IMU in the wearable device, after data collection is completed, performing low-pass filtering processing on original data to obtain sensor data; s2, decomposing the inertial sensor data obtained at the t moment into a trend component, a season component and a residual component by using LOESS according to an STL (Standard Template Library) cyclic trend decomposition method, and then carrying out normalization processing on the data; and S3, performing time sequence modeling on each inertial sensor component by using LSTM (Long Short Term Memory). According to the fall detection algorithm model for the wearable device of the stroke patient, collaborative optimization of multi-physical-quantity coupling feature decoupling and time sequence dynamic modeling is realized, and medical-level fall monitoring performance is realized under resource constraints of the wearable device.

The invention relates to the technical field of cerebral apoplexy diagnosis and treatment, and discloses a hemorrhagic cerebral apoplexy diagnosis and treatment evaluation system based on data analysis, which comprises a multi-dimensional acquisition module and an intelligent diagnosis and treatment module. According to the hemorrhagic cerebral apoplexy diagnosis and treatment evaluation system based on data analysis, personal information, treatment data and body function data of all cerebral apoplexy patients are acquired through the multi-dimensional acquisition module and are classified to form a data set, the intelligent diagnosis and treatment module analyzes the onset risk of each cerebral apoplexy patient according to age, induced diseases and living habits, and the diagnosis and treatment efficiency of the cerebral apoplexy patients is improved. The intelligent diagnosis and treatment module analyzes the change trend of the illness state of each stroke patient and the risk of hematoma expansion, generates a corresponding fluctuation index and a hematoma score, then analyzes the prognostic physical state of each stroke patient, generates a corresponding function data set, and performs diagnosis and treatment on the prognostic physical state of each stroke patient. And judging an onset risk level and an illness state development trend of the cerebral apoplexy of the patient, accurately predicting a hematoma expansion trend, and outputting a corresponding diagnosis and treatment suggestion.

The invention relates to the technical field of health management, in particular to a risk assessment method and device for a stroke patient, equipment and a medium, and the method comprises the steps: obtaining a video image and audio data of the stroke patient, the video image comprises a face reaction image made by the stroke patient based on the first guide instruction and a limb reaction image made by the stroke patient based on the second guide instruction, and the audio data is a reply made by the stroke patient based on the third guide instruction; determining a facial paralysis recognition result and an eye response recognition result based on the facial response image; determining a limb motion recognition result based on the limb reaction image; based on the audio data, determining a language pronunciation recognition result; according to the method, the risk assessment level of the stroke patient is determined based on the facial paralysis recognition result, the eye response recognition result, the limb movement recognition result and the language composition recognition result, and the risk of the stroke patient can be accurately and quickly assessed without considering medical environment conditions.

The invention provides a remote rehabilitation system for a stroke patient, relates to the technical field of stroke rehabilitation, and aims to remotely diagnose the patient, periodically generate a personalized rehabilitation scheme for the patient, and timely adjust and update a training scheme according to the exercise effect of the patient. The system comprises a data acquisition terminal, a data transmission terminal, a control terminal, a doctor monitoring terminal and a patient terminal, a personalized rehabilitation training scheme generation algorithm is applied, multi-dimensional data is fused to form a comprehensive feature vector, and rehabilitation potential and requirements are evaluated. Personalized schemes covering exercise, cognition and psychological intervention are customized for the patient, dynamic adjustment is conducted according to feedback, and precise rehabilitation is achieved. The system has the functions of real-time monitoring, intelligent reminding and abnormity early warning, the rehabilitation effect is optimized, the medical burden is relieved, the life quality of patients is improved, and the system is suitable for medical institutions at all levels.

APRIL research shows that in a selected stroke patient, infusion of 0.2 mg / kg of ApTOLL (SEQ ID NO: 1) in combination with intravascular treatment is safe, and the death rate of 90 days is reduced. ApTOLL has proved to limit intracranial hemorrhage and reperfusion injury which may occur after recanalization, and also improve early nerve injury and long-term disability. ApTOLL molecules are provided for reducing the risk of intracranial or secondary bleeding after acute ischemic stroke, reducing nerve injury, and improving nerve recovery in a subject.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention belongs to the technical field of assessment of dysphagia of stroke patients, and provides a stroke patient dysphagia risk prediction and personalized intervention recommendation system based on R language construction, which comprises a data processing module, a prediction model module, an intervention recommendation module and an output module, the data processing module receives and processes an input predictive factor value, the predictive model module constructed based on a large amount of case data is used for calculating a dysphagia risk score, then the intervention recommendation module intelligently recommends personalized intervention measures, and the personalized intervention measures are displayed to a user in a visual form through the output module; according to the system, accurate prediction and personalized intervention recommendation of the dysphagia risk of the stroke patient are realized, the prediction accuracy and reliability are improved, meanwhile, the pertinence and effectiveness of intervention measures are enhanced, and the life quality and rehabilitation effect of the patient are improved.

The invention provides an in vitro method for determining the prognosis of a patient with stroke comprising: (i) determining, in a sample isolated from the patient, the expression level of at least one miRNA selected from the group consisting of miR-376c-3p, miR-199a and combinations thereof, (ii) comparing the level of expression obtained in step (i) with a corresponding reference value, wherein if the expression level determined in step (i) is higher than the corresponding reference value, the patient is considered to have a bad prognosis. The invention also provides an in vitro method for deciding or recommending whether to initiate a medical regimen in a patient with stroke, and an in vitro method for determining the response of a patient already diagnosed with stroke, to a medical regimen for treating stroke. The invention further provides a miRNA inhibitor of a miRNA selected from the list consisting of miR-376c- 3p, miR-199a and combinations thereof, for use in the treatment or prevention of stroke in a patient. The invention also provides a combination of miRNAs comprising said miRNA inhibitor, and a pharmaceutical composition for use in the treatment or prevention of stroke.

The invention relates to the technical field of medical health information services, in particular to a cerebral apoplexy knowledge question-answering system construction method and system based on a knowledge graph and a large language model. Natural language input of a user is analyzed through a query agent, a query intention and constraint conditions are recognized, and a structured execution plan is generated; a user state management tool is forcibly activated, a static clinical portrait and a dynamic rehabilitation log are loaded, and a personalized context is constructed; a plurality of tools such as knowledge graph query, authoritative literature retrieval and rehabilitation plan generation are scheduled, and accurate retrieval and reasoning of heterogeneous knowledge are completed; through double verification of fact consistency and clinical risks, error or high-risk suggestions are intercepted and replaced with risk early warning. The problems of'illusion 'risk, insufficient individuation, poor interpretability and the like of a traditional single model are solved to a large extent, high-credibility, individuation and traceable rehabilitation knowledge service can be provided for the stroke patient and a caregiver of the stroke patient, and rehabilitation safety and effect are guaranteed.

The utility model discloses a walking rehabilitation training device for a stroke patient, and relates to the technical field of rehabilitation training. Comprising a base, a storage cavity is formed in the base, a plurality of lead screws are rotationally arranged in the storage cavity, the lead screws are movably sleeved with lifting frames, supporting plates are hinged to the tops of the lifting frames, and replacement plates are detachably connected to the tops of the supporting plates. According to the liftable replacement plate, the multiple supporting plates can form a stair state, a patient can simulate walking stair-climbing training, the training difficulty is increased, the angle of the angle-adjustable replacement plate can be adjusted, the multiple replacement plates can simulate a slope surface so that the patient can conduct walking training, the training difficulty is greatly increased, uphill training can be conducted, and downhill training can also be conducted; by selecting the replacement plates of different table tops, walking on different road surfaces can be simulated, the training difficulty is further increased, the structure is simple and compact, economic benefits are met, and the application prospect is wide.

The invention belongs to the technical field of medical care informatics, particularly relates to a method and a system for constructing a refined model of cerebral apoplexy dysfunction based on an ICF theory, and aims to construct a refined model capable of accurately describing a dysfunction area of a cerebral apoplexy patient through dynamic optimization and a self-adaptive adjustment mechanism. In order to improve the accuracy and efficiency of dysfunction assessment, multi-modal assessment data, including pathological information, imaging data and biochemical indexes, of a patient is continuously collected and analyzed through a dynamic scoring mechanism, then, in combination with an ICF theory, feature parameters of a barrier region are automatically extracted and optimized through feature matching and a deep learning algorithm, and the accuracy and efficiency of dysfunction assessment are improved. According to the method, a three-dimensional model capable of accurately evaluating and positioning an obstacle area is constructed, and the model not only can update and adaptively adjust parameters in real time, but also can provide personalized rehabilitation suggestions and treatment schemes according to individual differences of different patients.

The invention discloses a PID (Proportion Integration Differentiation) electrical stimulation balance rehabilitation method based on a damage synergistic effect. According to the method, firstly, myoelectricity and IMU joint movement data of a healthy subject on a disturbance platform are collected, non-negative matrix factorization (NMF) is conducted on the myoelectricity data, and the muscle synergistic effect of the healthy subject under a disturbance normal form is solved; myoelectricity and IMU signals of stroke patients in the same disturbance normal form are collected, nonnegative matrix factorization is carried out on the myoelectricity of the patients, and similarity analysis is carried out on the health synergistic effect and the patient synergistic effect to determine the injury synergistic effect. The injury synergistic effect is applied to adjustment of electrical stimulation pulse width parameters and used for conducting electrical stimulation on lower limb muscles, joint angle data are collected in real time, PID electrical stimulation control based on the injury synergistic effect is achieved, and therefore a patient can track a joint angle track of a healthy subject under a disturbance normal form. The patient is assisted to recover the injury synergistic effect under the disturbance normal form, the balance capacity is improved, and a new path is developed for rehabilitation treatment of the stroke patient.