437 results about "Exercise intensity" patented technology

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention relates to the technical field of physical education teaching, in particular to an interactive physical education teaching method based on a large model, and aims to realize real-time monitoring and personalized training guidance of the motion state of a student through an advanced sensor technology and a machine learning model. The method comprises the following steps: firstly, collecting motion data of a student in real time through a camera and a depth sensor, including motion posture, speed, response time, heart rate and other information; then, the collected data are input into a pre-trained large model, the model comprises a convolutional neural network, a support vector machine and a random forest model which are respectively used for evaluating the action standard, the exercise intensity and the fatigue degree of students, the large model automatically generates personalized training items, and the exercise postures of the students are captured in real time; according to the motion deviation correcting system and method, by comprehensively applying various technical means, the scientificity and individuation level of physical education are improved, and the motion deviation correcting system and method have wide application prospects.

The invention relates to a muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals, which comprises the following steps of: (1) acquiring the surface electromyogram signals of a measured muscle area by using a non-invasive sensor, and acquiring muscle mechanics parameters at the same time; (2) carrying out filtering, amplification and noise reduction preprocessing on the collected surface electromyogram signals to eliminate interference and enhance signal quality; and (3) extracting multi-dimensional features from the preprocessed surface electromyogram signals and muscle mechanics parameters, establishing a frequency domain data set by using the feature values, and calibrating the muscle mechanics parameters. The method has the advantage that the prediction accuracy and reliability are enhanced. Muscle strength and muscle fatigue states of different individuals can be reflected more accurately, and accurate prediction of individual differences is realized. The adopted surface electromyogram signal acquisition mode is non-invasive, and a plug-in electrode is not needed, so that the test discomfort is reduced, and the acceptability and participation degree of a testee are increased; by establishing an individual model and combining with real-time signal acquisition, real-time monitoring and prediction of muscle strength and muscle fatigue can be realized, and a user can obtain feedback of a muscle state at any time, so that the exercise intensity, posture and training plan are adjusted, and the training effect is optimized.

The invention relates to a human motion data processing method, and belongs to the technical field of data processing. Comprising the following steps: controlling a microwave radar to emit a detection signal, and detecting a heart rate signal and a respiration signal of a human body; performing clustering analysis on the data according to the collected heart rate and respiratory rate, and extracting heart rate, respiratory rate change, duration and exercise intensity characteristics; according to the extracted heart rate change, duration, exercise intensity and other characteristics, the exercise stage is recognized, and according to the neural network model library, an energy consumption result is output in combination with data analysis. According to the human motion data processing method provided by the invention, the microwave radar is combined with the intelligent wearable device, so that the detection precision of heart rate and respiration signals is remarkably improved, multi-modal data fusion is realized, personalized heart rate prediction is performed, motion stage division is optimized in combination with acceleration data, and the accuracy of motion data processing is improved. The accuracy of motion mode recognition is improved, and personalized motion suggestions can be provided for the user.

The exercise risk assessment method of the present invention includes the steps of acquiring historical electrocardiogram signals of an athlete awaiting evaluation at the exercise intensity at the time awaiting evaluation and at different previous exercise intensities; inputting the heart beat interval series for different time periods obtained based on the historical electrocardiogram signals into a fuzzy entropy model to calculate fuzzy entropy and obtain an exercise fuzzy entropy series; and inputting the time awaiting evaluation, the exercise fuzzy entropy series, and the exercise intensity into a series change point identification model to perform change point identification, thereby performing risk assessment based on the change point identification result obtained by inputting the time awaiting evaluation, the exercise fuzzy entropy series, and the exercise intensity into a series change point identification model, and a risk state transition model to obtain a risk assessment result at the time awaiting evaluation. [Effects] Not only can it reliably quantify the disorder and complexity of heart rate variability, but it can also analyze the nonlinear growth law of exercise load in real time, effectively quantify the relationship between exercise risk and exercise intensity, and further improve the reliability and accuracy of exercise risk assessment.

The invention belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation and control method for postoperative lung function rehabilitation training, which comprises the following steps: acquiring an electrocardiosignal, a respiration signal and a limb movement signal of a patient; identifying a heart rate fluctuation form of the electrocardiosignal to generate an abnormal fluctuation time period, and analyzing a time difference between an inspiration phase and an expiration phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiopulmonary risk level; the exercise intensity of rehabilitation training equipment is automatically adjusted based on the cardiopulmonary risk level, and meanwhile a regulation and control mode is dynamically selected according to the proportion change of the chest fluctuation amplitude and the abdomen fluctuation amplitude; when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value, gradually improving the exercise intensity; calculating a motion intensity correction coefficient and triggering a motion posture correction instruction; and generating a rating report containing the cardiopulmonary function fitness. According to the invention, the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art is solved.

The invention discloses a hospital obese patient intelligent weight loss dynamic matching intervention method and decision making system based on multi-modal data fusion, and relates to the field of medical health, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-modal data; s2, dynamic metabolism modeling and plateau period breakthrough; s3, generating a hierarchical intervention strategy; s4, performing psychological-physiological linkage intervention; and S5, performing real-time safety control and feedback optimization. By collecting multi-dimensional data, constructing the model, accurately predicting the metabolic rate, the metabolic plateau period and the sudden eating tendency, dynamically adjusting the exercise intensity and the diet scheme according to the conditions of different patients, triggering a low-intensity exercise mode for the obese patients with cardiac insufficiency and calibrating diet, ensuring the exercise safety, and improving the exercise efficiency. Operation suggestions are generated on the basis of the standard, precise preoperative evaluation is conducted, postoperative rehabilitation is optimized in combination with nerve stimulation data, dangerous postures are monitored, the sport injury risk is reduced, and personalized, safe and efficient intelligent weight reduction intervention for obese patients is achieved.

The invention belongs to the field of medical rehabilitation, and particularly discloses a diabetic foot ulcer patient non-load exercise rehabilitation exercise decision support system which comprises an evaluation grading module used for obtaining multi-mode ulcer data of a patient and scientifically grading diabetic foot ulcers of the patient in combination with a layering algorithm to obtain a grading result; the scheme generation module is used for matching a non-load exercise scheme suitable for the patient in a database according to the grading result and generating an initial exercise scheme; the exercise feedback module is used for sending the initial exercise scheme to a patient terminal, acquiring the exercise completion condition of the patient and performing personalized adjustment on the exercise intensity and frequency through a dynamic regulation and control mechanism; according to the method, static ulcer features and dynamic physiological features are combined through a multi-modal data fusion technology, automatic grading is realized based on an AI algorithm, and scientificity and consistency of grading evaluation are remarkably improved.

The invention provides a remote monitoring method and system applied to children's watches, and relates to the technical field of children's watches, time axis synchronous alignment is carried out on collected multi-source heterogeneous data through an interpolation method, data time sequence chaos caused by sampling rate differences is prevented, and then the data time sequence chaos caused by sampling rate differences is prevented. Through dynamic coupling calculation of a light intensity mutation gradient and a heart rate stress coefficient, the cooperative influence of environment change on the physiological state of the child is quantified in real time, and the physiological state of the child is determined by further combining power spectrum centroid frequency and physiological phase difference analysis of the motion rhythm and logarithmic correlation of acoustic envelope frequency and GPS speed. In addition, an acoustic propagation delay compensation mechanism is introduced, a two-factor dynamic equation model is constructed through a time delay cross term, and a dynamic risk threshold generation mechanism based on a noise energy ratio is combined, so that the dynamic risk threshold of the motion intensity, the rhythm characteristics and the acoustic scene is effectively distinguished. Effective remote monitoring on child safety in different scenes is realized.

The invention discloses an inducible muscle strength rehabilitation training method and device. The method comprises the steps of obtaining an electroencephalogram signal and an electromyographic signal of a patient, obtaining a first motion intention based on the electroencephalogram signal, obtaining a second motion intention based on the electromyographic signal, and determining a motion intention based on the first motion intention and the second motion intention; determining a fatigue level of the patient based on the electromyographic signal; based on the motion intention and the fatigue level, corresponding training modes are determined, and the training modes comprise an induction training mode and an on-demand auxiliary training mode. The motion intention of the patient is determined through fusion of the electroencephalogram signals and the electromyographic signals, and the accuracy and reliability of motion intention recognition are improved; based on the motion intention and the fatigue state of the patient, the corresponding training mode is determined for training, the patient can be induced to properly improve the motion intensity, accelerate the rehabilitation treatment process and improve the treatment effect, adaptive adjustment can be made in time when the state of the patient is poor, the patient is prevented from being injured, and the training safety guarantee is improved.

The invention relates to a method and device for determining a rope skipping recommendation plan based on a large model, and belongs to the technical field of training guidance. Determining a class exercise intensity score and an after-class exercise intensity score of the user; determining an after-class exercise quality score, a training effect score, a training stability score and a physique score; determining a physique similarity score, an exercise intensity similarity score and an exercise attitude similarity score; determining plan recommendation degree scores of the training plan of the user i in the current semester and the training plan of the user r in the current semester; according to the plan recommendation degree score, determining a training plan recommended by the semester; constructing a fine tuning data set, loading the large model, and finely tuning the large model to enable the large model to learn how to generate a rope skipping recommendation plan; and outputting a rope skipping recommendation plan through the large model. According to the method, the accurate rope skipping recommendation plan can be generated by fully utilizing the historical use data, and dynamic and personalized rope skipping guidance is provided for the user.

The invention relates to the technical field of health management and medical information, and discloses an all-dimensional intelligent cardiopulmonary health management system and method and electronic equipment, and the system comprises a biological sensing module which is used for continuously collecting key physiological indexes of the heart rate, the oxyhemoglobin saturation, the respiratory rate and the motion state of a user; the data processing module is used for storing the collected physiological data and historical exercise records of the user through a central database of a cloud computing platform; the deep learning analysis module is a prediction model built based on a deep learning framework and is used for analyzing the physiological data of the user and generating a personalized exercise plan; the real-time monitoring module is used for detecting the physiological state of the user and automatically adjusting the exercise intensity or type when the physiological state exceeds a preset threshold value. The technical scheme that the multi-mode biosensor is combined with the real-time monitoring module is adopted, and the technical effect of comprehensively monitoring the cardiopulmonary function state of the user in real time is achieved.

The invention discloses an upper limb rehabilitation training method and system based on a human body motion model, and relates to the technical field of rehabilitation training. Constructing a human body motion model and outputting simulation data; calculating a physiological index coefficient, and further calculating the exercise intensity of each training action; calculating a motion mode complexity index of each training action so as to obtain and calculate the training duration of each training action; and calculating a motion deviation value, and further calculating a comprehensive dynamic deviation index. Presetting a threshold value and judging whether the rehabilitation training plan is suitable for the current state of the patient; whether a rehabilitation training plan needs to be changed; according to the method, the suitability of the rehabilitation training plan can be evaluated in real time, and the training scheme is dynamically adjusted; the training is always fit with the condition of the patient, and the rehabilitation efficiency and success rate are improved.

The invention relates to the technical field of digital health, in particular to a personalized rehabilitation exercise method for rheumatoid arthritis patients based on a BCW theory, which comprises the following steps: acquiring joint angle change and calculating a pain delay response coefficient, detecting fluctuation of supporting force and stabilizing force to obtain a joint load tolerance value, analyzing respiratory coordination to generate a respiratory coordination index, and performing rehabilitation training on the respiratory coordination index. The method comprises the steps that a joint angle sensor and pain intensity data are analyzed, a BCW capacity factor weight value is generated in a weighted mode, a fuzzy control algorithm is input to conduct hierarchical mapping adjustment on action amplitude frequency and rest intervals, and exercise adaptability feedback data are generated through monitoring completion degree and pain feedback. The exercise intensity is dynamically adjusted in real time through a data model, a personalized rehabilitation scheme is automatically optimized, the problems that subjective feedback is relied on, and physiological changes cannot be responded in time are solved, more accurate exercise amount adjustment is provided, the situation that the joint burden is increased due to improper exercise intensity is avoided, and the rehabilitation process is effectively promoted.

The invention discloses a method for researching the influence of lower limb movement on the blood sugar of a type II diabetes patient, and relates to the technical field of medical health. Comprising the following steps: S1, screening research objects: selecting patients diagnosed as T2DM; s2, CPET evaluation: performing CPET on the patient by using a professional cardiopulmonary exercise test system, and measuring cardiopulmonary function indexes of the patient, including anaerobic threshold and maximum oxygen uptake; individualized exercise intensity is formulated according to the data; s3, diet control: ensuring that the patient takes standardized breakfast before testing so as to reduce the influence of food intake on blood sugar fluctuation. According to the method, the patient is assessed in detail through the cardiopulmonary exercise test, the oxygen-free threshold, the maximum oxygen uptake and other key indexes are obtained, and the exercise intensity and mode meeting individual requirements can be formulated; according to blood glucose data and cardiopulmonary function indexes monitored in real time, the exercise scheme is dynamically adjusted, and reliability and safety are improved.

The invention provides a hypertension VR intervention method based on dynamic biofeedback and individualized modeling, and provides a hypertension VR intervention system based on dynamic biofeedback and individualized modeling. The method comprises the following steps: performing state analysis on dynamic biological feedback indexes of a hypertensive patient, namely a predicted pressure index (SPI) and an exercise intensity index (EII), deciding a hypertension VR intervention decision matched with the state of the dynamic biological feedback indexes, activating a VR scene of the corresponding decision in a system, and actively enabling the hypertensive patient to perform adaptive intervention training through VR equipment. Therefore, dynamic biological feedback intervention is realized by utilizing an intelligent decision training mode of the intelligent model. According to the method, training mode recommendation can be accurately carried out according to the patient state, state reward can be carried out according to the blood pressure change amplitude and the patient compliance score, so that the patient training mode is optimized, intelligent training is achieved, and the method has high guiding significance.

The invention discloses a personalized precise fitness scheme intelligent pushing method and system, and relates to the technical field of intelligent fitness, and the method comprises the steps: collecting motion data of a user in real time, and carrying out the preprocessing; the method comprises the following steps: collecting a fitness target of a user, converting the fitness target into a target vector, activating a task sub-network in a dendritic neural network according to a matching degree between motion data of the user and the target vector, dynamically adjusting weights and importance of different tasks in the task sub-network through an attention mechanism, and generating and optimizing a preliminary fitness scheme through multiple agents. The most relevant sub-networks are activated in real time, task priorities are adjusted through an attention mechanism to adapt to environment changes and user states, the exercise intensity is optimized through fuzzy logic reasoning, training tasks are automatically adjusted according to real-time feedback of users, the balance between the tasks is optimized through co-evolution between agents, and the training efficiency is improved. The personalized fitness scheme can be continuously optimized, and more accurate exercise recommendation is provided under the dynamic environment and user demand changes.

A system for remote monitoring of a workout, including: a processor; a non-transient memory; an imaging sensor; and a display device, the non-transient memory having computer-readable instructions stored thereon which, when executed by the processor, perform steps including: capturing imagery of at least a face of a subject, estimating and monitoring a peripheral blood volume pulse (BVP) or heart rate from the imagery using remote photoplethysmography (rPPG), performing facial age estimation using computer vision to analyze the imagery of the face to estimate an age of the subject, estimating a maximum heart rate using at least the age of the subject, calculating a heart rate zones as a predetermined percentage of the maximum heart rate, and displaying an indication of a current heart rate zone based on a current BVP or heart rate.

The invention discloses an intelligent tracking method and system for the rehabilitation state process of an orthopedic patient, and relates to the technical field of medical health. By integrating the wearable device, vital signs, motion states and pressure distribution data of a patient are collected and analyzed in real time, the rehabilitation state of the patient can be accurately monitored, and dynamic progress evaluation is provided; based on a rehabilitation progress evaluation result, the system can automatically generate a personalized rehabilitation exercise scheme, exercise intensity, exercise frequency and a training plan are adjusted according to specific requirements of each patient, and high efficiency and safety of the rehabilitation process are ensured; through analysis of vital sign fluctuation, motion precision and plantar pressure balance, the system can identify potential risk factors in real time and trigger early warning in time to help a patient and nursing personnel thereof to take necessary intervention measures, and smooth proceeding of the rehabilitation process is ensured.

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 discloses an exercise management method and system for musculoskeletal chronic pain, and relates to the technical field of exercise management rehabilitation, and the method comprises the steps: building a biomechanical model for a patient through finite element analysis software, determining an optimal exercise mode according to the pain mode and the activity ability of the patient, and generating an exercise track through a path planning algorithm, on the basis of micro-current collaborative feedback data, the pain change trend of the patient is calculated, and a motion adjustment plan is generated, a biomechanical model can truly reflect the force distribution conditions of the patient under different motions, the safety of a motion scheme is remarkably improved, and the potential injury risk is reduced; scientific basis is provided for dynamically adjusting the exercise intensity, the obtained exercise adjusting plan can respond to the state change of the patient in time, and the intelligent level and the long-term curative effect of rehabilitation management are further enhanced.

The invention discloses a video follow-up exercise evaluation and exercise intensity monitoring method and system. The method comprises the following steps: acquiring basic information of a user; collecting the heart rate, expression, breathing and sweating conditions of the user during exercise; collecting a user motion video; obtaining a standard motion video; extracting a user action model of the user motion video and a standard action model in the standard action video; scoring the user action standard degree according to the relationship between the overall similarity and a threshold value; and performing real-time exercise intensity reminding on the user according to a comparison result of the real-time exercise intensity of the user and the exercise intensity threshold range. According to the method, comprehensive judgment is carried out by combining parameters such as heart rate, breath, expression and sweating conditions, so that the result is more accurate. In addition, in the setting of the exercise intensity threshold value, parameters such as age, gender, body weight and subjective willingness of the user are integrated, so that the alarm is more referential.

One or more embodiments of the invention provide a graph-to-video model training method and device, equipment and a storage medium, and the method comprises the steps: obtaining a first sample video, and extracting an image frame from the first sample video; acquiring multiple types of exercise intensity data of the first sample video obtained by performing exercise intensity evaluation on the first sample video by a trained exercise estimation model; inputting the image frames and the multiple types of exercise intensity data into a preset image-generated video model, and performing calculation by the image-generated video model based on the image frames and the multiple types of exercise intensity data to generate a corresponding video; and determining training loss based on the generated video, and determining to complete training for the map-to-video model after completing adjustment of model parameters for the map-to-video model according to the loss.

The invention discloses an anti-resistance exercise training system for old people based on intelligent wearable equipment, and relates to the technical field. The problems that in the prior art, a dynamic action monitoring error correction mechanism is lacked, exercise intensity adjustment is not personalized and real-time, interaction and data feedback functions do not exist, and equipment portability and wearing adaptability are insufficient are solved. The system collects data in real time through the whole body position intelligent wearable device, corrects non-standard actions through the action recognition feedback module, dynamically regulates the intensity through the exercise intensity evaluation module, reduces the risks of muscle strain, cardiovascular burden and the like, takes standardized action guidance, personalized intensity adaptation and data visualization as the core, ensures target muscle activation, and improves the training efficiency. The training effect is quantified, the operation is simple, the use threshold is reduced through voice guidance, the boring feeling is relieved through personalized plans and summary reports, the training achievement feeling and compliance of the old people are enhanced, the muscle strength can be improved through whole body position training, and the anti-resistance exercise requirement of the old people is met.

The invention provides a self-adaptive assistance system and method for adjusting an exoskeleton based on a sweat sensor, and the system comprises a sweat amount detection chip which is embedded into the surface of wearable flexible clothes, is in contact with the skin of a human body, and is used for converting the detected sweat amount into an electric signal, and further converting the electric signal into a control signal; the knapsack comprises a control system, a driving system and a sensing system, and is worn on the trunk of the human body after being connected with the wearable flexible clothing; the power assisting device comprises a power assisting boot which is worn on the foot of the human body and connected with the driving system through a Bowden cable; and the specific output value of the power assisting device is controlled through the control signal. Dynamic matching of exercise intensity and mechanical assistance is achieved through an amplification control mechanism driven by sweat amount, gait phase compensation fed back by plantar pressure is combined, the energy utilization efficiency is improved by 20% or above, the problem that a traditional exoskeleton physiological-physical interface is disjointed is effectively solved, and the energy utilization rate is increased by 20% or above. The assisting synchronism is improved, and meanwhile the wearing comfort degree and the motion freedom degree are remarkably improved.

The invention discloses a personalized health and care management method based on bioelectricity signal monitoring, and relates to the technical field of personalized health and care management, and the method specifically comprises the following steps: obtaining heart monitoring information of a heart disease patient in an exercise process in real time, analyzing the heart monitoring information after obtaining, and respectively generating a heart rate variability index and an exercise intensity load index; constructing a heart rate fluctuation evaluation model for the generated heart rate variability index and the exercise intensity load index, generating a heart rate fluctuation evaluation coefficient, analyzing and evaluating the heart rate fluctuation condition of the heart disease patient during exercise after the heart rate fluctuation evaluation coefficient is generated, and dividing the heart rate fluctuation condition into normal fluctuation, warning fluctuation and abnormal fluctuation according to the evaluation result; and respectively taking corresponding motion adjustment measures for the heart disease patients according to the division results of various fluctuations. The problem that abnormal heart rate fluctuation is difficult to monitor in real time during high-intensity exercise of the heart disease patient is solved, and the accurate and dynamic heart rate safety management effect is achieved.

The invention relates to the technical field of intelligent sports equipment, and provides a counting error correction and sports intensity dynamic grading method and system for an intelligent skipping rope. The method comprises the following steps: acquiring three-dimensional motion data in a rope skipping process; performing feature extraction on the three-dimensional motion data to obtain rope skipping motion features; identifying a target data segment in three-dimensional motion data through the rope skipping motion features; acquiring a time interval and displacement track repeatability of two adjacent actions in the target data segment, and when the time interval exceeds a normal rope skipping frequency range or the displacement track repeatability is lower than a preset threshold value, judging that the counting is invalid counting and removing; the abnormal rope skipping counting result is corrected; and in combination with basic information and rope skipping frequency, continuous exercise time, accumulated rope skipping times and heart rate data which are acquired in real time, calculating a current exercise intensity index so as to perform dynamic grading. According to the invention, more accurate and personalized exercise experience is provided for the user.

The utility model discloses a double-track damping web plate which comprises a first web plate, a second web plate and a hinge piece arranged between the first web plate and the second web plate. A plurality of sliding rails are arranged at the tops of the first health web plate and the second health web plate; a plurality of sliding blocks are arranged at the top of the first health web plate, and a plurality of sliding pieces are arranged at the bottoms of the sliding blocks; a plurality of sliding blocks are arranged at the top of a first web exercising plate, when a user supports arms on the sliding blocks for exercising, the sliding blocks slide along the sliding rails under the cooperation of the sliding rails, the connecting blocks, the pulleys and the damping ropes, each sliding block slides on multiple rails, and the stability during sliding exercising is improved; the high-elasticity damping rope has the damping effect, the user is helped to better control the force and the speed in the exercise process, and the user can pull the exercise device by himself according to the exercise intensity needed by the user so as to achieve the personalized exercise effect.

The invention relates to the technical field of rehabilitation treatment, and discloses a lumbar vertebra rehabilitation exercise adjusting method and system based on HAPA and a storage medium. The method comprises the following steps: setting an HAPA psychological scale electronic questionnaire on a patient terminal, collecting answer data, and standardizing to obtain an HAPA psychological index; meanwhile, the waist three-axis angular speed, the myoelectricity root-mean-square value and the voice pain score are collected, and the exercise tolerance index is obtained. And inputting the HAPA psychological index and the exercise tolerance index into an exercise intensity mapping model to obtain an exercise intensity grade, and calling out corresponding parameters from a rehabilitation action library to form a basic training scheme. The exercise intensity grade, the exercise tolerance index and the corresponding threshold value are judged, the exercise parameters of the basic training scheme are correspondingly adjusted, the adjusted parameters and the patient completion degree are transmitted to a cloud end, and model self-learning is achieved. Personalized, intelligent and self-adaptive adjustment of lumbar vertebra rehabilitation training is achieved, and the training tolerance and the rehabilitation effect of a patient are improved.

The invention discloses a method and a device for evaluating recommended supply flow of hypoxia gas, which are mainly used for supplying hypoxia air with proper concentration for a user to carry out hypoxia training. The method comprises the steps that the heart rate and the respiratory rate of a to-be-evaluated user are obtained; evaluating the exercise intensity condition of the user according to the heart rate and the respiratory rate; the heart rate variability rate and the blood oxygen concentration of the to-be-evaluated user are obtained; evaluating the adaptation degree of the user to the current altitude according to the heart rate variability and the blood oxygen concentration; and the low-oxygen gas supply flow is recommended according to the comprehensive adaptation degree condition. The low-oxygen gas is generated by recycling the exhaled gas, so that the cost is reduced, the weight is reduced, an intelligent decision-making and recommendation system is introduced, and training mode recommendation can be performed according to the personal condition of a user.