76 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 provides a peripheral artery disease patient rehabilitation management system based on a whole process, and relates to the technical field of peripheral artery disease patient rehabilitation management. The system comprises a patient screening and risk grading module, a rehabilitation evaluation module, an exercise prescription generation and dynamic adjustment module, an intelligent propaganda and education content pushing module and a quality control closed-loop management module. PAD patients are accurately screened through a medical data interface and an NLP technology, and the risk levels of the patients are dynamically evaluated in combination with an intelligent risk grading system; generating a personalized exercise prescription according to the evaluation report, complication and biological monitoring data, and guaranteeing the exercise safety through a dynamic threshold model and collaborative early warning; dynamic matching and timeliness pushing of propaganda and education contents are realized by using an improved KL divergence algorithm; and full-process quality closed-loop management is realized through the quality control closed-loop management module. The precision and safety of rehabilitation management of the PAD patient can be improved, medical resource configuration is optimized, and rehabilitation practice driven by full-process data is assisted.

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 provides a motion rehabilitation method, device and equipment for movement fear of a cardiovascular disease combined weak patient. The method comprises the following steps: S101, establishing different potential category influence factor models of movement fear of the cardiovascular disease combined weak patient on the basis of a potential category analysis method; s102, based on a knowledge graph technology, aggregating related evidence-based evidences of health education and exercise rehabilitation of the movement fear of the senile cardiovascular disease and weak patient, and constructing to obtain a knowledge graph-based exercise rehabilitation knowledge base of the movement fear of the senile cardiovascular disease and weak patient; s103, in combination with the potential category influence factor model and the multi-modal data of the patient, constructing a multi-modal data-based digital twinborn body of the senile cardiovascular disease combined with the dynamic terrorism of the weak patient; and S104, according to the dynamically updated digital twinborn model, performing matching from the exercise rehabilitation knowledge base according to the health target, the body condition, the real-time environment and the preference characteristics of the patient to obtain personalized health education and exercise prescription recommendation.

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 provides a method and system capable of adjusting an exercise prescription in real time for a metabolic disease group, and the method comprises the following steps: obtaining the body data of a user, and determining the exercise risk level of the user through a metabolic disease risk assessment model according to the body data of the user; acquiring activity data of the user, and performing quantitative analysis according to the activity data of the user to obtain a physical activity level score of the user; obtaining function data of the user, and determining the motor function level of the user according to the function data of the user; matching an exercise prescription for the user according to the exercise risk level, the physical activity level score and the exercise function level of the user; monitoring data of the user in the exercise process according to the exercise prescription are obtained, and the exercise prescription of the user is adjusted according to the adjustment rule base. The corresponding exercise prescription is formulated according to the actual situation of the user, so that the formulated exercise prescription is more suitable for the user, the adjustment mechanism of the exercise prescription is perfected, and the actual body change of the user in the exercise process is accurately adapted.

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.

The invention discloses a method, system, medium and product for dynamically generating an exercise prescription, and relates to the field of exercise prescription generation. The method comprises the steps of generating an initial exercise prescription of a user through a machine learning model based on historical health records and real-time physiological parameters of the user; determining target motion scene information of a target motion scene of the user according to the current geographical location information of the user; determining target fitness equipment owned by the target exercise scene based on the target exercise scene information; and adjusting the initial exercise prescription according to the target exercise scene information and the target fitness equipment of the user to obtain a current exercise prescription. By implementing the technical scheme provided by the invention, the adaptation degree of the generated exercise prescription and the exercise scene is improved.

The invention discloses a method and a device for generating a medical exercise prescription, electronic equipment and storage equipment, the method and the device are applied to the electronic equipment, specifically, in response to an information input request of a user, user data of the user are received, and the user data comprise body data, exercise habit data and / or health state data; modeling and analyzing the user data by using a machine learning algorithm and a data mining technology to obtain the physical condition and exercise ability of the user; and processing the physical condition and / or the exercise ability by using a professional exercise prescription generation algorithm to obtain and output a medical exercise prescription adapted to the user. Therefore, the scheme does not need field evaluation and subjective judgment of doctors in the process of obtaining and outputting the medical exercise prescription, so that the evaluation basis is more objective and scientific, and the accuracy of the medical exercise prescription is improved.

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.

The invention discloses a system and a method for automatically generating an exercise prescription guide scheme and implementing intelligent guide. A platform administrator uploads exercise prescription data to the system through an operation workbench, so that corresponding exercise prescriptions are generated, and the exercise prescriptions comprise exercise action guide schemes and exercise prescription knowledge bases. When a doctor inquires, the closest exercise prescription can be called from the established exercise prescription knowledge base through the exercise prescription knowledge engine, so that the doctor can quickly make a diagnosis. When a user executes an exercise prescription, the user completes actions by referring to a guiding scheme, the system detects and identifies human body key points of the user in the exercise process through a human body key point identification engine, the human body key points serve as a scoring basis, and each action of the user is correspondingly scored through an action scoring engine. After the exercise prescription is finished, an evaluation report is generated according to the score, and corresponding exercise suggestions and health suggestions are fed back to the user, so that improvement can be performed according to the score and the suggestions when the exercise prescription is executed next time.

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 relates to the technical field of adjusting systems, in particular to an anterior cruciate ligament reconstruction exercise rehabilitation control adjusting system and a use method. The problems that according to an existing rehabilitation exercise method design, individualized rehabilitation is difficult to achieve, rehabilitation supervision on different stages before and after scientific, long-term, regular and systematic reconstruction is lacked, and the function of suggesting and formulating a next exercise prescription according to the training condition cannot be achieved are mainly solved. According to the technical scheme, the device comprises a fixedly-arranged base, a cavity is formed in the base, an arc-shaped plate is installed at the position of the cavity, and multiple sets of balls are installed on the arc-shaped plate; and the balance ball is mounted in the arc-shaped plate, the balance ball is arranged in a hemispherical shape, and the balance ball is used for carrying out balance training. The system has the advantages of being more personalized, comprehensive, systematized and scientific, personalized rehabilitation training can be automatically adjusted according to different stages of different patients, and meanwhile the effects of evaluation and guidance are achieved.

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 invention provides a method, device, smart wearable device and medium for generating an exercise plan, which are applied to smart wearable devices, including: obtaining user health information uploaded by a user through a questionnaire, and obtaining the physical fitness index obtained by the user through a health and physical fitness test; generating the user's exercise prescription based on the FITT-VP principle, the user's health information and the physical fitness index; generating an exercise plan based on the physical fitness index and the exercise prescription, and obtaining the user's exercise data in real time during the user's training process, and sending feedback information to the user based on the exercise data, so that the user can dynamically adjust the exercise plan based on the feedback information. The present invention can recommend an exercise plan to the user more scientifically, thereby improving the training effect.

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.