151 results about "Stroke patient" patented technology

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 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 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 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 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 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 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 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.

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 belongs to the technical field of rehabilitation equipment, and discloses a stroke patient home walking exercise device which comprises a supporting frame. The supporting frame is slidably connected with a mounting frame, and the mounting frame slides in the horizontal axis direction. A rotating seat is rotationally connected to the side wall of the mounting frame, the rotating axis of the rotating seat is horizontally placed, and the rotating axis of the rotating seat is perpendicular to the sliding equation of the mounting frame; a seat plate for supporting a user is fixed at the upper end of the mounting frame; a massage mechanism is arranged on one side, close to legs of a user, of the seat plate; a transmission unit is arranged on the mounting frame and is connected with the rotating seat and the massage mechanism; the power input end of the transmission unit is connected with the mounting frame, when a user is converted from a sitting posture to a standing posture, legs push the seat plate, the rotating seat and the mounting frame to move, and horizontal moving power of the mounting frame serves as input power of the power input end; the problems that in the prior art, due to the fact that a seat plate is fixed, the conversion process between the sitting posture and the standing posture of a patient is blocked, and potential safety hazards exist are effectively solved.

1. Name of the product in this design: Intelligent Rehabilitation Therapy Mirror. 2. Purpose of this design: For postoperative rehabilitation training of stroke patients without equipment. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application relates to a stroke patient upper limb function reconstruction system, which at least comprises: a signal acquisition unit for acquiring physiological electrical signals of a stroke patient; a signal processing unit for processing the physiological electrical signals; and an upper limb training unit for assisting upper limb movement based on the control of the signal processing unit. The signal acquisition unit can at least acquire electroencephalogram signals of the stroke patient and first electromyogram signals generated by the upper limb under the assistance of the upper limb training unit, wherein the signal processing unit extracts the attention level of the patient according to the electroencephalogram signals, and the upper limb training unit assists the upper limb to move when the attention level reaches a preset value; the signal processing unit can also derive second electromyogram signals according to the electroencephalogram signals, and the signal processing unit continuously corrects the mapping relationship between the electroencephalogram signals and the motor imagery task based on the difference between the first electromyogram signals and the second electromyogram signals, so as to gradually improve the accuracy of the mapping relationship between the electroencephalogram signals and the motor imagery task.

The utility model provides an autonomous training device for a stroke patient. The autonomous training device comprises a base, a supporting column, a top plate, a back plate and wrapping cloth. The back plate is arranged in the vertical direction and fixedly connected to the base through a top plate and a supporting column, the wrapping cloth can be connected to the front side face of the back plate in the vertical direction in an up-down sliding mode, and the height of the supporting column is matched with the height from the feet to the chest of the human body. The size of the wrapping cloth is matched with the size of the chest of a human body, the wrapping cloth comprises left wrapping cloth and right wrapping cloth, and the left wrapping cloth is detachably connected with the right wrapping cloth. By means of the positioning design of the wrapping cloth, a stroke patient can be connected to the back plate in a positioning mode through the wrapping cloth, the supporting and protecting effects on the patient are achieved, a walking trainer or a running machine is used in a matched mode, and therefore the stroke patient can conveniently conduct autonomous rehabilitation walking training, the training autonomy and safety are improved, and the stroke rehabilitation training effect is improved. And meanwhile, the accompanying training burden of the protection personnel is also reduced.

The application relates to a method and device for predicting the risk of depression of a post-stroke patient, a medium and an electronic device. The method comprises: acquiring a brain image of the post-stroke patient and a standard graph of a depression functional network, the standard graph of the depression functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard graph of the depression functional network; acquiring a standard graph of depression structural disconnection, the standard graph of the depression structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard graph of the depression structural disconnection; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model to determine the risk of depression of the post-stroke patient after several months of onset through the prediction model. The application can predict the risk of depression according to the direct impact of a stroke lesion on a brain network responsible for emotion regulation in the brain.

The invention relates to a cerebral apoplexy gait phase recognition method and system under class imbalance, and belongs to the technical field of cerebral apoplexy gait analysis. The method comprises the following steps: acquiring original gait signal data of a stroke patient; inputting the original gait signal data into a feature enhancement unit, and performing global average pooling, time sequence feature enhancement branch and channel feature enhancement branch processing to obtain a first fusion feature; inputting the first fusion feature into a long-short sequence feature extraction unit to obtain a second fusion feature; inputting the second fusion feature into a feature calibration unit to obtain a calibration feature; the calibration features pass through a linear layer classifier to obtain a stroke gait phase recognition result; in the training process, a self-adjusting cost-sensitive loss function is adopted to carry out parameter optimization. The accuracy of stroke gait phase recognition can be improved.

The invention discloses a personalized emotion compensation method for lower limb walking intention perception of a stroke patient and a related device, and relates to the technical field of medical rehabilitation, the method comprises the following steps: synchronously collecting an electroencephalogram before a lower limb walking rehabilitation task of the stroke patient and a lower limb surface electromyogram signal in the task; preprocessing the electroencephalogram physiological signals and calculating dynamic brain function connection characteristics; preprocessing the electromyographic signals; inputting the brain function connection features into a TCN-LSTM model to obtain an initial emotion vector, and determining a comprehensive emotion vector in combination with an individual character vector converted by a psychological scale; time and amplitude scaling factors of the electromyographic signals are determined according to priori knowledge and a predefined optimization algorithm; inputting the preprocessed electromyographic signals into a convolutional network model to extract deep spatio-temporal features, performing scaling factor compensation to obtain compensated electromyographic features, inputting the compensated electromyographic features into a classifier to identify lower limb walking intention category output, and controlling rehabilitation equipment or feedback; the effect and efficiency of lower limb walking rehabilitation training of the stroke patient are effectively improved.

The invention aims to provide a cerebral apoplexy swallowing assessment and extubation decision-making system based on ultrasound and video stream fusion, and the system comprises an ultrasound image collection module which focuses on the dynamic movement of tongue muscles, throat muscles, tongue bones and throat lacuna; the video stream acquisition module is used for calculating facial expression, oral activity and throat form change characteristics by using a motion tracking algorithm; the analysis module is used for analyzing spatial characteristics of muscle movement; identifying time sequence characteristics of the swallowing action; the evaluation and decision module is used for generating a swallowing function evaluation report; and the user interface and report output module is used for outputting an automatically generated swallowing function evaluation report to a clinician. The swallowing function can be evaluated in real time, noninvasively and accurately through a multi-modal data fusion technology in combination with a deep learning algorithm, a more reliable swallowing function evaluation basis is provided for clinicians, and rehabilitation treatment and intervention of stroke patients are helped.

The utility model discloses a stroke dysphagia aided training instrument, including sheet mask and neckband, the mask is provided with eye open pore, nose open pore, ear open pore and lower jaw open pore, the bottom of the mask is connected with the top of the neckband, the two sides of the mask are provided with mask connecting piece, the mask connecting piece is connected with the mask connecting piece, and the mask connecting piece is connected with the neck band. The mask is fixed through the mask connecting piece after surrounding the head by a circle from the two sides of the face towards the back of the brain, neck band connecting pieces are arranged on the two sides of the neck band, the neck band is fixed through the neck band connecting piece after surrounding the neck by a circle from the lower portion of the lower jaw backwards, and a plurality of electrode plates are installed on the mask and the neck band in an embedded mode respectively. And a massage device is detachably mounted above each ear opening of the mask. Electrical stimulation and massage are combined, comprehensive treatment is achieved for dysphagia of a patient, the rehabilitation training effect is good, comfort is good, operation is easy and convenient, cost is low, and the rehabilitation training device is suitable for household rehabilitation training.

The application discloses a clinical training massage device for a stroke patient, which comprises a docking buckle, a controller, a sleeving cover, a fitting limiting piece, a massage seat and a pressing device. When the clinical training massage device is used, the pressing assembly is connected to the patient's leg through the docking buckle and the massage seat, the traction piece is connected to the lower end of the arc adjusting piece through the movable connecting piece, the arc adjusting piece is adjusted through the traction of the two, the two massage contact pieces on the arc adjusting piece are simultaneously opened inwards or outwards under the traction of the connecting traction piece, the patient's leg is effectively wrapped by the massage seat, and the clinical training massage device effectively has a good massage effect.

The application discloses a biomarker for predicting the adverse functional prognosis of acute mild stroke and high-risk TIA patients and application thereof, and belongs to the technical field of biology. The application discloses the correlation between the low-density lipoprotein receptor-related protein 1 (LRP-1) index and the adverse functional prognosis of acute mild stroke and high-risk TIA patients, verifies the prediction value of LRP-1 in the adverse functional prognosis of acute mild stroke and high-risk TIA patients, and constructs a joint prediction model by combining LRP-1 and traditional risk factors, thereby improving the prediction ability of the traditional model, and being beneficial to more accurately and efficiently identifying the high-risk population of stroke occurrence and adverse functional prognosis, and early intervention, so as to improve the life quality of the stroke patients.

The present application relates to the field of nutritional balance evaluation, and discloses an ICU stroke patient nutritional balance real-time evaluation and optimization method based on artificial intelligence, comprising the following steps: collecting patient activity energy consumption through a wearable device, combining dietary intake and metabolic indicators to construct an individualized nutritional metabolism model, calculating nutritional balance degree in real time and generating a quantitative deviation index.Combining the quantitative deviation index and the target of the rehabilitation stage, a nutrition optimization scheme is generated through a reinforcement learning algorithm, the scheme is executed and trained, and the model parameters of the individualized nutritional metabolism model are automatically iterated to achieve the purpose of continuous optimization.The present application overcomes the pain points of traditional nutritional evaluation of ICU stroke patients, such as lag and dependence on experience-based decision-making, and realizes precise matching of nutritional supply and rehabilitation demand through the form of artificial intelligence, thereby shortening the stay time of ICU stroke patients in the ICU.

1. Name of the designed product: rehabilitation chair (for stroke patients). 2. Use of the designed product: for rehabilitation training. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

The invention provides a shoulder joint semi-dislocation image data processing method and device and a medium. According to the method, medical image data of a first area of a shoulder joint of a to-be-detected object is obtained, then motion artifact detection processing is performed on the medical image data of the first area, motion artifact level information of the medical image data is obtained, and under the condition that the motion artifact level information indicates that the medical image data meets a preset quality condition, motion artifact detection is performed on the shoulder joint of the to-be-detected object. And performing shoulder joint anatomical mark detection and distance measurement processing on the medical image data to obtain a first distance parameter between the acromion and the humeral head and / or a second distance parameter between the acromion and the humeral greater tubercle, and finally, determining a shoulder joint subluxation evaluation result of the to-be-detected object according to the first distance parameter and / or the second distance parameter. Therefore, accurate and repeatable quantitative measurement of the distance between the acromion and the humeral head and the distance between the acromion and the humeral greater tubercle can still be realized in a high-exercise-risk population, namely a stroke patient.

The invention belongs to the technical field of patient rehabilitation auxiliary equipment, and particularly relates to a stroke patient limb rehabilitation activity auxiliary device which comprises a first U-shaped hollow metal frame and a second U-shaped hollow metal frame, the outer wall of the first U-shaped hollow metal frame is fixedly connected with an L-shaped hollow rod, and the outer side end of the L-shaped hollow rod is fixedly connected with a check block. The limb rehabilitation activity assisting device for the stroke patient not only has the function of accurate height adjustment, but also can be accurately matched with the height of the stroke patient, can avoid various discomfort of the patient during exercise, has the ability of alternate movement, can continuously support and assist the stroke patient who performs lower limb exercise, and is convenient to use. The function of improving the stability of rehabilitation exercise of the lower limbs of the stroke patient is achieved, the situation of injury caused by falling down is avoided, the use reliability of the device can be improved, and the safety and effect of the rehabilitation exercise of the limbs of the stroke patient can also be improved.

The application relates to the technical field of intelligent medical auxiliary diagnosis and automatic evaluation of nerve functions, in particular to an NIHSS score automatic evaluation system for stroke patients. The system comprises a multi-modal induction and perception unit, which is used as a front-end data inlet and collects patient responses in real time to generate a video stream containing depth and color information and a synchronous audio stream; a dynamic reference calibration unit, which extracts kinematic characteristics to construct an individualized neural reference template; a neural motion frequency spectrum decomposition unit, which generates a frequency spectrum pathological characteristic vector distinguishing muscle weakness and ataxia; a contralateral mirror inhibition analysis unit, which generates compensation and driving confidence representing real neural driving intentions; a cross-modal reflection analysis unit, which generates a sensory pathway integrity index according to the difference in nerve conduction velocity; and a collaborative scoring decision engine, which maps multi-modal characteristics to a standardized NIHSS score. The application effectively eliminates the interference of age and basic physical fitness on the score, and can also provide high-precision comparison references for subsequent abnormality determination of the affected side.