106 results about "Patient feedback" patented technology

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention discloses an intelligent hospital guide and tracking return visit recording integrated system for medical examination, which relates to the technical field of intelligent medical treatment, and comprises the following steps: based on a comprehensive data set, using an A-star algorithm to identify a hospital guide path; in the process that the patient waits for the hospital according to the hospital guide path, the system collects behavior data of the patient in real time; based on historical data, training a waiting time difference prediction model, inputting the patient behavior data into the waiting time difference prediction model, and predicting a waiting time deviation value; dynamically adjusting a hospital guide path according to the waiting time deviation value; and after the patient finishes visiting according to the adjusted hospital guide path, the system automatically provides health propaganda and education contents, collects patient feedback data and stores the patient feedback data in the tracking return visit system. Through the A-star algorithm and the waiting time prediction model, the system can perceive waiting delay in advance and adjust a path in time, the waiting time of a patient is remarkably shortened, and the doctor-seeing experience is improved.

The invention discloses an intelligent analysis method and system of an oral cavity image, relates to the technical field of image processing, and aims to calculate a tooth deviation index YPZ through intelligent processing and analysis of the oral cavity image and accurately reflect the overall deviation degree of tooth arrangement and occlusion relationship. Through comparison with a preset clinical statistical deviation threshold LZ, an oral suggestion evaluation scheme can be generated, the deviation index and the deviation threshold are iteratively optimized according to feedback information of a patient, in the traditional orthodontic process, a doctor depends on experience and manual judgment, subjective errors may exist, and the adjustment process is lack of a real-time feedback mechanism, so that the accuracy of orthodontic treatment is poor. The lagging of the treatment effect is caused. Through a feedback and adjustment mechanism of the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, dynamic evaluation of the oral health condition and real-time optimization of the treatment scheme are realized by using a back propagation algorithm, and the accuracy and efficiency of the treatment effect are greatly improved.

The invention discloses a generative artificial intelligence auxiliary method and system for lower lumbago rehabilitation, and the implementation method comprises the following steps: S1, collecting health data of a patient in real time through a data collection module, and processing the health data; s2, the generative artificial intelligence auxiliary system converts the dialogue content between the patient and the doctor into a structured text record through an automatic voice recognition module and a natural language processing module, and the structured text record is integrated into an EHR system; s3, analyzing the health information of the patient by using the generative AI model, and generating a simplified medical language version; s4, generating a personalized rehabilitation scheme based on the health data; s5, the rehabilitation scheme is pushed to the patient through a user interface, and the generative artificial intelligence auxiliary system dynamically adjusts the rehabilitation scheme in real time according to feedback of the patient; and S6, continuously optimizing the generative AI model by continuously collecting patient feedback data. According to the invention, a personalized rehabilitation scheme can be automatically generated.

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention belongs to the related technical field of neural rehabilitation engineering, and particularly relates to a cerebral apoplexy rehabilitation method based on fusion of a motor imagery brain-computer interface and transcranial direct current stimulation, which comprises the following steps: acquiring a corresponding artifact template based on transcranial direct current stimulation parameters; carrying out source separation on the collected electroencephalogram test times by adopting a joint blind source separation algorithm to obtain clean electroencephalogram signal components; wherein the construction mode of the artifact template library comprises the following steps: extracting electroencephalogram trial artifact samples of multiple stimulation periods, and generating a transcranial direct current stimulation artifact template through an average analysis or principal component analysis mode to obtain the artifact template library; performing motion intention decoding on the clean electroencephalogram signal component to obtain a motion intention of the patient; inputting the motion intention, pre-collected patient body state evaluation information and patient feedback comfort into a fuzzy system algorithm to obtain stimulation parameters of to-be-applied transcranial direct current stimulation, and realizing active rehabilitation through the transcranial direct current stimulation. According to the invention, algorithm-driven closed-loop rehabilitation regulation and control can be realized.

The invention relates to the technical field of medical health, in particular to a chronic disease patient-oriented home nursing management platform, comprising: a terminal sensing module comprising a monitoring bracelet and a glucometer and used for obtaining real-time data of a patient; the monitoring bracelet is used for monitoring physiological indexes and motion data of a patient in real time; the glucometer is used for monitoring blood glucose data of a patient in real time; the edge calculation module comprises home gateway equipment and an edge intelligent analysis system; the cloud service module comprises a data processing center and an intelligent decision-making system; the intelligent decision-making system comprises a health risk assessment model, an exercise scheme optimization model and an expert knowledge base comprising an exercise guidance strategy and a life rhythm management strategy; and the optimization module is used for comprehensively coordinating the generated results, dynamically adjusting motion scheme parameters according to patient feedback and execution conditions, and pushing an optimized personalized scheme to a user terminal for execution.

The invention belongs to the technical field of medical data processing, and relates to a urinary surgery diagnosis and treatment data intelligent processing method and system, and the system comprises a multi-modal data collection module which is used for collecting basic diagnosis and treatment data, behavior data and diagnosis and treatment process data of a patient; the diagnosis and treatment data intelligent analysis module is used for calculating the intention value of the patient for the diagnosis and treatment item and generating a diagnosis and treatment path diagram; the intelligent decision support module is used for generating personalized diagnosis and treatment suggestions in combination with a guide and a model; the doctor-patient interaction feedback module is used for collecting patient feedback and updating an analysis weight; and the follow-up management module is used for generating a follow-up plan and relapse early warning. Basic diagnosis and treatment data and patient behavior data are fused, and diagnosis and treatment requirements are reflected more comprehensively; the dimension weight is adjusted and analyzed in real time through patient feedback, and the personalized diagnosis and treatment precision is improved; a guide rule and a machine learning model are combined to assist a doctor to quickly generate an optimal treatment scheme; through the block chain and privacy protection technology, compliance use of the medical data is ensured.

The invention relates to the technical field of adjuvant therapy monitoring, in particular to an anorectal disease adjuvant therapy monitoring system and method based on a radix scrophulariae extract. The method comprises two stages of system initialization and adjuvant therapy monitoring, in the initialization stage, initialization setting is completed, and a dynamic monitoring code is generated to guarantee data transmission security; in the auxiliary treatment stage, baseline data are collected, abnormal values are removed, then personalized baselines are generated, the drug dosage and the release rate are adjusted according to the baseline data, meanwhile, pressure, temperature, blood flow and electrophysiological data are monitored in real time, key indexes are calculated, monitoring labels are generated, the physiological indexes are standardized, the treatment effect is comprehensively evaluated, and classification is conducted. The dynamic trend is analyzed by adopting a polynomial fitting method, an evaluation result is generated, the drug release rate and dosage are dynamically optimized in combination with real-time monitoring data and patient feedback, and fine treatment is further performed by adopting a feedback adjustment mechanism. According to the invention, efficient auxiliary treatment and intelligent monitoring of anorectal diseases are realized.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The present invention discloses a rehabilitation training system for MCI patients in different scenarios based on deep learning, which belongs to the field of rehabilitation training technology and includes a data collection module, a data processing module, a model building module, a prediction analysis module and a rehabilitation training module. The present invention solves the problem that the existing rehabilitation training for MCI patients cannot perform rehabilitation training for MCI patients in different scenarios, and cannot adjust and optimize the rehabilitation training process in real time, resulting in poor rehabilitation training effect for MCI patients. The present invention performs rehabilitation training for MCI patients in different scenarios according to rehabilitation training schemes for different scenarios, adjusts and optimizes rehabilitation training schemes based on different scenarios for MCI patients based on feedback information from MCI patients, can perform rehabilitation training for MCI patients in different scenarios, can adjust and optimize the rehabilitation training process in real time according to the rehabilitation training situation of MCI patients, and can improve the rehabilitation training effect of MCI patients.

The invention provides a head and neck cancer patient symptom management method and system based on multi-source data fusion and AI driving, and the method comprises the steps: integrating electronic medical records, wearable equipment, questionnaire survey, popular science videos and other multi-modal data, employing an attention mechanism to dynamically fuse features, and constructing unified knowledge representation. A dynamically extended symptom-risk-intervention knowledge graph is established based on a Bayesian network and a graph neural network (GNN), and cross-layer symptom recommendation is realized in combination with a collaborative filtering algorithm. The symptom severity is evaluated in real time through an XGBoost algorithm, three-color early warning is output, and finally the knowledge graph is continuously optimized through reinforcement learning based on patient feedback data. According to the method, accurate and personalized symptom management of the patient with the head and neck cancer is realized.

The invention discloses a cardiac patient personalized rehabilitation plan generation and optimization method, and relates to the technical field of medical treatment, and the method comprises the steps: S1, carrying out the real-time collection and analysis of the physiological parameters and behavior patterns of a patient based on multi-source data, S2, monitoring the psychological state of the patient through psychological scale evaluation and an emotion calculation model, s3, dynamically adjusting the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve hyperexcitation; S4, optimizing the rehabilitation plan based on patient feedback and clinical data; according to the personalized rehabilitation plan generation and optimization method for the heart disease patient, a comprehensive rehabilitation management scheme is provided for the heart disease patient, potential inducements of cardiovascular events are effectively reduced, the heart burden is relieved, and an important guarantee is provided for long-term rehabilitation of the patient; and the psychological state of the patient is dynamically monitored and actively intervened to regulate and control so as to solve the problem of hyperexcitation of sympathetic nerves caused by psychological stress.

A method and apparatus for generating a visual field pattern which serves as a subjective test protocol to evaluate central vision using dynamic image generation to prevent the brain from filling-in to provide better results of patient feedback and reporting of locations of visual disturbance. This enables effective central vision testing to detect the presence of visual field disruptions including scotoma, visual-neurological perceptual effects such as filling-in, and Troxler fading. The present method and apparatus can be used for vision monitoring and self-assessment, and more particularly for monitoring vision for damage or disease to the macula or retina.

The invention discloses a moxibustion technique track acquisition and reproduction method, which relates to the technical field of moxibustion treatment, and comprises the following steps: installing a sensor and a patient feedback device, collecting moxibustion environment data and patient information, and dynamically adjusting initial parameters; according to the initial parameters, collecting a moxibustion technique track of a moxibustion technician, change of a moxibustion application environment and real-time feedback of a patient, performing multi-modal data fusion, generating a high-quality track data set, and training a machine learning model to obtain a track learning result; combining a track learning result with moxibustion application environment data and patient information, and controlling the moxibustion robot to execute manipulation track reproduction; the moxibustion applying effect is evaluated, and a moxibustion applying report is generated; according to the invention, the consistency and safety of the moxibustion effect are greatly improved by integrating a plurality of sensors and combining moxibustion environment data and patient personalized information. Meanwhile, moxibustion applying parameters can be automatically adjusted according to real-time feedback of the patient, the comfort level of the patient is further improved, and the treatment experience is optimized.

The invention relates to a hospital nursing resource intelligent allocation method and device, equipment and a medium. The method comprises the following steps: generating a nurse skill assessment report based on nursing operation records, training data and patient feedback; secondly, analyzing a nursing demand dependency relationship and a time constraint in the electronic medical record, and constructing a patient demand constraint graph; further quantitatively matching the nurse skill items with the patient demand items, and generating a nurse-demand sorting result sorted according to the matching degree; and finally, through deviation analysis of actual execution data and a prediction result, continuously optimizing the nurse skill evaluation model. According to the method, the technical defects that dynamic changes of nurse skills are not captured in real time, a complex nursing demand dependency relationship is not systematically analyzed, man-post matching lacks a quantitative basis and execution feedback does not drive model optimization in traditional manual distribution are overcome, and the nursing resource utilization rate and the clinical demand response accuracy are remarkably improved.

The invention relates to the technical field of telemedicine, and particularly discloses an online inquiry method and device, computer equipment and a storage medium. According to the method, the doctor can be accurately matched according to the inquiry information and the historical medical data of the patient, the inquiry information and the historical medical data are analyzed, the to-be-examined part and the examination prompt of the patient are determined, effective reference is provided for diagnosis and treatment, manual analysis is not needed, the virtual consulting room is constructed through the augmented reality equipment, and the diagnosis efficiency is improved. Patient feedback can be obtained in time according to the patient virtual image, and the diagnosis efficiency is improved. The method is applied to an online inquiry system in the medical field, inquiry information and historical medical data of a patient can be accurately analyzed, and a doctor is effectively assisted in quickly and accurately obtaining a diagnosis result.

The invention relates to the technical field of internet information, and provides an order sending method, system, device and medium for remote consultation, and the method comprises the steps: obtaining the details of a patient, the details of the patient comprise the information of the patient, the specialist of the illness state, and the priority of the condition of the patient; generating an order sending queue of doctors according to patient information and illness conditions; dynamically generating a scheduling amount in batches in an order dispatching queue according to patient condition priorities, and dispatching orders in sequence; the doctor who obtains the dispatched order receives the consultation call application; collecting patient feedback, and updating the comprehensive score of the doctor. The most suitable doctor is quickly matched, the waiting time is shortened, and the consultation efficiency is improved; factors such as geographic positions, professional competence and service evaluation are comprehensively considered, and reasonable distribution of doctor resources is realized; based on a comprehensive scoring mechanism, doctors are motivated to improve service quality, and patient satisfaction is enhanced.

The invention discloses an intelligent follow-up visit management system for skin disease patients, and relates to the technical field of Internet. During operation of the system, multi-source data is acquired from mobile equipment, medical equipment and environment acquisition equipment worn by the patients, a comprehensive original data set D covering illness conditions and environment factors is formed, summarizing calculation is performed, and a follow-up visit result is obtained; according to the method, the dynamic follow-up scoring index DP is generated, so that the follow-up frequency adjustment identifier SF is generated, accurate regulation and control of the follow-up frequency are realized, and the defect that a fixed follow-up period cannot respond to the change of an illness state in real time is overcome. The evaluation feedback and adjustment module calculates a follow-up visit frequency adjustment identifier SF for the second time based on follow-up visit data fed back by the patient, and substitutes the follow-up visit frequency adjustment identifier SF into the data processing and feature extraction module for iterative optimization; the system can continuously adapt to dynamic changes of illness conditions, and precision and individuation of follow-up visit plans are achieved.

The invention relates to a respiration guiding system for image examination, which is applied to the technical field of image examination and comprises a respiration monitoring module, a patient feedback module, a control processing module and a physician interaction terminal, a plurality of different target breathing waveforms are pre-stored in the doctor interaction terminal, the control processing module generates real-time breathing waveforms based on breathing waveform signals, time synchronization is conducted on the target breathing waveforms and the real-time breathing waveforms, then the patient feedback module and the doctor interaction terminal are controlled to be displayed in a parallel comparison mode, and a patient is guided to adjust breathing. According to the invention, through ''physician-system-patient'' three-element closed-loop interaction, respiratory training and pre-judgment before scanning and continuous guidance and stable maintenance during scanning are realized, so that the success rate of first scanning is improved, the number of times of repeated scanning is reduced, and the radiation dose risk and medical cost of a patient are reduced.

The invention discloses a data analysis system for predicting respiratory diseases of children, and relates to the technical field of health data analysis, and a data acquisition platform comprises a data cleaning model used for acquiring heterogeneous data of the respiratory diseases of children; the data cleaning model identifies the data inconsistency of the child respiratory diseases, analyzes unstructured clinical patient feedback, and extracts semantic information; the prediction model construction module is used for constructing a hybrid prediction model based on the medical environment data and the semantic information; and the scheme making module is used for inputting the respiratory disease condition of the child into the prediction model building module, analyzing the influence of different climate changes on the air quality and customizing a prevention and treatment scheme. According to the invention, real-time early warning of air quality is realized, parents and medical institutions are helped to take preventive measures in time, the onset risk of respiratory diseases of children is reduced, the intelligent level of prediction is improved, and a solid technical support is provided for personalized health management.

The invention discloses a disease control state evaluation system and method based on psoriasis patient feedback, subjective feelings of patients on disease control can be captured in clinical and non-clinical scenes, and a concept framework is based on literature and doctor-patient feedback. Different from the past psoriasis PROM, the invention provides a method for comprehensively, standardly and quickly collecting'disease control 'information, and the structure content has value for clinical and scientific research. Statistical analysis shows that good clinical benefits can be obtained when the model is applied clinically, and an effective basis for individualized precise treatment is provided for improvement and self-management of psoriasis, especially chronic diseases in China.

A method for capturing patient feedback for a medical treatment includes: receiving user instructions indicating a symptom being treated, a symptom score for the symptom being treated, and an indication of a beginning of a treatment session; receiving patient feedback comprising an updated symptom score for the symptom being treated and / or absence or presence of one or more of a plurality of attributes; storing the received patient feedback and a time in the treatment session at which the respective patient feedback was received; repeating the receiving of patient feedback and storage of data during the treatment session; and receiving a user instruction indicating an ending of the treatment session, wherein an alerting module alerts a user of the computing device for input of patient feedback after a predetermined period of time has elapsed following receipt of patient feedback without a subsequent receipt of patient feedback.

The present invention belongs to the field of medical simulation and education technology. The present invention discloses a method and system for building an interactive medical simulation scene based on AI artificial intelligence, including collecting patient physiological data, medical information data and patient feedback data; preprocessing the patient physiological data and medical information data to obtain a physiological characteristic data set and a medical information characteristic data set; fusing the patient feedback data into the physiological characteristic data set and the medical information characteristic data set to obtain a medical comprehensive characteristic data set; training a medical scene prediction model, inputting the medical comprehensive characteristic data set into the medical scene prediction model, and predicting medical scene simulation data; processing the predicted medical scene simulation data to obtain data simulation similarity error; providing more realistic data support for medical training, research and decision-making, and improving the effect and reliability of medical simulation.

The invention discloses a modularized oral medical service platform intelligent doctor recommendation system, and relates to the technical field of medical information systems. According to the method, the to-be-selected doctors are screened out after the patient inputs the type of the oral disease needing to be treated, evaluation is carried out from three dimensions of efficiency, evaluation and experience, weighted calculation is carried out based on the evaluation result to obtain the biased comprehensive index, and the patient can accurately calculate the biased comprehensive index according to own demand preference when the biased comprehensive index is calculated. Weight adjustment is carried out on the efficiency expression index, the patient feedback index and the experience expression index, to-be-selected doctors are arranged according to the deviation comprehensive index, an arrangement result is pushed to the patient, and the patient selects the corresponding to-be-selected doctor to carry out appointment registration after receiving the pushing result.

The present invention discloses a retinal fixation training method and device based on fundus images, belonging to the field of retinal training technology, comprising the following steps: S1: adjusting a fundus camera to obtain a real-time fundus image of a patient based on the fundus camera; S2: processing the real-time fundus image of the patient to determine a fundus feature image of the patient; S3: analyzing and evaluating the fundus feature image of the patient to determine the fundus image analysis and evaluation result; S4: performing intelligent retinal fixation training on the patient; and S5: optimizing and adjusting the retinal fixation training program based on patient feedback and suggestions. The present invention solves the problem that existing personalized retinal fixation training programs cannot be formulated for patients based on fundus images, resulting in poor retinal fixation training results for patients. The present invention can formulate personalized retinal fixation training programs for patients based on fundus images, thereby improving the retinal fixation training results for patients.

This invention provides a basic jaw pad, a dental instrument, and a preparation method. The basic jaw pad includes a first support portion and a second support portion connected to each other. After the basic jaw pad is installed in the through hole of an orthodontic appliance, at least a portion of the first support portion is located within the cavity of the orthodontic appliance for accommodating the dentition. The first support portion mates with the occlusal surface of the dentition on which the orthodontic appliance is worn. At least a portion of the second support portion is located outside the orthodontic appliance and protrudes towards the occlusal surface of the opposing dentition. The basic jaw pad has a preset basic shape, and at least the material of the second support portion of the basic jaw pad is a polished material. This invention improves the versatility of the basic jaw pad, enabling mass production. Furthermore, clinicians can make real-time adjustments and optimizations based on the target positions of the upper and lower jaws in the patient's occlusal state, the specific situation during actual wear, and patient feedback, thereby improving the orthodontic effect.

The invention belongs to the field of auxiliary medical treatment, and provides a personalized electrical stimulation treatment scheme generation system and method, and the method comprises the steps: obtaining a voice signal of a patient, and extracting voice dynamic change information; acquiring neuromyoelectric signals, heart rate signals and motion information of the patient, and extracting physiological change information; performing association modeling on the voice change information and the physiological change information to generate fusion features for representing a multi-part tremor mode of the patient; performing dynamic generation of personalized electrical stimulation parameters based on the fused features; constraining the dynamic change of the current amplitude, the stimulation frequency and the pulse width within an individually set safety threshold range, and performing protective adjustment in combination with real-time feedback of a patient; and the electrical stimulation module is used for implementing electrical stimulation on a target nerve part in the radial nerve, the median nerve or the ulnar nerve according to the parameters issued by the decision module.