110 results about "Heart rate change" patented technology

The invention discloses a sleep monitoring analysis method and system based on HRV, and relates to the technical field of sleep monitoring, and the method comprises the following steps: at the beginning of monitoring, continuously recording a continuous heartbeat time interval of a sleeper at a preset initial heartbeat interval acquisition frequency for evaluating the autonomic nervous system state of the sleeper at night; the respiration data of the sleeper is acquired in real time while the heart rate data is acquired. By combining real-time respiration data and an intelligent increment control algorithm, when irregular respiration is detected, the heartbeat interval acquisition frequency is dynamically adjusted, so that the sampling precision of heart rate change is improved, fine fluctuation of a sleeper when apnea or irregular respiration occurs can be captured, and the sleep quality of the sleeper can be improved. In addition, the detection accuracy of the apnea event can be improved under high sensitivity, potential health risks can be recognized in time, more reliable data support is provided for improvement of sleep quality and early intervention, and therefore health damage caused by missing detection is effectively avoided.

The invention discloses a postoperative physical sign monitoring system based on medical information processing. The postoperative physical sign monitoring system comprises a data acquisition module, a physical sign feature extraction module, a postoperative physical sign evaluation model establishment module and a postoperative physical sign monitoring module. The invention belongs to the field of medical information processing, and particularly relates to a postoperative physical sign monitoring system based on medical information processing. Quantifying the total change trend of the heart rate fluctuation in the monitoring time; the quantitative basis is provided for the postoperative recovery process by integrating the blood pressure and heart rate changes; the sign monitoring effect is improved; focusing on dynamic adjustment through a time weight function, a recovery stage weight and an environment correction factor in a postoperative key time period; deviation weights are introduced for fluctuations of heart rates and blood pressures, loss coefficients are dynamically adjusted, segmented loss functions are designed according to physical sign values, conditions of different risk intervals are processed respectively, and the method adapts to uncertainty of individual differences and physical sign fluctuations of postoperative patients; and thus, the accuracy of post-operation sign monitoring is improved.

The invention relates to the technical field of electrocardiogram monitoring, in particular to an electrocardiogram monitoring method and system based on heart rehabilitation data. The method comprises the following steps that electrocardiosignals in different motion states are collected through a multi-lead electrocardiograph and motion monitoring equipment, and fatigue electrocardio mapping is carried out to obtain fatigue signals. Then, heart rate changes of the fatigue electrocardiogram mapping signals are analyzed, the disordered scatter point trend of the heart rate is quantified, dynamic inflection point distribution is fitted, and trend dynamic data are obtained; multi-scale rule intersection analysis is carried out based on the data, a fatigue heart rate change rule is extracted, and rehabilitation deviation quantitative evaluation is further carried out. Finally, the rehabilitation deviation evaluation result is sent to the terminal for real-time electrocardiogram monitoring, and heart rehabilitation management is supported. According to the method, the electrocardiogram monitoring technology is optimized, so that the electrocardiogram monitoring technology is more perfect.

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 invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention provides an old people body health early diagnosis and risk assessment system based on artificial intelligence, and relates to the technical field of old people health monitoring and assessment. Old people health parameters are collected in real time according to a set time interval, and a statistical method is used to calculate a mean value and a standard deviation of cardiovascular parameters; then, based on acceleration data, heart rate change, blood pressure fluctuation and position change information, sleep, movement and resting states of the old people are judged, and the day and night blood pressure change rate, the sleep state heart rate standard deviation, the movement state pulse wave conduction speed change quantity and the resting state heart stroke output average value are calculated; four risk assessment formulas of blood pressure risk, heart rate risk, pulse wave conduction velocity risk and heart stroke output quantity are constructed according to the data, a comprehensive health risk value is calculated through weighted merging, and the health state of the old people is judged to be healthy, medium-low risk or high risk according to a set threshold value.

The present application provides a heart rate detection method and electronic device, which relate to the field of communication technology. Through this solution, when it is detected that a user wearing a smart wearable device is performing a first exercise, a PPG signal is collected by a PPG sensor in the smart wearable device; based on the PPG signal and the first deep sequence neural network model, the first heart rate data is obtained; based on the PPG signal and the first frequency tracking algorithm model, the second heart rate data is obtained; based on the first heart rate data and the second heart rate data, data fusion is performed to obtain the user's target heart rate value during the first exercise. Since the frequency tracking algorithm can quickly track changes in heart rate, it can compensate for scenarios where the deep sequence neural network cannot track in time when the heart rate suddenly changes. Therefore, the present application solution can improve the accuracy of heart rate prediction by fusing heart rate data obtained by different models.

The invention discloses a data acquisition and management system for autism spectrum disorder child information, which comprises a puzzle game unit, a data acquisition unit, a data processing unit, a remote monitoring unit, a network transmission unit and a scheme customization unit, and belongs to the technical field of autism spectrum disorder. Intelligence game interaction is combined with various technical means of angle acquisition, sound wave acquisition, heart rate acquisition and response acquisition; comprehensive and objective data acquisition of expressions (such as correct rate and attention concentration degree), emotional fluctuation (through sound wave amplitude intensity), physiological status (such as heart rate change) and social reaction (such as response value) of children in the puzzle game is realized, and furthermore, the acquired data is scientifically analyzed through an algorithm formula, so that the data acquisition accuracy is improved. The accuracy and objectivity of evaluation are greatly improved, and multi-dimensional and all-around evaluation of the illness state of the child is achieved.

The invention is suitable for the technical field of intelligent exercise monitoring, and provides an artificial intelligence-based exercise monitoring management method and system, and the method comprises the steps: obtaining a training reference video and a training process video of a trainer, analyzing whether the trainer has a target exercise action with the deviation degree exceeding a preset threshold value or not through combining the training reference video and the training process video, and if the target exercise action exists, executing the step 2; a training intensity index for the target athletic action is determined. According to the method, the training reference video and the training process video of the trainee are obtained, the deviation between the movement action and the standard action of the trainee is analyzed in combination with the artificial intelligence technology, the physical performance and heart rate change of the trainee are monitored in real time, and therefore the personalized training intensity index is generated.

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

The invention discloses a personalized arrhythmia radiofrequency ablation system based on intelligent data analysis, and the system comprises a pressure stimulation module which is configured to apply periodic mechanical pressure stimulation to a specific anatomical site of a heart through an ablation catheter, the contact force range of the pressure stimulation is 5-10 g, and the frequency is 1-3 Hz; the autonomic nerve state recognition module is used for collecting heart rate change data of the patient after pressure stimulation in real time, and when the absolute value of the heart rate change exceeds 15% of a base line, it is judged that the patient enters an autonomic nerve sensitive window phase; the dynamic ablation strategy generation module is used for dynamically adjusting a radio frequency energy release mode and an ablation target priority according to the autonomic nerve sensitive window phase type, the sympathetic dominant type or the parasympathetic dominant type; according to the invention, by taking'neural interference conversion-dynamic energy adaptation-intelligent marking verification 'as a core, all-round breakthrough is realized from technical principle, clinical effect to application cost, and an accurate, safe and popular innovative scheme is provided for arrhythmia treatment.

The invention discloses an emergency nursing system capable of dynamically adjusting priority, which belongs to the technical field of emergency nursing and comprises the following steps: acquiring heart rate detection data, blood pressure detection data and oxyhemoglobin saturation detection data of a patient at preset time intervals, and filtering; setting a sliding time window, and respectively calculating a heart rate change rate, a blood pressure change rate and an oxyhemoglobin saturation change rate for the filtered heart rate detection data, the filtered blood pressure detection data and the filtered oxyhemoglobin saturation detection data in the sliding time window; correlation analysis is conducted on the heart rate change rate, the blood pressure change rate and the blood oxygen saturation degree change rate, parameter correlation characteristics are obtained, and a comprehensive parameter deterioration index is calculated; the comprehensive parameter deterioration index is compared with a preset threshold value, and whether a priority improvement mechanism is triggered or not is judged; and executing the priority lifting mechanism. By establishing a dynamic monitoring mechanism based on the change rate of the physiological parameters, real-time capture and quantitative analysis of the change trend of the illness state of the patient are realized.

The present invention relates to the field of image recognition technology, and more specifically to a method, system and storage medium for analyzing aerobics training movements. The method comprises: step S1: obtaining a user's first movement video in real time and monitoring the user's heart rate; step S2: obtaining a second movement video, periodically obtaining sub-movement frames from the second movement video, and extracting a first joint trajectory, comparing it with a reference joint trajectory, obtaining a first comparison result, and obtaining a sub-movement trajectory according to the first comparison result, the second joint trajectory and the third joint trajectory; step S3: comparing the line changes of key points with the reference lines of key points, and obtaining a second comparison result; step S4: estimating the first heart rate change of each target sub-section in the sub-movement trajectory and comparing it with the second heart rate change, and obtaining a third comparison result. The present invention solves the problem of inaccurate analysis results of aerobics training movements and improves the accuracy of the analysis results of aerobics training movements.

The invention relates to the technical field of resistance adjustment, in particular to a multifunctional spinning and a multifunctional spinning system, and the system comprises a pedaling frequency recognition module, a state judgment module, a stage control module, a load adjustment module and an output response module. According to the invention, through continuous analysis of the pedaling frequency and time information, accurate recognition of the rhythm fluctuation state, and combination of the linkage change of the flywheel rotation speed and the pedal pressure, the dynamic judgment of the motion stability and the matching mechanism of the training time and stage setting are realized; a resistance adjusting strategy is deduced according to the relation between flywheel acceleration and pedal pressure, the sensitivity and precision of regulation and control response are enhanced, the load weight is set by fusing the heart rate change trend, the resistance output is more matched with the individual physiological state, the load rhythm matching degree is optimized, and the training efficiency is improved. And the rhythm following capability, the resistance adjustment rationality and the training individuation level are integrally improved.

The invention relates to the technical field of artificial intelligence, in particular to a drowning early warning method and device based on a neural network, a medium and a program product.The method comprises the steps that the sampled heart rate and the sampled blood oxygen of a user in the latest detection period are obtained, the sampled heart rate and the sampled blood oxygen are preprocessed, and a heart rate and blood oxygen time sequence of the user is obtained; inputting the heart rate and blood oxygen time sequence into a heart rate and blood oxygen prediction model to obtain a prediction data sequence of the user in the current detection period; the predicted data sequence comprises a predicted heart rate change sequence and a predicted blood oxygen acceleration sequence; the heart rate and blood oxygen prediction model is obtained based on heart rate and blood oxygen time sequence training of a user under multiple different environment data and motion states; a heart rate and blood oxygen data sequence of the user in the current detection period is obtained, the heart rate and blood oxygen data sequence is compared with the prediction data sequence, and the drowning risk is determined; according to the invention, personalized judgment of the drowning risk can be realized, and drowning accidents can be effectively prevented.

The invention discloses a children propofol anesthesia depth monitoring method based on permutation entropy and heart rate change, and belongs to the technical field of anesthesia depth monitoring, and the method comprises the following steps: extracting a children electroencephalogram, and carrying out preprocessing and spectral analysis on the children electroencephalogram to obtain permutation entropy; child heart rate data are extracted and processed to obtain the heart rate change degree; inputting the permutation entropy, the heart rate change degree and the age into the mixed prediction model to obtain a composite index, and indicating the narcotic depth of the propofol for the children. According to the children propofol anesthesia depth monitoring method based on the permutation entropy and the heart rate change, the mixed prediction model is constructed by combining the electroencephalogram permutation entropy, the heart rate change degree and the child patient age, and higher accuracy, real-time performance and reliability are shown in children propofol anesthesia depth monitoring; the anesthesia state change can be quickly responded, and the problem of misjudgment caused by age dependence and insufficient linear processing in the prior art is effectively solved.

The invention relates to the technical field of data processing, and provides a heart rate data analysis system and method.The heart rate data analysis method comprises the steps that heart rate monitoring signals are collected through wearable heart rate monitoring equipment, and motion acceleration data at all moments are recorded; analyzing the discrete and differential performance of the central rate monitoring data in the monitoring time period, and quantifying the heart rate change intense index in the monitoring time period; obtaining a motion transformation index of the monitoring time period based on the discrete representation of the motion acceleration data in the monitoring time period; obtaining the interference source possibility of each independent component signal; screening a plurality of interference source component signals according to the interference source possibility; obtaining the frequency decoupling degree of each interference source component signal; obtaining a preprocessing demand degree of each interference source component signal; and performing smoothing based on the preprocessing demand degree, performing reconstruction to obtain a processed heart rate monitoring signal, and performing data analysis. The invention aims to solve the problem that heart rate data monitoring is influenced by a motion mode to generate motion artifacts.

The invention relates to the field of rehabilitation monitoring, in particular to an intelligent monitoring device for exercise rehabilitation of chronic diseases, which comprises a box body with a top opening and a controller, a water cavity used for containing liquid is formed in the box body, a running function assembly is arranged at the bottom of the water cavity, and the running function assembly is used for enabling a patient to run or walk underwater after being started; a water body fluctuation sensor is arranged in the water cavity and used for detecting water body fluctuation caused by underwater running of the patient; the controller is used for judging and recording the exercise intensity of the patient according to the water body fluctuation amplitude detected by the water body fluctuation sensor, and the water body fluctuation amplitude is in positive correlation with the exercise intensity of the patient. According to the technical scheme, whether the patient enters the traveling state or not is judged by detecting the heart rate change of the patient, and the exercise intensity of the patient is analyzed through the water fluctuation amplitude.

The invention discloses a temperature adjusting method and system for moxibustion equipment controlled by an intelligent chip, and the method comprises the steps: firstly, collecting a heart rate sequence of a user during moxibustion through the intelligent chip, decomposing a long-term trend item and a short-term fluctuation item, and calculating the instantaneous heart rate change; thirdly, a causal model of the instant heart rate change and a temperature sensitivity variable is built according to the instant heart rate change; secondly, extracting historical moxibustion temperature setting data from the equipment database, and analyzing temperature setting preferences of a user in different temperature sensitivity states; splitting a basic preset temperature and a fluctuation temperature regulation part based on preference characteristics, and determining fluctuation temperature regulation coefficients in different heart rate reaction modes according to heart rate changes; meanwhile, the preference and change of the user to the moxibustion part are analyzed in combination with historical use records; and finally, storing the personalized temperature adjustment data of the user in the intelligent chip, and updating the personalized temperature adjustment data in real time after the personalized temperature adjustment data is used each time. The method can effectively grasp personalized demands of users and improve the moxibustion experience and effect.

A cardiopulmonary management system includes a multi-sensor device and a controller. The multi-sensor device includes an elongated body having a first end and a second end, a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body, an electrical circuitry disposed near a middle of the body, and a sound sensor. The multi-sensor device is configured to be disposed on a skin of a subject and to measure cardiopulmonary parameters from the subject. The controller is configured to derive a score of sound change, heart rate change, thoracic impedance change, respiratory rate change, QRS wavelength change, QT interval change, electromechanical activation time change, and left ventricular systolic time change based on the measured cardiopulmonary parameters. The controller is further configured to control an operation of the system when the derived score is greater than a threshold.

The present invention relates to the field of image processing technology, and in particular to a pain grading method and system based on image processing. When grading pain for an object to be graded, the present invention selects a pain grading sub-model based on changes in the object's heart rate, thereby improving the efficiency of pain prediction. Simultaneously, in a second pain grading sub-model, to reduce the amount of multi-feature fusion computation, a method for constraining the generation of facial feature vectors using heart rate data is employed, based on the characteristic that heart rate can better indicate the state to be assessed, thereby improving the efficiency of pain grading. Furthermore, by adjusting the weight adjustment parameters of each organ, the changing trend of each feature weight can be flexibly controlled. Furthermore, the present invention can more accurately reflect the contribution of each feature to facial expression at different heart rate levels, thereby improving the generalization capability of pain grading.

The invention relates to a method, a device and equipment for measuring a QTc interval in a flat movement test process, a storage medium and a program product. The method comprises the steps of collecting electrocardiogram data according to a preset mode in a flat plate exercise test process; respectively determining a QRS wave group starting point and a T wave end point from the electrocardiogram data; determining a measured QT interval according to the QRS wave group starting point and the T wave end point; the measured QT interval is corrected for the first time through the time limit of the QRS wave group, and the corrected QT interval is obtained; automatically correcting the corrected QT interval for the second time according to the heart rate change of the testee in the tablet exercise test process to obtain a QTc interval; and drawing a real-time change trend chart of the QTc interval according to different stages of the flat plate motion test. Therefore, the QTc interval measurement of each stage in the plate motion test process can be automatically completed, the real-time measurement value of the QTc interval is obtained, the test efficiency is high, and the result is more accurate.

The present invention discloses a safety early warning monitoring method and system for live working process of a substation, which relates to the field of early warning technology of substations, and includes the following steps: obtaining the initial coordinate information of a mobile positioning device through a GPS satellite receiver; correcting the obtained coordinate information using a carrier phase integer ambiguity fixing algorithm to obtain the final coordinate information; using a sensor to detect signs of illness, and determining whether the substation where the final coordinate information is located is safe through vital sign monitoring, and issuing an early warning based on the alarm terminal device. The present invention determines whether the human body is within a safe range of signs through changes in the human heart rate and blood sample saturation, thereby determining whether the substation where the human body is located is dangerous, so that the alarm terminal device worn by the positioned human body can send a safety early warning signal through a main control unit in a timely manner, so that personnel can evacuate the substation in dangerous areas in a timely manner.

The invention discloses a non-contact real-time heart rate detection method and system based on rPPG and wavelet enhancement, and belongs to the technical field of medical health. Face video signals are collected through the camera, a sensor or an electrode does not need to be worn, real non-contact physiological signal detection is achieved, and the comfort level and use convenience of a user are improved; discrete wavelet transform denoising and enhancement processing is introduced on the basis of pulse signals extracted by a POS algorithm, and high-frequency noise caused by illumination variation, camera quantization errors and facial movement is removed through multi-scale decomposition and threshold filtering, so that the signal-to-noise ratio of the pulse signals is remarkably increased; the fast Fourier transform is combined with a sliding window mechanism, so that real-time updating and continuous calculation of the heart rate can be realized; the signal after wavelet enhancement is clearer in periodicity, so that the heart rate calculation error is remarkably reduced, a dynamic visual interface of a heart rate change curve and a pulse waveform is provided, a user can observe physiological state changes in real time, and good interactive experience is achieved.

The invention provides an intelligent data analysis method for a pension information service system, relates to the technical field of physiological data processing, and aims to judge behavior stages according to the daily moving speed of the elderly and the continuity of the speed in time, select stable effective behavior stages through deterministic scores, and improve the accuracy of the behavior stages. Judging a corresponding behavior state according to the change characteristic of the heart rate in the effective behavior stage, identifying the body function expression of the old person in the corresponding state according to the change rate characteristic of the central rate of each behavior state, and judging the reaction intensity in the first behavior state and the flat intensity in the second behavior state; and a heart function change index is constructed by combining the long-term comprehensive expression of the reaction intensity, the flat intensity and the heart rate in the third behavior state, so that the overall body function change characteristics are reflected and quantified, and a guardian can conveniently know the overall body condition change of the old.

The invention relates to the technical field of home rehabilitation nursing, and discloses an intelligent home nursing management method based on intelligent medical treatment, which comprises the following steps: acquiring related illness state and physiological data of a patient, automatically generating a rehabilitation model, judging the rehabilitation process of the patient, and when the rehabilitation process is far away from the estimated data, performing early warning and adjustment by a system in time. According to the door-to-door nursing intelligent management method based on intelligent medical treatment, the tolerance degree of a patient to pain is judged, rehabilitation recommendation is intelligently performed, a single-day change curve is generated according to heart rate changes of the patient under different rehabilitation items and drug treatment, the rehabilitation process of the patient is judged, and when the rehabilitation process of the patient is greatly different from an estimated model, the patient is reminded to go to the patient. The system can timely prompt an attending physician of the patient to adjust rehabilitation items, facilitates rehabilitation of the patient and work of nursing personnel, improves safety and effectiveness of rehabilitation of the patient, promotes development of personalized medical treatment, improves medical service quality, reduces unnecessary medical cost, and improves life quality of the patient.

The present disclosure provides an information processing device, alcohol intake estimation device, method for calculating an indicator for estimating alcohol intake, alcohol intake estimation method, biological examination method, biological examination device, estimation device, learning model, estimation method, learning model generation method, and computer program. The information processing device comprises a processing unit that acquires a total heart rate change representing the total sum of heart rate changes, which are amounts by which a subject's heart rate has changed. The processing unit calculates a first indicator for estimating alcohol intake, said indicator representing the total heart rate change in a state in which the subject is inactive and a state in which the heart rate of the subject exceeds a reference heart rate.

The invention relates to the technical field of health monitoring, in particular to a heart rate monitoring method and system based on deep learning. The method comprises the steps of obtaining multi-modal physiological data collected by sensor equipment worn by a user, generating a multi-modal physiological data sequence, and preprocessing the multi-modal physiological data sequence to obtain a preprocessed data sequence; extracting time sequence features of the data to form a training data set, training the training data set through LSTM and GRU deep learning models, and learning a heart rate change mode in a resting and motion state transition period to obtain a pre-trained heart rate fluctuation mode recognition model; inputting a heart rate signal acquired in real time into the pre-training model, and generating a detection result in combination with the motion state data; and on the basis of a detection result, abnormal fluctuation is evaluated in combination with a rule engine, and personalized health feedback is generated. The accuracy of heart rate fluctuation recognition is improved, personalized health management and real-time feedback are supported, and the method is suitable for health monitoring requirements of complex multiple scenes.

The invention relates to a method and system for monitoring the state of a person in the exercise process, and relates to the field of exercise monitoring, and the method comprises the steps: obtaining body fat data of a user before exercise, image information of the user during exercise, and a heart rate change diagram and a blood pressure value of the user during exercise, and determining a body type score of the user based on the body fat data and the image information, and performing feature recognition on the image information to obtain a motion posture of the user, determining a motion score of the user based on the body type score, the motion posture, the heart rate change diagram and the blood pressure value, and when the motion score is lower than a preset score threshold, outputting alarm information and stopping continuous motion. The method and the device have the effect of timely and accurately predicting fatigue or accidents of the hypertension user in the running process.

The invention relates to the technical field of data processing, in particular to a preoperative anxiety relieving method and system based on the virtual reality technology, and the method comprises the steps: obtaining preoperative physiological rhythm data of a patient, the physiological rhythm data at least comprising respiratory frequency and heart rate change information; constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, wherein the virtual natural scene comprises light and shadow fluctuation elements corresponding to the respiratory frequency; a patient wears a VR head-mounted display and a bone conduction earphone, the equipment automatically loads a customized virtual scene, breathing key points are prompted in real time through virtual guidance, and the patient is helped to establish perception association of rhythm elements; the physiological data of the patient is analyzed in real time to obtain the respiratory rate, the heart rate variability and the skin electrical activity value, and when the difference value between the light and shadow fluctuation frequency and the respiratory rate exceeds a threshold value, the light and shadow frequency is adjusted step by step to approach the respiratory rate. According to the multi-dimensional evaluation system, the anxiety state of the patient is reflected more comprehensively, and insufficient intervention caused by evaluation deviation is avoided.