506 results about "Cerebral stroke" patented technology

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

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 high risk group positioning evaluation system based on multi-modal data, and relates to the technical field of medical information science, the cerebral apoplexy high risk group positioning evaluation system comprises a cerebral apoplexy prevention and control management platform, and the cerebral apoplexy prevention and control management platform is in communication connection with the following modules: a multi-modal data acquisition and integration module, the multi-modal data collection module is used for collecting multi-modal data related to cerebral apoplexy from multiple channels and carrying out preprocessing operation on the collected multi-modal data. By integrating clinical data, image data, omics data and terminal health data, multi-dimensional information related to the cerebral apoplexy can be comprehensively captured, particularly, cerebral vessel digital twin is utilized to simulate hemodynamic characteristics, a plurality of data sources are fused in combination with a graph neural network, high-risk groups can be recognized more accurately, and the accuracy of cerebral apoplexy recognition is improved. The accuracy and reliability of risk prediction are remarkably improved, the problem of missing detection caused by dependence on a single data source in a traditional method is solved, and more powerful support is provided for early intervention.

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 discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

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 discloses a cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The invention relates to a self-supervised cerebral apoplexy focus segmentation method based on sparse stripe sampling and a lightweight encoder. The invention relates to the technical field of cerebral apoplexy image segmentation, and the method comprises the steps: carrying out the preprocessing of input MRI image data, and carrying out the sparse fringe patch sampling of the preprocessed data; establishing a lightweight encoder, and carrying out self-supervision pre-training; based on the trained lightweight encoder, performance optimization and task adaptation are realized through a lightweight fine tuning mode, and optimization of the encoder is completed; and according to the optimized lightweight encoder, encoding and decoding the area after sparse fringe patch sampling, and outputting a segmentation result. According to the method, local convolution and global attention modeling are comprehensively considered, and the structural recognition capability of the focus with the complex form and the fuzzy boundary is effectively improved.

The invention discloses a cloud edge collaborative early warning method for early recognition of cerebral apoplexy, and the method comprises the steps: collecting the posture data and voice data of an edge end, and carrying out the preprocessing of the data; on the edge end, lightweight model reasoning is performed on the attitude data and the voice data based on a sub-modal processing and feature fusion strategy, and finally the cerebral apoplexy risk probability is output; and cooperative and multi-terminal linkage early warning is carried out based on the cloud and the edge terminal. According to the method, active capture of the cerebral apoplexy risk is realized through a framework of edge-end multi-modal real-time processing, cloud collaborative optimization and multi-end linkage early warning, the mode is converted from passive help calling to active early warning-rapid linkage, and the early warning precision and the response speed are improved.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

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 invention provides a multi-modal cerebral arterial thrombosis medical image segmentation method, device and equipment, and the method comprises the steps: extracting the independent features of different modal medical images through combining a ViT encoder branch and a CNN encoder branch which are finely adjusted by a hybrid expert as a multi-modal image double-branch coding network; further integrating complementary information of different modes by using a mode missing adaptive fusion network, and performing global-local information interaction between CNN features and ViT features by using an encoder branch interaction network, so that specific features and cross-mode invariant features of different available modes can be decoupled under the condition of mode missing; and meanwhile, the advantages of different types of features are fully utilized, and the value information of the multi-modal image features is deeply mined, so that accurate multi-modal cerebral arterial thrombosis medical image segmentation and imaging are realized, and the method is high in reliability, good in accuracy and good in practicability.

The embodiment of the invention discloses a cerebral apoplexy upper limb rehabilitation training system and method based on dynamic reward feedback. A data acquisition module is used for acquiring physiological signals, motion signals, emotional state data and training performance data; the motion intention decoding module is used for performing motion intention feature extraction and reliability evaluation on the physiological signals according to an improved MREE-Net + + fusion algorithm, performing weighted fusion after feature weights are adjusted according to a reliability result, obtaining a unified motion intention feature vector, performing motion intention decoding, and obtaining a motion intention intensity index and a motion intention vector; the dynamic reward decision module is used for processing the facial micro-expression data according to an improved VGG-Face model to obtain an emotion titer, and obtaining an emotion awakening degree according to the voice signal; a reward action is generated according to an improved deep reinforcement learning algorithm; and the adaptive training regulation and control module is used for generating a personalized virtual training scene according to the generative adversarial network and regulating and controlling the training intensity according to the fatigue index. The rehabilitation training effect can be improved.

The invention provides a cerebral apoplexy medical treatment seeking recommendation method and system based on intelligent screening and remote collaboration, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the body index information, medical history information and diagnosis and treatment agreement confirmation information of a to-be-diagnosed user, sending the first information to a preset screening model for feature extraction, and obtaining the first information of the to-be-diagnosed user; constructing disease risk index data containing multiple types of variables; then, inputting the index data into an intelligent cerebral apoplexy risk prediction model, performing risk assessment through a random forest, XGBoost and DNN neural networks, and fusing prediction results to obtain a cerebral apoplexy risk level of the user; and on the basis of the prediction result, generating an electronic referral sheet by combining the geographic position of the user and the medical resource distribution condition through an intelligent referral model, and recommending an optimal receiving hospital, thereby realizing personalized medical seeking recommendation. According to the method, the screening accuracy of high-risk people suffering from cerebral apoplexy and the collaborative efficiency of remote medical treatment are remarkably improved.

The invention relates to the technical field of medical rehabilitation, in particular to a post-stroke upper limb function recovery training system combined with a vr technology, which comprises a fixed base, and a motor is fixedly connected to the center of an inner cavity of the fixed base. The upper limb rehabilitation training device has the advantages that the angle adjusting and rotating functions are achieved, a personalized training plan can be made according to the specific conditions of a patient, and therefore upper limb rehabilitation training of the patient is more effectively promoted; through cooperative use of a fixed base, a motor, a fixed column, a supporting rod, a fixed sleeve, a rotating rod, an adjusting rod, an electric push rod, a lifting block, a seat and a sliding rod, the rotating and angle adjusting functions of the seat can be achieved, it is ensured that a patient can obtain the best comfort degree and experience feeling in the training process, the angle is flexibly adjusted through the seat, and the training efficiency is improved. A patient can experience a more real training scene in an immersive manner, and the brain is stimulated to generate strong neural feedback, so that the movement function recovery of the upper limbs is effectively promoted.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention relates to a stroke gait phase recognition method and system based on a multi-stage model, and the method specifically comprises the following steps: employing a self-adaptive sliding window algorithm based on index-standard deviation fusion, carrying out the dynamic fragment interception of a gait signal of a stroke patient, and obtaining the original stroke gait data; preprocessing the original stroke gait data to obtain original gait features; constructing an integrated feature enhancement unit (IFEU), inputting original gait features, and extracting time context features of the stroke gait signals; constructing a hierarchical gait feature aggregation unit HGFAU, inputting time context features, performing gait feature extraction of each level, and obtaining multiple fusion features after fusion; and inputting the multiple fusion features into a linear layer classifier, and classifying the input features to obtain a stroke gait phase recognition result. According to the invention, gait phase recognition can be realized, and objective walking state evaluation and abnormity monitoring can be carried out on rehabilitation treatment assistance of a patient.

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 relates to an automatic detection method for a stroke focus based on computer vision, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring brain CT images, and constructing a training data set; self-adaptive adjustment and standardization of the window width and the window level of the brain CT image are carried out through a self-adaptive window width and window level adjustment strategy; processing the standardized brain CT image through a multi-scale non-uniform region-of-interest sampling strategy; constructing a stroke focus automatic detection model; carrying out double-flow convolution feature extraction and fusion by adopting a double-flow encoder network; processing the fusion feature map through a density perception space attention module; calculating a focus boundary perception loss function, adopting a gradient weighting strategy to carry out gradient adaptive optimization, and updating model parameters through an optimizer to obtain a trained model; and inputting a region-of-interest image block obtained by processing a newly collected brain CT image into the trained model to obtain a final detection result. The accuracy of stroke focus detection can be improved.

Provided is an application of a circular RNA in the preparation of a cerebral stroke diagnosis product, belonging to the field of circular RNA medical treatments. Provided is a kit, comprising a reagent and / or test paper and / or gene chip for detecting a circular RNA-circ-Magi1. The nucleotide sequence of the circular RNA-circ-Magi1 is as shown in SEQ ID NO: 1 or SEQ ID NO: 4. Further provided is the application of a detection system for the diagnosis, assisted diagnosis or prognosis evaluation of cerebral stroke, and a detection reagent of the circular RNA-circ-Magi1 in the preparation of a kit, test paper, chip or high-throughput sequencing platform for the diagnosis, assisted diagnosis or prognosis evaluation of cerebral stroke.

The invention discloses a nasal delivery multistage targeting bionic nano drug delivery system as well as a preparation method and application of the nasal delivery multistage targeting bionic nano drug delivery system. According to the drug delivery system, a black phosphorus nanosheet with an active oxygen scavenging effect is used as an inner core carrier, a neuroprotective agent fingolimod hydrochloride FTY720 and polydimethyldiguanide PolyMet are loaded, and a microcolloid cell membrane and a mitochondrial membrane are modified on the surface of the preparation; the nano drug delivery system is applied to treatment of cerebral arterial thrombosis reperfusion injury, can overcome the blood brain barrier, can quickly enter the brain through nasal administration, realizes efficient delivery of drugs, remarkably reduces the cerebral infarction area, reduces the brain inflammation level, protects nerve cells, greatly relieves cerebral arterial thrombosis reperfusion injury, and has application prospects.

The invention relates to a bionic nano CO generator as well as preparation and application thereof, belongs to the technical field of medicines, and solves the problem that a CO delivery system which can synergistically promote neuroprotection and neurogenesis and has brain targeting and controlled release capabilities is urgently required to be developed at present. According to the technical scheme, the bionic nano CO generator comprises a drug-loaded particle inner core and a covering film, the drug-loaded particle inner core comprises hollow mesoporous CeO2 nano particles and MnCO loaded on the hollow mesoporous CeO2 nano particles, and the covering film is a macrophage membrane and wraps the drug-loaded particle inner core. The compound is used for apoplexy treatment administration by integrating neuroprotection and enhancing innovative dual channels of endogenous nerve growth, and is expected to significantly reduce brain injury after cerebral apoplexy and restore neurological functions so as to relieve the worry about high death rate and long-term disability after cerebral ischemia-reperfusion injury.

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 discloses a cerebral apoplexy intelligent rehabilitation monitoring method and system based on a sensor, and the method comprises the steps: carrying out the comprehensive quality evaluation of health monitoring data of each monitoring item based on a pre-trained data quality evaluation model, outputting the quality score of each data point, and combining the pathology correlation intensity with the quality score of the data point, a weighted fusion strategy is adopted, the data credibility of the health monitoring data of each monitoring item is calculated, and when the data credibility of a certain monitoring item is lower than a preset threshold value, the health monitoring data of the monitoring item is reconstructed through a multi-modal data fusion and generative adversarial network (GAN) technology according to the pathology correlation intensity and in combination with data of other credible monitoring items, so that the health monitoring data of the monitoring item is obtained. And based on all credible or reconstructed monitoring data, calculating a comprehensive health index of the target patient in rehabilitation training by adopting a multi-objective optimization algorithm. Quality evaluation, abnormal correlation analysis and health index generation are carried out on the monitoring data, and the precision of rehabilitation evaluation is improved.

The invention provides a method and system for evaluating the instant curative effect of acupuncture head acupoints on cerebral stroke cognitive impairment based on electroencephalogram signals, and the method comprises the steps: collecting the electroencephalogram signals before, during and after acupuncture in stages, and synchronously combining a MoCA / MMSE scale and a cognitive task test to obtain subjective curative effect data; an insulating acupuncture needle is adopted to reduce interference on electroencephalogram signals, and 4-8-conduction portable dry electrode electroencephalogram equipment is matched to collect signals in an area avoiding acupuncture points of the head, so that acupuncture operation and electroencephalogram collection are ensured not to influence each other. A condition vector is generated by using acupuncture point parameters and acupuncture manipulation, and a space-time Transform-GNN model is driven to dynamically focus alpha / theta / beta frequency band time sequence characteristics and a brain network connection mode of cognitive related brain regions such as a prefrontal lobe, a parietal lobe and a temporal lobe. According to the method, a multi-modal feature vector containing electroencephalogram frequency domain features, time domain components and clinical scale scores is constructed through an electroencephalogram-acupoint-curative effect triple data training model of at least 100 patients, and an obvious / effective / invalid level is output.

The utility model discloses a novel neurology department clinical post-stroke limb rehabilitation training auxiliary device in the technical field of stroke limb rehabilitation training, which comprises a fixing frame, universal wheels are respectively arranged on the periphery of the bottom of the fixing frame, two supporting frames are arranged on the top of the fixing frame, sliding blocks are sleeved on the outer sides of the supporting frames, and the sliding blocks are connected with the fixing frame. A fixing rod is installed between the two sliding blocks, the two fixing rods are convenient for a patient to hold, the patient is prevented from falling down and being secondarily hurt during walking rehabilitation training, then the sliding blocks are locked and fixed by using locking fasteners, so that the fixing rod is suitable for people with different heights to use, and the practicability is high. The problems that the human body is prone to inclining or falling down, the human body cannot be supported in an auxiliary mode, the height of an auxiliary device is single, the auxiliary height is inconvenient to adjust to adapt to people of different heights, and the stability and practicability of limb rehabilitation training are affected are solved.

The invention discloses application of an NR1D1 protein phosphorylation site in preparation of cerebral hemorrhage treatment drugs, and belongs to the technical field of molecular biology. According to the invention, the specific phosphorylation site and phosphorylation modification level of the NR1D1 protein are determined for the first time, and the NR1D1 protein can be used as a key marker for evaluating the secondary nerve injury degree of the hemorrhagic cerebral apoplexy and developing treatment and / or alleviation of the hemorrhagic cerebral apoplexy; a specific antigen peptide and an antibody for accurately detecting the NR1D1 protein phosphorylation site are further constructed, and an efficient tool is provided for molecular evaluation of hemorrhagic stroke secondary nerve injury; meanwhile, polypeptide molecules capable of inhibiting NR1D1 protein phosphorylation are researched and developed, through the dual effects of specifically blocking the phosphorylation process and reducing protein degradation, necrosis of brain tissues around hematoma after hemorrhagic cerebral apoplexy is relieved, secondary dyskinesia is improved, and the effect of inhibiting NR1D1 protein phosphorylation is achieved. And a brand new scheme is provided for molecular evaluation and targeted intervention of hemorrhagic stroke secondary nerve injury.

The invention relates to a stroke rehabilitation training action quality evaluation method and system based on multi-modal fusion, and belongs to the technical field of intelligent rehabilitation and artificial intelligence. The system comprises a multi-modal data acquisition module, a video semantic and feature extraction module, a semantic-guided multi-modal fusion module, a special quantization module, a rehabilitation stage evaluation module and a multi-dimensional dynamic weighting evaluator. The method comprises the following steps: synchronously acquiring a training video, a surface electromyogram signal and inertial measurement data; extracting bottom visual features and high-level semantic features of the video; multi-modal feature fusion of semantic guidance is realized through a gating cross-modal attention mechanism; generating a special quantized value of the abnormal motion mode by using a conditional diffusion model under the condition of the fusion features; a rehabilitation stage is automatically evaluated based on historical data; and combining stage labels and real-time state dynamic weighting, and outputting a multi-dimensional comprehensive score. According to the invention, comprehensive, self-adaptive and intelligent evaluation of rehabilitation actions is realized, and evaluation objectivity and rehabilitation individuation level are improved.