62 results about "Exercise prescription" patented technology

The present application provides a comprehensive approach to improving cardiometabolic health and fitness through assessing key current health and fitness metrics, testing, and assignment of appropriate movement protocols that are specific to the needs of the individual. The present application provides a unified system for cardiometabolic health testing, activity and exercise monitoring, and user education and engagement. This platform may also be useful for the management of current health dysfunction and for the prevention of disease.

The invention belongs to the technical field of health assessment, and particularly relates to an artificial intelligence-assisted coronary heart disease patient cardiopulmonary fitness assessment method. The system is specifically composed of five core modules including a multi-source lightweight data acquisition module, a data fusion and intelligent processing module, a community artificial intelligence evaluation model, a precise exercise prescription generation module and a community collaborative feedback and management module. Through a technical chain of multi-source lightweight data fusion, a community artificial intelligence evaluation model, dynamic prescription optimization and cross-level collaborative management, an evaluation result is directly converted into an executable personalized exercise prescription, adaptive adjustment is formed through real-time feedback, and a whole-process data closed loop from evaluation to intervention is formed; through grading diagnosis and treatment and intelligent tools, the social medical cost is remarkably reduced; in addition, doctor-patient cooperation and full-process management efficiency is greatly improved, a grassroots community hospital has the assessment capability close to that of a superior hospital, and the urban and rural medical treatment gap is reduced.

The invention relates to the technical field of medical care, in particular to a lung cancer chemotherapy patient resilience intervention system and application thereof. According to the intervention system based on the PERMA mode, intervention is implemented in stages through comprehensive measures such as acute symptom management, psychological construction strengthening, chemotherapy adverse reaction cognition, home nursing skill training, physical recovery training, social support linkage and treatment compliance improvement. The method comprises the following steps: quantifying pain by using digital scoring equipment, and assisting pressing to relieve symptoms by using acupoint diagrams; the psychological toughness is enhanced through the positive voice listening and the anti-cancer brave diary recording; an online and offline combined mode is adopted, group lectures, scene simulation video teaching and online community interaction are carried out, and social support of patients is enhanced; personalized exercise prescriptions and home safety lists are formulated, and treatment compliance is improved. The system can significantly improve the recovery of a patient, improve the life quality during chemotherapy, and provide a scientific basis for innovation of a tumor nursing mode.

The invention provides a personalized exercise prescription generation method based on an analytic hierarchy process, which comprises the following steps of: acquiring body index information, psychological state information, exercise preference information and exercise environment information of a user, determining a physiological health index, a psychological health index, environment suitability and user preference of the user according to the collected user information, and recommending a plurality of candidate exercises; constructing an analytic hierarchy process evaluation model based on four factors of a physiological health index, a psychological health index, environmental suitability and user preference of the user so as to comprehensively score candidate motions; an exercise prescription is generated for the user according to the comprehensive score of the candidate exercises, and parameters in the exercise prescription comprise recommended exercises, exercise intensity, exercise frequency and duration; and acquiring exercise execution data and tracking feedback data of the user, and adjusting parameters in the current exercise prescription.

The invention provides a body function evaluation method and device based on multi-modal sensing fusion. Real-time detection and analysis of human motion and postures are achieved through a multi-modal sensing technology. According to the method, the test process is standardized, operation is automatic, scoring is objective, the accuracy and repeatability of the test are improved, and manual intervention is reduced. Through visualization and voice guidance, action demonstration and prompts are provided on a large screen, it is ensured that a testee completes a test autonomously, and the operation burden of workers is reduced. Meanwhile, the system automatically calculates scores and generates reports according to a standard SPPB scoring rule, provides result interpretation and rehabilitation suggestions, and remarkably improves the evaluation efficiency and practicability. In addition, a personalized video exercise prescription is generated according to the test score, so that the testee can track the training progress conveniently. The closed loop of detection and intervention services is ensured, and the rehabilitation effect is improved.

The invention provides a multi-mode biological signal dynamic fusion fitness guidance method and system. The method comprises the steps that a signal acquisition layer synchronously acquires dynamic force and static pressure through a flexible voltage capacitance sensor; in the feature processing layer, frequency domain energy distribution is extracted from voltage signals through short-time Fourier transform, static pressure gradient is obtained from capacitance signals through wavelet packet decomposition, time-frequency decomposition is carried out, and feature dimensionality reduction is carried out; the fusion modeling layer is used for eliminating time migration of sensor data through dynamic time warping to carry out time alignment and carrying out feature fusion based on a Transform attention mechanism; and the decision generation layer performs incremental learning and large model driven fine tuning to generate a personalized exercise prescription. Based on the technical scheme of the invention, the muscle state analysis precision is effectively improved through the dynamic time-frequency fusion of the dynamic and static signals; real-time dynamic feedback of the system is improved; the model power consumption is reduced, and the noise suppression rate is improved; and the personalized adaptation degree is improved by a dynamic threshold value.

The invention relates to an exercise prescription generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining target input information of a user, and achieving the standardization of the input information; on the basis of the target input information, retrieval is performed in the plug-in knowledge base to obtain the retrieval result matched with the target input information, so that the retrieval result is professional, and the retrieval accuracy is improved; the target input information and the retrieval result are input into the trained retrieval generation model, the exercise prescription is output, the retrieval generation model is iteratively trained according to the marked fine adjustment data set, compared with traditional medical consultation, the exercise prescription is output through the trained retrieval generation model, the generation time of the exercise prescription is shortened, and the user experience is improved. Compared with general artificial intelligence model interaction, the exercise prescription generated by the method is combined with the retrieval result of the plug-in knowledge base, and the method is more professional.

The invention discloses a personalized exercise prescription generation method fusing deep learning, learnable similarity and multi-modal time sequence analysis. The method comprises the steps that firstly, static signs and health risk labels of a user and dynamic signals such as the heart rate, the HRV and the step number collected by wearable equipment in real time are integrated, and unified modeling is conducted through a multi-modal encoder to form individual state vectors; then outputting a preliminary motion scheme by a BERT-Transform text generation model, mapping a user to a hidden space taking a historical execution effect as supervision by using a comparative learning network, and accurately inferring an FITT core parameter through approximate nearest neighbor retrieval and effect weighting; performing consistency optimization on texts and parameters through a large language model, completing risk verification under a safety barrier constructed by a medical rule base, ensuring that output meets clinical taboo and individual tolerance, and finally continuously optimizing long-term health income under medical constraints in combination with an offline reinforcement learning strategy, and cooperating with meta-learning cold start and uncertainty estimation, so as to achieve the purpose of improving the safety of the health management system. Safe, accurate, self-adaptive and explainable personalized exercise prescription generation for new and old users is realized, and the method can be widely applied to intelligent fitness, chronic disease exercise intervention and digital health management scenes.

The invention discloses a skeletal muscle repair method and system based on muscle satellite cell regulation and control. The method comprises the following steps: performing proliferation and differentiation correlation analysis on satellite cell steady state data and aerobic exercise intensity data to establish a proliferation regulation and control map; determining a collaborative repair time window according to the atlas, extracting difference response deviation correction parameters to form an activity correction factor, and performing repair intensity correction on the collaborative repair time window to generate a self-adaptive repair window; carrying out exercise prescription adaptation on the self-adaptive repair window, identifying a satellite cell activation regulation and control channel, obtaining activation delay time through activation efficiency mapping, and delimiting a response level according to the activation delay time to generate layered repair configuration; activation delay evaluation is conducted on layered repair configuration to determine a preferred activation mode, dynamic activation characteristics are extracted based on optimal activation time window data to form an intervention execution sequence, a skeletal muscle repair execution instruction is output in combination with an exercise prescription parameter table, and dynamic collaborative adaptation of satellite cell repair state monitoring and training scheme parameters is achieved.

This invention relates to the field of exercise prescription generation and recommendation technology, and discloses an LLM-based generative recommendation method for exercise prescriptions. The method involves acquiring and preprocessing data, constructing a knowledge graph based on the preprocessed data (S1), pre-training the LLM model, inputting the constructed knowledge graph into the LLM model, and optimizing it through reinforcement learning. The PPO algorithm is used to optimize the LLM model's output strategy. Based on the optimized LLM model, the exercise prescription is derived and generated in natural language using FITT parameters. This method combines user characteristics and scenario information to generate high-quality, personalized exercise prescriptions. The invention utilizes a multi-dimensional reward function framework to quantify prescription quality and optimize the generation strategy, ensuring that the generated exercise prescriptions are not only scientifically sound but also conform to the user's actual situation and preferences. Synthetic data is generated through a user simulator, achieving an upgrade from static recommendation to dynamic adaptation, improving user experience and the level of intelligence in exercise and health management.

The invention belongs to the technical field of information processing and artificial intelligence, and discloses an exercise prescription generation method, device and system based on clinical data, a medium and equipment, and the method comprises the steps: obtaining the clinical data of a test object; joint physiological features are extracted from the clinical data based on a multi-source heterogeneous data fusion model; inputting the extracted combined physiological features into a dynamic randomization engine, and grouping the combined physiological features based on the dynamic randomization engine; and constructing and training a health state prediction model, inputting the grouped combined physiological features into the trained health state prediction model, outputting a health state level of the test object and a physiological state evolution track under different exercise interventions, and generating a personalized exercise prescription. According to the method, accurate prediction of the individual health state and personalized generation of the intelligent exercise prescription can be realized, and scientificity, pertinence and practicability of health management of the test object are effectively improved.

The invention discloses a lung cancer patient home exercise rehabilitation training system based on a severe game, and the system comprises a patient terminal device which is used for operating a severe game application and collecting exercise behavior data and physiological signals; the medical care terminal management interface is used for medical care personnel to log in and check patient information and training execution conditions; and the server / cloud platform comprises an exercise prescription engine module which is used for generating an individualized exercise prescription based on the baseline evaluation data of the patient. Through a virtual game scene and a hierarchical breakthrough and reward mechanism, the originally boring respiratory training, resistance training and walking training are integrated into a severe game with the sense of sexuality and goals, the participation interest and the sense of achievement of a patient are stimulated, and the possibility of long-term persistence is remarkably improved.

The present application relates to a kind of children's physical health exercise guidance method and system based on intelligent algorithm, personal physical data and physical activity data are collected using acquisition equipment, then the physical data and physical activity data collected are matched database, generate personal record.Intelligent algorithm analyzes personal record, automatically analyzes basic body data, automatically classifies, generates personalized exercise prescription.Personalized exercise prescription is pushed to user in real time in system front end, user continues to exercise according to personalized prescription.Subsequently, motion data in the exercise process are collected by acquisition equipment and are transmitted to cloud, the exercise prescription and data of this time are stored by cloud using big data, in combination with artificial correction error prescription mechanism, the wisdom algorithm model is nibbled optimization, overcome the technical defects that existing method lacks systematicness and integrity, lacks curative effect evaluation and quality control, detection result is not accurate and system automation degree is low.

This application discloses an exercise prescription generation device, method, equipment, and medium, relating to the field of sports health management technology and applied to computer devices. It includes: a feature extraction module that simultaneously collects body composition and muscle strength data from obese subjects, and obtains body composition and muscle strength features through preprocessing and feature extraction; a preliminary generation module that performs hierarchical judgment on the two types of features to determine the obesity type and muscle strength level, and generates a preliminary exercise prescription based on the combination results; a prescription generation module that uses the body-muscle combination index to determine aerobic exercise parameters and determines resistance training parameters based on the muscle strength level, generating a target exercise prescription for the current execution cycle; an exercise review module that obtains the review results after the subject executes the prescription; and a prescription adjustment module that adjusts the prescription based on the review results to obtain the target exercise prescription for the next execution cycle. This effectively solves the problems of single-dimensional exercise prescription assessment, insufficient personalization, inaccurate intensity matching, and lack of quantitative decision-making and dynamic adjustment.

The present invention discloses a personalized exercise prescription recommendation system based on big data analysis. User information is used to find an exercise method that suits the user. Because under gravity, pressure is downward, and the human body is supported by the feet. This pressure is related to the body's posture. Therefore, during training, multiple training segmented images can be obtained through historical user movement footprint images. A relationship is thus established between the segmented images and the pressure exerted by the user's posture on the footprints. Therefore, the multiple segmented images can not only represent the user's movement posture, but also the force applied to each body part when the user makes the gesture. This allows the user to determine whether the user's movements are standard. This allows the user to recommend an exercise prescription that enables the user to make movement postures more smoothly and accurately apply force to each body part.

The invention discloses a multi-mode spine health management device and system based on an AI Agent and a game interaction platform, through an AI Agent module, personalized exercise prescriptions are generated according to health data of teenagers, and intelligent services of'one person, one spine and one prescription, and thousands of persons and thousands of faces of exercise libraries' are realized. According to the personalized health management mode, the scoliosis prevention and correction effect can be effectively improved, and it is ensured that each user can obtain the most suitable exercise scheme. The spine state and motion data of a user are monitored in real time through intelligent hardware, and instant feedback is provided. The exercise prescription is dynamically adjusted according to the real-time performance of the user, and the continuity and progress of the training effect are ensured. Spine health management is converted into gamification experience, so that the participation willingness and persistence of teenagers are remarkably improved. The participation degree of the user is improved, and the spine health is gradually improved in a relaxed and pleasant environment.

The application relates to the technical field of obesity exercise analysis, and discloses a method for generating age-section personalized obesity prescriptions based on deep learning, which comprises the following steps: acquiring metabolic data of target objects in different age sections within 24 hours after exercise in real time, analyzing the metabolic data, obtaining metabolic phase angle maps of different age sections, and taking the target objects as obese people; through age-section analysis of 24-hour metabolism and multi-organ data after exercise, a personalized exercise prescription is generated, from a metabolism phase angle map to a rhythm step-out amount, to a metabolism resonance factor and a target direction amount, each link focuses on age differences, accurately captures metabolic fluctuation characteristics of young and middle-aged obese people, and finally, the prescription is combined with a resonance sensitive period, a metabolic elasticity interval and the like, dynamic adaptation of intensity, time length and time period is realized, the problem that a traditional prescription is disconnected with actual metabolism is effectively solved, and the accuracy and effect of obesity intervention are greatly improved.

The invention provides a depression movement intervention effect evaluation method and system based on multi-omics data, and the method comprises the steps: obtaining a multi-omics original data set of a subject at each preset evaluation time node, the subject comprising a movement group and a control group; performing data preprocessing and core feature extraction on the multi-omics original data set to obtain a standardized multi-omics feature set; wherein the core feature extraction is used for extracting feature data related to depression improvement; based on the standardized multi-omics feature set, feature fusion and model training are carried out through a machine learning algorithm, and a multi-omics collaborative change model is constructed; and inputting feature data of different evaluation time nodes in the standardized multi-omics feature set into a multi-omics collaborative change model, and performing comparative analysis on the motion group and the control group to obtain a motion intervention quantitative evaluation result. According to the method, objective quantitative evaluation of the exercise intervention effect can be realized, and a mechanism-driven decision basis is provided for exercise prescription optimization.

A method for prescribing customized grip strength recovery exercise on the basis of an IOT-based digital dynamometer, according to an embodiment of the present invention, is a method in which a healthcare application executed by at least one processor of a terminal provides a customized exercise prescription service, and comprises the steps of: performing an initial setting by obtaining a basic grip strength at a time point before surgery; obtaining a first grip strength at a first time point after the surgery; generating a first exercise program on the basis of the basic grip strength and the first grip strength; obtaining a first exercise dataset for the first exercise program at a second time point that is a predetermined time point after starting to perform the first exercise program; generating exercise result content for the obtained first exercise dataset; and generating a second exercise program in which the composition of the first exercise program is changed on the basis of the obtained first exercise dataset.

The present disclosure relates to a method and an apparatus for prescribing exercise on the basis of hemodynamic information, and a recording medium. The method for prescribing exercise on the basis of hemodynamic information, according to an embodiment of the present disclosure, comprises the steps of: obtaining optical signals detected after passing through a user's body tissue; calculating hemodynamic information on the basis of the optical signals; calculating, on the basis of the hemodynamic information, at least one of lactate information and fat burning information; and generating an exercise prescription for the user on the basis of at least one of the lactate information and the fat burning information.

The invention relates to the technical field of exercise health big data processing and personalized exercise prescription generation, and discloses a personalized prescription generation method based on exercise big data. The method covers the whole process of motion data acquisition, time sequence standardization, feature extraction, grading judgment and prescription output. By collecting continuous heartbeat, displacement and time consumption information, an equal-interval heartbeat sequence is reconstructed and normalized, frequency domain transformation and integral analysis are combined, and physiological and behavioral characteristics of a sporter are accurately obtained. And on the basis of low-frequency and high-frequency energy integrals, a training intensity index is constructed in combination with an average speed, and a grading threshold value is set, so that automatic personalized exercise prescription output is realized. The whole process supports historical data archiving and trend analysis. According to the method, the problem that fine evaluation is difficult due to the fact that traditional methods depend on single indexes and subjective experience is solved, and the scientificity, pertinence and intelligent level of exercise prescriptions are improved.

The invention discloses a digital health management system and method based on cardiopulmonary fitness analysis, and belongs to the technical field of artificial intelligence and digital health. The method comprises the following steps: collecting and preprocessing physiological features and motion data of a user; constructing a data set containing the actually measured maximum oxygen uptake; introducing a sparrow search algorithm to optimize hyper-parameters of the multi-layer perceptron model, and training to obtain a high-precision maximum oxygen uptake prediction model; predicting by using the model; generating a correlation analysis heat map based on the physiological features of the user and the maximum oxygen uptake predicted value; generating a personalized exercise prescription based on the prediction result and an FITT-VP rule base; the maximum oxygen uptake is re-predicted regularly, the change trend is tracked, and the exercise prescription is automatically adjusted; and finally, all information is fed back through a visual interface. The method has the technical advantages of accuracy, robustness and closed loop, breaks through the limitation of a traditional health management mode, and provides a one-stop active health management service for a user.

This invention provides an intelligent exercise intensity assessment system and method. The intelligent exercise intensity assessment system includes: an exercise testing machine, a physiological information sensor, a signal transmitter connected to the physiological information sensor, a central control unit connected to the signal transmitter, and a cloud database connected to the central control unit. The physiological information sensor detects the exerciser's physiological information before and after operating the exercise testing machine. The signal transmitter transmits the physiological information to the central control unit, which then transmits it to the cloud database. The cloud database analyzes the physiological information and obtains its corresponding wattage value. Based on the wattage value, the cloud database obtains the resistance coefficient of different fitness equipment. Therefore, the intelligent exercise intensity assessment system of this invention can track and record the exerciser's data and calculate a suitable exercise prescription based on the exerciser's physiological information.

The invention discloses an oxygen uptake cardiopulmonary endurance testing method based on load turn-back walking and a human brain structure, and relates to the technical field of exercise physiology and biomedicine detection.The method comprises the steps that progressive load turn-back walking exercise data and a high-resolution brain structure image of a subject are obtained; extracting key brain region parameters such as the thickness of the anterior cinerary cortex, the grey matter density of island leaves and the signal intensity of brainstem respiratory center; normalizing the motion features and brain structure parameters and then constructing a 12-dimensional multi-modal fusion feature vector; and inputting a deep feedforward neural network model to predict the maximum oxygen uptake, and performing five-level cardiopulmonary endurance evaluation according to an age and gender correction result. According to the method, quantitative association between peripheral exercise performance and a central nervous anatomy basis can be realized, the heart and lung endurance evaluation precision of old people and individuals with abnormal neurological functions is remarkably improved, and rapid screening and personalized exercise prescription making are supported.

The invention belongs to the field of health screening instruments, and particularly relates to an exercise prescription health screening system based on pulse rest. The system comprises an acquisition module, a processing module and a regulation and control module. The acquisition module comprises a hanging ring, and the hanging ring is made of a silica gel material; a pulse sensor is arranged in the hanging ring, and the pulse sensor is a piezoelectric sensor; a plurality of airflow sensors are fixedly arranged on the right side of the pulse sensor in the hanging ring, and the airflow sensors are MEMS microphones; pressure sensors are evenly distributed on the surface, making contact with the neck, of the hanging ring. A gyroscope for collecting three-axis acceleration is arranged in the hanging ring, and the pulse sensor is used for collecting pulse data of the neck of a user. According to the scheme, the collection module, the processing module and the regulation module cooperate, the limitation of the prior art is broken through, and the problem that exercise prescription health screening is inaccurate due to the fact that physiological signals are easily disturbed by behaviors of a user when collected at the neck part is solved.

According to an embodiment of the present invention, a method for prescribing a customized shoulder rehabilitation exercise on the basis of an IoT-based digital dynamometer is a method in which a healthcare application executed by at least one processor of a terminal provides a customized exercise prescription service. The method comprises the steps of: performing initial setting by acquiring basic gripping power and a basic angle at a time point before surgery; acquiring first gripping power and a first angle at a first time point after surgery; generating a first exercise program on the basis of the acquired basic gripping power and first gripping power, and the basic angle and the first angle; acquiring a first exercise dataset for the first exercise program at a second time point which is a prescribed time point after starting to perform the first exercise program; generating exercise result content for the acquired first exercise dataset; and generating a second exercise program in which the construction of the first exercise program is changed on the basis of the acquired first exercise dataset.

The invention discloses a research method for improving depression by influence of movement on hippocampus, and relates to the field of medical science. Comprising the following steps: grouping experimental animals and constructing a model, selecting animal groups, and establishing a CUMS depression model; performing swimming exercise intervention, setting training parameters, and performing intervention by using an inhibitor; sample collection and molecular detection: anesthetizing animals, taking hippocampus tissues, and carrying out ferroptosis index detection, protein and gene expression analysis and neuron morphology observation on the hippocampus tissues; data analysis and mechanism verification are carried out, and behavioristics and molecular index correlation analysis and path blocking experiments are carried out respectively. According to the invention, the ferroptosis mechanism is associated with the motion anti-depression effect for the first time, the hub effect of the Nrf2 / SLC7A11 / GPX4 pathway is disclosed, behavioristics, molecular biology and ultrastructure analysis are integrated, a multi-dimensional verification system is established, quantifiable motion intervention parameters are provided, and a basis is provided for formulating a clinical motion prescription.

1. The name of the design product: a graphical user interface for prescribing exercise for pregnant women for electronic devices. 2. The use of the design product: for an electronic device. 3. The design features of the design product: the content of the graphical user interface. 4. The picture or photo that best shows the design features: front view. 5. The use of the graphical user interface: the front view is the main interface for prescribing exercise for pregnant women, clicking the "doctor's order" in the menu bar on the left side of the front view enters the interface change state diagram 1, clicking the "add doctor's order" in the lower left corner of the interface change state diagram 1 enters the interface change state diagram 2, clicking the triangle symbol in the box in the upper left corner of the interface change state diagram 2 enters the interface change state diagram 3, selecting the patient "Zhang Li" in the interface change state diagram 3 enters the interface change state diagram 4, clicking the plus sign in the upper right corner of the "exercise prescription" in the interface change state diagram 4 enters the interface change state diagram 5, checking the "dynamic electrocardiogram" in the lower right corner of the interface change state diagram 5 enters the interface change state diagram 6, checking the "oximeter" in the lower right corner of the interface change state diagram 6 enters the interface change state diagram 7, clicking "lying power car" in the interface change state diagram 7 enters the interface change state diagram 8, clicking the point on the coordinate axis on the left side of the interface change state diagram 8 enters the interface change state diagram 9, clicking "confirm" below the interface change state diagram 9 enters the interface change state diagram 10, clicking "save" below the interface change state diagram 10 enters the interface change state diagram 11, and clicking "OK" in the interface change state diagram 11 enters the interface change state diagram 12.

The invention discloses a multi-modal heart rate index-based exercise state monitoring and exercise prescription adjusting method, which comprises the following steps of: preprocessing multi-modal heart rate index data of a user, and calculating a data quality score based on a signal-to-noise ratio and an exercise artifact degree of the processed heart rate index data; extracting a heart rate mean value, a heart rate variability index and a heart rate recovery index from the processed heart rate index data, inputting the heart rate mean value, the heart rate variability index and the heart rate recovery index into an individualized adaptive model, and determining a physiological load index and risk classification; calculating a recovery prediction window according to the heart rate recovery index and the historical recovery data; and generating an exercise prescription adjustment instruction based on the data quality score, the physiological load index, the risk classification and the recovery prediction window, and dynamically adjusting the exercise intensity. The problems that the traditional technology is difficult to adapt to individual differences and dynamic changes, is easily interfered by motion artifacts, lacks multi-modal fusion and is rough in feedback mechanism are solved, and the motion state monitoring accuracy and the refinement level of motion prescription adjustment are improved.

The invention relates to the technical field of exercise prescription recommendation, in particular to an intelligent exercise prescription recommendation system and method for different chronic disease patients, and the system comprises a data collection module, a data preprocessing module, a dynamic disease interaction module, a real-time closed-loop risk monitoring module, a patient feedback and long-term optimization module and a central control management module. Through a multi-source data fusion mode, exercise prescription recommendation can be conveniently carried out on patients suffering from various chronic diseases, the reasonability of a recommended scheme is improved, then the adoption rate of the scheme is improved, through a real-time dynamic risk management and control mode, the exercise intensity and the exercise time of the patients are adjusted, the exercise safety of the patients is guaranteed, and the exercise efficiency of the patients is improved. Through a personalized feedback mechanism, the recommendation precision of the system for the patient each time is improved, long-term exercise prescription recommendation is provided for the patient, and the treatment effect of the patient is improved.