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

The invention discloses 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 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 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 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.

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 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 discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.

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 discloses a Doppler ultrasonic diagnosis system for dysphagia detection, and belongs to the technical field of Doppler ultrasonic diagnosis. The method comprises the following steps: respectively measuring the geniolingual muscle movement, swallowing time, tongue movement and the distance between the tongue bone and the thyroid cartilage of a stroke patient in a B-type ultrasonic mode and an M-type ultrasonic mode, recording test data after multiple swallowing tests, calculating to obtain an average value of the geniolingual muscle movement, the swallowing time, the tongue movement and the distance between the tongue bone and the thyroid cartilage, and calculating the average value of the geniolingual muscle movement, the swallowing time, the tongue movement and the distance between the tongue bone and the thyroid cartilage. Then the measurement results of the swallowing time and the distance between the tongue bone and the thyroid cartilage are used as evaluation and diagnosis bases for diagnosis, so that objective and accurate diagnosis bases are provided for the treatment and rehabilitation process of the swallowing dysfunction of a stroke patient, and nursing personnel can evaluate and diagnose the swallowing dysfunction after the stroke by using a simple B-ultrasonic instrument at the bedside; and the diagnosis accuracy of medical personnel is improved.

The invention relates to the technical field of image analysis, in particular to a training set expansion method for cerebral apoplexy patient recurrence risk assessment, and the method comprises the steps: obtaining an initial training set composed of historical images under different recurrence risk levels; clustering is carried out based on similar conditions among different historical images, and a recurrence risk level corresponding to each target cluster is determined; dividing the historical images in each target cluster; determining a target core degree corresponding to each core possible image in each target cluster; determining a feature satisfaction degree corresponding to each core possible image in each target cluster; and screening out a target core image from each target cluster according to the target core degree and the feature satisfaction degree corresponding to all the core possible images in each target cluster, and expanding the initial training set based on all the target core images. According to the method, the historical images are analyzed, so that the training set extension is realized, and the rationality of the training set extension is 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 paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

The invention discloses a stroke-oriented motion cognition composite rehabilitation system, which adopts a cloud distributed architecture and comprises a database unit, a patient terminal and a doctor terminal, the database unit is constructed based on a cloud server, adopts modular design and comprises a doctor information module, a patient information module, an evaluation standard module and a task information module; the evaluation standard module comprises a personalized evaluation parameter library and a standardized reference database; the patient terminal is used for receiving the task data transmitted by the database unit, displaying a specific task to a patient, and collecting task parameters of the patient while the patient executes training and evaluation tasks; the patient terminal comprises an account management module, a task execution module and a data acquisition module, and the task execution module integrates a trajectory tracking task, a visual arrival task, a motion interception task, a labyrinth navigation task and a target memory task; the doctor terminal is used for a doctor to remotely manage a patient, formulate a personalized rehabilitation scheme and track the rehabilitation progress in real time.

The embodiment of the invention provides an automatic follow-up visit system for a stroke patient, and relates to the technical field of medical follow-up visit systems.The automatic follow-up visit system comprises a data receiving module, a large language model module, a data output module and a scoring module, the question and answer data are answers of the stroke patient according to greetings or question data; the big language model module is used for analyzing the answer data by utilizing a Baichuan-M2 medical big language model based on the cerebral apoplexy health risk assessment and an improved Rankin scale to generate question data; the data output module is used for outputting problem data to the stroke patient; the scoring module is used for generating a stroke score of the stroke patient based on the question data and the answer data, and the stroke score is used for evaluating the rehabilitation condition of the stroke patient. The human cost can be greatly reduced, and the recording and follow-up visit efficiency is improved.

The technical scheme of the invention discloses a dynamic optimization system for the household rehabilitation training content of a stroke patient based on GRPO. The exercise performance and physiological feedback of the patient are collected in real time, the system can adjust the training content in a closed-loop mode, the scientificity, safety and efficiency of rehabilitation training are remarkably improved, and the purposes of progressive training difficulty and individualization are both achieved. The system disclosed by the invention not only breaks through the limitations of fixed templates, manual adjustment, feedback lag and the like of traditional rehabilitation training, but also has good expansibility, can be integrated with a rehabilitation robot, intelligent wearable equipment and a mobile health management platform, and promotes collaborative development of rehabilitation medicine, intelligent medical treatment and health industries. Therefore, a new personalized rehabilitation mode based on frontier artificial intelligence decision is provided for patients with cerebral apoplexy and related diseases, theory and engineering systems in the field of intelligent rehabilitation are greatly enriched, and the method has important clinical application value and wide industrial popularization prospects.

The application discloses a kind of upper limb movement evaluation method and upper limb movement evaluation system of stroke patient, former includes: the video when the two sides of stroke patient upper limb successively complete predetermined movement process is collected;The target that the two sides of upper limb complete predetermined movement process is, the movement track formed along the vertical midline of stroke patient is axisymmetric;Extract skeletal key point data in video;Skeletal key point data characterizes the position information of each skeletal key point of stroke patient upper limb at corresponding moment;Based on the feature difference of two sides of skeletal key point data in predetermined movement process, determine the NIHSS score of stroke patient, obtain upper limb movement evaluation result.The present application utilizes computer vision technology, can realize to upper limb movement objective, automatic detection, improve evaluation objectivity and standardization degree, can real-time detection and analysis, improve evaluation efficiency, can provide timely data support for doctor, help to formulate individualized rehabilitation plan and adjust treatment scheme.

The invention provides a lower limb rehabilitation evaluation method, system and device based on a brain-computer interface and a medium, and relates to the technical field of rehabilitation medicine and medical electronics. According to a lower limb rehabilitation evaluation technology based on a brain-computer interface, a two-way evaluation system of peripheral muscle form-central nervous activity is constructed through synchronous acquisition of wearable A-type ultrasound and electroencephalogram signals; a-type ultrasound monitors the change of the thickness of the rectus femoris in real time, and electroencephalogram signals analyze the coherence of Alpha / Beta frequency bands, so that functional difference evaluation of a stroke patient in a resting state, a passive motion state and an active motion state with different resistances is realized, the limitation of single subjective scoring of a traditional scale is avoided, and a multi-dimensional quantitative index is provided for rehabilitation evaluation.

The utility model provides an auxiliary walking protector for a stroke patient, which comprises a knee sheath, an electrical stimulation module arranged on the knee sheath, an ankle support, a first support rod for connecting the knee sheath and the ankle support, and a clamping component arranged on the first support rod, the clamping assembly comprises a supporting block fixedly connected to the first supporting rod, two clamping rods oppositely arranged at the two ends of the supporting block, an arc-shaped plate, an elastic piece connecting the clamping rods and the arc-shaped plate and a driving structure driving the two clamping rods to move relatively, the arc-shaped plate is arranged close to one face of the knee sheath, and the driving structure is electrically connected with the electrical stimulation module. And the driving structure drives the two clamping rods to be relatively close to each other, so that the arc-shaped plate is propped against the knee sheath. The electrical stimulation module is closer to the body of a patient, and the arc-shaped plate can clamp the knee sheath during each movement, so that the problem that the knee sheath falls off when the knee rotates can be effectively avoided.

The invention discloses a apoplexy patient monitoring system based on cell therapy and negative oxygen ion conditioning, and particularly relates to the technical field of medical monitoring. According to the method, the multi-dimensional physiological parameters of the patient are converted into the quantized physiological expression coefficients, the scheme adjustment signaling is accurately triggered, and the optimal treatment scheme is matched from the mapping library based on the monitoring data and the individual information; wherein the mapping library comprises a mapping relationship between different physiological parameter combinations and corresponding cell therapy schemes and negative oxygen ion conditioning scheme combinations, and then evaluating scheme effects through subsequent monitoring data to determine whether to optimize the schemes, so that monitoring and treatment are deeply coordinated, and the treatment effect is improved. The problems that most of apoplexy patient monitoring systems in the prior art can only achieve a single physiological parameter monitoring function and are not effectively combined with treatment means, and a treatment scheme cannot be made according to the real-time physiological state of a patient are solved.

Extracellular vesicles and / or exosomes (EVs) remain sparsely studied in hemorrhagic transformations, including stroke and intracranial hemorrhages and could provide key insights into stroke pathophysiology. We assessed plasma levels of neuron-derived EVs (NDEs), astrocyte-derived EVs (ADEs), and oligodendrocyte-derived EVs (ODEs) in 58 patients 5, 15, and 30 days post-ischemic stroke along with 46 matched controls using sandwich immunoassays. Hemorrhagic transformation was a better predictor of ADE levels than lesion volume in linear regression models. These findings suggest that ADE levels are preferentially increased over the first month post-stroke compared to EVs from other neural cell types. ADEs may be of additional interest as biomarkers of blood-brain barrier breakdown to predict hemorrhagic transformation at earlier time points after stroke.

This invention discloses an essential oil-based intervention system for the simultaneous treatment of heart and brain function based on dual feedback of eye movement and heart rate variability, belonging to the field of rehabilitation medicine technology. The system includes: a data synchronization acquisition module, which acquires heart rate variability data and eye movement characteristic data and generates a synchronized physiological data stream; a dual feedback joint assessment module, which assesses the patient's real-time physical and mental state level based on the synchronized physiological data stream; a synchronized intervention decision module, which generates essential oil atomization control instructions and eye movement training control instructions in parallel according to the physical and mental state level; and a synchronized intervention execution module, which drives the essential oil atomization unit and the eye movement training unit to execute corresponding intervention actions synchronously at the hardware level on the time axis. This invention achieves integrated and precise intervention of heart and brain function through dual-modal physiological feedback closed loop and synchronized control, effectively solving the problems of brain separation, delayed intervention, and poor coordination in traditional rehabilitation centers, and improving the synchronous rehabilitation effect of cognitive function and autonomic nervous function in stroke patients.

The utility model relates to the technical field of multifunctional rehabilitation training operation books, in particular to a multifunctional module stroke patient fine motion rehabilitation training operation book which comprises a book shell, a stringing page, a bead clamping page, a wood inserting page, a rotating page, a pulling force page, a book spine, a seam hole, a seam line and an auxiliary assembly. Bead clamping pages, wood inserting pages, rotating pages and pulling pages are arranged in the book shell, an auxiliary assembly is arranged on the side wall of the book shell, a spine is arranged on one side of the book shell, multiple sets of seam holes are formed in the side wall of the spine, and sewing threads are arranged in the seam holes. In the using process of the multifunctional rehabilitation training workbook, a patient can perform personalized rehabilitation training by changing different pages, the design not only improves the pertinence and effectiveness of training, but also integrates interesting elements, and enhances the training enthusiasm and compliance of the patient.

The invention discloses a cerebral apoplexy thrombolysis patient evaluation method, device and equipment and a storage medium, and the evaluation method comprises the steps: obtaining patient information, obtaining the weight data of a patient from a record database, reading the type of a drug, obtaining a dose coefficient and a conversion factor from a drug database, dose data of the thrombolytic drug is calculated through a first formula and is sent to the display module to be displayed, rapid conversion of the administration dose is achieved, calculation errors of the thrombolytic drug dose are avoided, then blood pressure data, heart rate data and blood oxygen saturation data of a patient are obtained in real time, and the accuracy of thrombolytic drug dose calculation is improved. And judging whether the vital signs are correspondingly located in the first range, the second range and the third range or not, and if not, sending out an alarm signal, so as to accurately judge the vital sign data of the thrombolysis patient, timely discover abnormity and send out an early warning.

The invention discloses an intelligent fabric shoulder compensation monitoring system for rehabilitation treatment of a stroke patient. The intelligent fabric shoulder compensation monitoring system comprises a sensor, a fixing carrier part used for fixing the sensor, a signal acquisition module in signal connection with the sensor and a PC in signal connection with the signal acquisition module. The plurality of sensors are fixedly connected to the fixed carrier part, and the sensors are fabric stretching sensors; the fixing carrier piece is made of fabric, and the fixing carrier piece detachably wraps the single-side shoulder and the shoulder blade of the patient. According to the invention, the system can timely discover the compensatory exercise of the patient and remind the patient, so that the fitness compensatory is reduced and the exercise rehabilitation training quality is improved.

The utility model relates to a manual bean grinding device suitable for a stroke patient. The manual bean grinding device comprises a base, a cylinder arranged on the base, a grinding disc arranged in the cylinder and a handle arranged at the top of the cylinder. A rotating shaft is vertically arranged in the barrel, the grinding disc comprises an inner disc and an outer disc which are matched with each other, a grinding disc gap is formed between the inner disc and the outer disc, the outer disc is fixedly connected into the barrel, and the inner disc is arranged on the rotating shaft; a feed port is formed in the top of the barrel, a discharge port is formed in the bottom of the barrel, and the feed port and the discharge port are communicated through a millstone gap; the handle is of a symmetrically-arranged four-connecting-rod structure, one end of the four-connecting-rod structure is detachably connected with the rotating shaft, the end opposite to the end is provided with a handheld part, the other two ends are provided with locking devices, and the rotating shaft is driven to rotate relative to the barrel by rotating the handle. The bean grinding device enables rehabilitation actions to become interesting-oriented operation activities, and the rehabilitation actions are closer to life.