54 results about "Heart rate fluctuation" patented technology

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 medical care management, in particular to an intelligent mental disease risk assessment system which comprises a physiological rhythm extraction module, a rhythm alignment module, an interference analysis module, a branch recognition module and an assessment output module. According to the method, the sleep duration and the heart rate change are continuously recorded, the corresponding relation between the sleep rhythm and the heart rate fluctuation is constructed, a quantifiable mental rhythm parameter set is formed, dynamic mapping is established between sleep interruption and the heart rate fluctuation, and the stability of the individual physiological rhythm and the time structure of rhythm deviation are effectively revealed. Anchoring is performed based on a maximum offset day, a time contrast section is established around a specific time point, and differential features of variables in the section are finely extracted, so that micro rhythm disturbance is dynamically captured and measured. A coupling factor evaluation mechanism is constructed by using cross decision logic of sleep time history and heart rate amplitude, and the sensitivity and multi-dimensional analysis ability of rhythm change recognition are enhanced.

The invention discloses an intelligent data labeling method based on artificial intelligence, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining physiological parameter data, historical health parameter data and motion state data of a user; outputting motion state label data through a pre-constructed motion recognition model according to the motion state data; and performing sliding window analysis on the historical health parameter data, and dynamically establishing and updating long-term change data of the user health parameters. According to the scheme, whether the physiological parameter fluctuation is caused by the movement or not can be distinguished through associated retrieval of time window division and the movement state label, for example, when it is detected that the heart rate fluctuation of the user during running exceeds the threshold value, normal physiological response instead of abnormity can be judged in combination with the movement label; the probability that normal physiological parameter fluctuation caused by movement is mistakenly marked as abnormal can be effectively reduced, and meanwhile, the pertinence of anomaly detection is improved.

The invention provides a sleep-aiding medicine pillow dynamic regulation and control method and system based on physiological parameter analysis. According to the method, pressure data and sleep bio-electricity signals of a contact area of a user are collected in real time, a characteristic frequency band proportion and a heart rate fluctuation parameter are analyzed, and a correlation model is established; calculating a bioelectricity signal energy integral value of the characteristic frequency band based on the model, and quantizing to generate a bioelectricity intensity index; the heat conduction efficiency is changed by adjusting the molecular density of the temperature-sensitive material, and a contact layer heat transfer dynamic instruction is generated; pressure feedback and instruction waveform characteristics are combined, the micropore on-off duty cycle is adjusted, and the opening duration of unit time is controlled; the drug release rate is synchronously adjusted, so that the response waveform and the heat conduction instruction are dynamically balanced, and closed-loop regulation is formed. According to the application, through multi-parameter dynamic analysis and feedback adjustment, accurate cooperative control of heat conduction and medicine release of the medicine pillow is realized, and the matching degree of sleep-aiding substance release and sleep rhythm is optimized.

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 provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention relates to the technical field of infusion monitoring, in particular to a patient heart rate and infusion monitoring system based on wearable equipment, which comprises a physiological parameter acquisition module, a data trend analysis module, a risk judgment module, an abnormity early warning module and an equipment management module. According to the method, based on the blood oxygen change rate, the blood oxygen decrease trend is analyzed, the heart rate fluctuation condition is combined, dynamic evaluation of physiological parameters is achieved, the blood oxygen decrease rate, the heart rate mutation index and the mutation amplitude are integrated for comparison, the accuracy of infusion related risk judgment is improved, infusion abnormity is classified and managed by setting the risk level, and the accuracy of infusion risk judgment is improved. And corresponding emergency measures are taken in combination with preset rules, so that events of different risk levels can be effectively responded, intelligent analysis is performed on the infusion state in combination with the change trend of physiological parameters, infusion monitoring is not limited to flow velocity and liquid quantity levels, comprehensive judgment is performed in combination with the physiological status of a patient, and the accuracy of infusion monitoring is improved. And the comprehensiveness of abnormal recognition in the infusion process is improved.

The invention relates to the technical field of emotional robots, and discloses an embracing emotional robot which comprises an inner shell, low-cost and high-simulation breathing simulation is achieved through cooperative control of an air pump and an air valve, and the embracing emotional robot is designed by combining simulation body temperature brought by a heating piece and synchronous action of a rotatable arm. The emotion robot can naturally rotate forwards when being embraced and is matched with simulated respiration and simulated body temperature, so that the immersion and emotion interaction experience of a user are remarkably improved; the operation cycle of the air pump strictly follows biological breathing characteristics, the inspiration stage accounts for 2 / 3 of the breathing cycle, the speed of the inspiration stage changes fast and slow, the expiration stage accounts for 1 / 3 of the expiration stage, smooth pressure relief is achieved through the air valve, and the highly-simulated breathing mode can effectively guide a user to breathe synchronously, so that parasympathetic nerves are activated, the anxiety level and heart rate fluctuation are reduced, and the user experience is improved. And the whole structure adopts a modular design, complex functions such as breathing simulation, embracing and body temperature simulation can be realized, the maintenance is convenient, and the device is particularly suitable for long-term accompanying scenes.

The invention relates to the technical field of exercise risk analysis and early warning, and discloses an exercise risk analysis and early warning system. The exercise risk analysis and early warning system comprises a patient data collection module, a patient exercise risk analysis module, a patient exercise risk early warning module and a data visual storage and display module. According to the system, whether the patient can do exercise or not is preliminarily judged by combining medical records of the patient, the heart rate, the respiratory rate, the body temperature and the stability analysis result of the patient in the exercise process are considered in the process of calculating the # imgabs0 #, the smaller the value of the # imgabs1 # is, the more indicating that the heart rate fluctuates during rehabilitation exercise, and the lower the value of the # imgabs1 # is, the lower the value of the # imgabs1 # is, the more indicating that the patient can do rehabilitation exercise. The more stable the change condition of the step length and the respiratory rate is, the lower the risk degree of the rehabilitation exercise is reflected, so that the patient can visually check the own condition, and the patient can visually see the own rehabilitation condition by checking the # imgabs2 # calculated after the rehabilitation exercise is performed each time.

The invention discloses a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and relates to the technical field of thoracic surgery postoperative complication. Key sign signals such as heart rate fluctuation amplitude Cxv, respiratory rhythm drift coefficient Chx, pulse waveform disturbance degree Cmd and epidermal temperature change rate Cbt of a postoperative patient are continuously collected and denoised through a postoperative sign data acquisition module, and a structured sign data set TW is formed. By means of the method, tiny physiological fluctuation of a postoperative patient within 24 hours can be captured, early risk signals are prevented from being neglected, and the time sensitivity of complication recognition is improved from the source. A dynamic sliding window regression technology is introduced to carry out trend modeling on the sign data, so that a miniature instability section can be accurately extracted; through the improvement, the system can capture precursor signals of potential infection and bleeding earlier, and the possibility of early intervention is improved.

The invention discloses a heart rate pectoral girdle using process management method and system based on monitoring data feedback, and relates to the technical field of heart rate pectoral girdle using process management, and the method specifically comprises the following steps: obtaining motion state information generated by a user wearing a heart rate pectoral girdle in an aerobic motion process in real time, respectively generating a heart rate fluctuation intensity coefficient and an exercise load fluctuation index; and constructing an exercise load adjustment evaluation model for the generated heart rate fluctuation intensity coefficient and the exercise load fluctuation index, generating an exercise load adjustment evaluation coefficient, and evaluating the exercise load monitoring demand degree of the user wearing the heart rate chest strap in continuous long-time aerobic exercise. And the conditions are divided into three types of conditions of low demand degree, medium demand degree and high demand degree. The heart rate chest strap solves the problem that an existing heart rate chest strap cannot accurately and dynamically adjust the monitoring strategy in the aerobic exercise process of a user, and through real-time feedback and periodic optimization, the exercise load evaluation accuracy and the equipment performance are improved.

The invention relates to the technical field of health monitoring, in particular to a cardiovascular and cerebrovascular disease risk assessment management system based on accurate driving of health data, and the system comprises a rhythm sampling tracking module, a fluctuation mode joint recognition module, an abnormal joint change labeling module, a priority follow-up sorting module and a risk location division module. According to the method, the difference between systolic pressure and diastolic pressure extreme values in a continuous period is dynamically compared, the heart rate fluctuation stability screening condition is combined, blood oxygen saturation and heart rate variability data are collected in a linkage mode, the sampling frequency is improved, and a multi-parameter combination structure with direction consistency and time coincidence is constructed; and further extracting change fragments with consistent trends and generating a difference sequence, performing quantitative comparison in combination with body temperature amplitude variation characteristics, realizing accurate positioning of a high-collaboration section through time axis aggregation analysis, and effectively enhancing the recognition capability of risk fluctuation fragments in a short period and the continuous tracking effect of a dynamic process.

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 heart rate monitoring, in particular to an arrhythmia early warning system based on individual heart rate variability analysis, which comprises a feature expression acquisition module, a division module, a heart rate fluctuation rule degree acquisition module, a non-disease possibility acquisition module and a correction module, dividing a heart rate fluctuation time period in the heart rate time sequence into an increasing trend section and a decreasing trend section, determining a main change trend of the heart rate fluctuation time period, and obtaining the heart rate fluctuation rule degree of the heart rate fluctuation time period according to the association between the increasing trend section and the decreasing trend section in combination with the characteristics of the main trend section. The non-disease possibility of the heart rate fluctuation time period is obtained based on the characteristic expression and the heart rate fluctuation rule degree of the heart rate fluctuation time period, and reverse correction is performed on the heart rate variability of the heart rate fluctuation time period according to the non-disease possibility, so that the credibility of the heart rate variability can be improved, and the accuracy of the heart rate variability is ensured; therefore, the accuracy of early warning of arrhythmia of the patient is improved.

The invention belongs to the technical field of computer vision and behavior recognition, and particularly discloses and provides a multi-dimensional drowning risk trajectory backtracking visualization system, which is characterized in that through time-space correlation analysis of swimmer stroke frequency variation coefficient, heart rate fluctuation trend and water flow data, traditional single data source monitoring limitation is broken through, and early warning mode upgrading is realized; based on spatial relationship detection of dynamic visual range rendering and risk key nodes, responsibility boundaries lacking environmental objective limitation and human intervention are distinguished, and wave height and illumination intensity are introduced to correct transparency of a mask layer in real time, so that the view of a lifeguard is highly consistent with an actual shielding state; a risk weight distributor and a three-dimensional space-time path are utilized to convert chain reactions of action abnormity, water flow environment sudden change and view shielding into a space-time evidence chain capable of being quantitatively traced, multiple data sources are integrated to generate a visual report, and the proof effect of drowning accident liability traceability and the accurate optimization effect of safety design are achieved.

The invention discloses a portable autonomic nerve function evaluation device, method, equipment and program product, and is specifically characterized in that the breathing of a user is individually adjusted to a specific frequency through an intelligent breathing monitoring and guiding program, and the heart rate fluctuation condition of the user in the process is recorded by a heart rate acquisition device; the heart and lung coupling strength of a user is quantitatively analyzed based on a feature extraction algorithm, and a mapping model between physiological features and clinical indexes is constructed to realize quantitative evaluation and portable monitoring of autonomic nerve functions. Based on accurate decoding of the cardiopulmonary coupling process, the portable autonomic nerve function evaluation device, method, equipment and program product which are rapid, low in manufacturing cost and easy and convenient to operate and have popularization significance are achieved, and the needed measuring device is simple and convenient and can be used for individualized home health evaluation.

The embodiment of the invention relates to a carsickness early warning method and device, a vehicle, electronic equipment and a storage medium. The method comprises the steps that the current attitude angle of the vehicle is acquired; calculating the current vestibular stimulation intensity based on the attitude angle and a preset carsickness weight coefficient; acquiring a current electrocardiosignal of a target user in the vehicle from a preset electrocardiosignal acquisition device; determining current heart rate fluctuation data of the target user based on the electrocardiosignal; detecting the pupil diameter of the target user to obtain the current pupil diameter of the target user; and performing carsickness risk prediction on the vestibular stimulation intensity, the heart rate fluctuation data and the pupil diameter by using a preset carsickness risk prediction model to obtain a carsickness risk level. According to the embodiment of the invention, the physiological abnormal state of the user in the vehicle can be detected in time, the carsickness risk level is accurately judged, the specific user is detected in a targeted manner, the adaptability of carsickness early warning and the user is improved, and the vehicle can execute a targeted coping strategy for the carsickness degree.

The present application relates to the technical field of health monitoring, in particular to a cancer survivor work pressure monitoring system based on big data, which comprises a physiological parameter analysis module, a stress cause analysis module, a work load distribution module and an abnormal stress monitoring module. In the present application, the sensitivity and accuracy of data analysis are improved through the difference analysis of heart rate fluctuation, skin conductance and blood pressure range, the data points with large fluctuations are screened and classified, the efficiency of abnormal data capture and classification is optimized, the stress cause identification accuracy and distribution law insight are improved, the key stress factors are determined through sensitivity sorting, the stress evaluation is more scientific and quantitative, the priority dynamic adjustment is realized in load classification and grouping, the work pressure distribution is optimized, the abnormal stress point monitoring and grading judgment are strengthened, the stress abnormality grade fine monitoring is realized, the data quantification and dynamic monitoring capability are enhanced, the stress management efficiency is improved, and the targeted data support is provided for personalized health intervention.

The invention relates to the technical field, in particular to a monitoring bracelet based on edge end lightweight preprocessing, which comprises the following steps: a fluctuation sampling linkage module, a multi-signal coupling module, an event state identification module, a structured compression module and a communication scheduling control module. According to the method, through combined extraction of the heart rate fluctuation trend and the periodic variation index, the recognition accuracy of the abnormal physiological state is improved, time period matching analysis of IMU and PPG signals is utilized, the positioning precision and correction continuity of abnormal data are enhanced, a state level system is constructed in combination with rhythm structure parameters and mutation features, and the recognition accuracy of the abnormal physiological state is improved. Subdivision and grading of signal states are achieved, field extraction and data compression are adopted, the structured organization and transmission efficiency of edge data are optimized, communication configuration parameters are adjusted in a linkage mode through signal strength, power consumption changes and delay records, and the adaptability of channel activation and the communication energy efficiency of the system are improved. Closed-loop operation of physiological signal collection, state recognition and data uploading at the edge end is achieved.

The invention relates to the technical field of physiological data monitoring, in particular to an orthopedic postoperative patient physiological data analysis method and system.The orthopedic postoperative patient physiological data analysis method comprises the following steps that heart rate blood oxygen and limb activity data are collected, a heart rate fluctuation value, a blood oxygen change value and an activity range are extracted, and a multi-dimensional physiological feature sequence is constructed; and calculating a data change range in the sliding window of each dimension and a frequency change value of adjacent windows. According to the method, fine quantitative analysis of heart rate fluctuation, oxyhemoglobin saturation and limb movement amplitude is achieved by dynamically adjusting data time span and calculating indexes in a segmented mode, change characteristics of different recovery stages are adapted, data cleaning accuracy is improved through high-density region statistics and noise elimination, scientific induction of dynamic characteristics is ensured, and the method has good application prospects. In combination with feature extraction and trend analysis of an acute period, a functional reconstruction period and a stable period, comprehensive evaluation of inter-stage features is formed, and based on an offset rate, a prediction deviation value and abnormal signal trajectory labeling, the anomaly identification and real-time tracking capability is enhanced.

This invention discloses a method, system, and computer medium for determining heart rate fluctuations. The method includes: analyzing and processing 24-hour Holter monitoring data to obtain a Holter monitor containing only sinus beats; identifying each sinus beat-only Holter monitor period where the instantaneous heart rate continuously increases, and recording the event of continuous instantaneous heart rate increase as a heart rate increase event; determining whether each heart rate increase event belongs to a heart rate fluctuation event based on the instantaneous heart rate increase amount and heart rate fluctuation threshold, thereby analyzing cardiac load information based on the heart rate fluctuation events. This invention provides a new indicator for studying cardiac load by determining heart rate fluctuation events, and also provides direction and basis for studying the causes of abnormal human vital signs.

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 present invention discloses a diffusion magnetic resonance (MR) method for measuring arterial pulsation dependence of perivascular cerebrospinal fluid flow in glymphatic system: DTI acquisition: Brain MRI images were acquired using dynamic diffusion tensor imaging (DTI); Cardiac signal synchronization: Simultaneously collect heart rate fluctuation time-series signals; Peak coordinate determination: Identify the peak timing of cardiac pulsation signals from the time-series data; Image realignment: Reorganize brain MR images according to their temporal positions within the cardiac cycle; Temporal interpolation: Perform uniform time-sampling reconstruction to generate equidistant diffusion MRI datasets across the cardiac cycle; Parameter calculation: Compute axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) at each voxel level; Mask-based analysis: Generate characteristic curves of AD / RD / MD and spin density(S) dynamics using region-specific masks in individual space. This method enables non-invasive measurement of: (1) Cerebrospinal fluid (CSF) flow velocity / direction in large perivascular spaces during heartbeats; (2) Microvascular perivascular CSF dynamics through diffusion parameter analysis.

This invention provides a heart rate information acquisition device that can select the appropriate signal from multiple signals to obtain highly accurate heart rate information. [Solution] A heart rate information acquisition device for acquiring heart rate information of a subject lying in bed comprises: a signal acquisition unit that acquires a plurality of signals, each containing a component that fluctuates according to the subject's heart rate; a signal selection unit that selects an information acquisition signal from the plurality of signals to be used for acquiring the heart rate information based on the kurtosis of each of the plurality of signals; and a heart rate information acquisition unit that acquires the subject's heart rate information based on the information acquisition signal.

The invention relates to the technical field of data monitoring, in particular to an air pressure stability monitoring method for an anti-bedsore air mattress. Obtaining a periodic characteristic value according to the air pressure change characteristic of the air chamber; obtaining a historical period range according to all the periodic characteristic values; a fluctuation reference value is obtained according to the air pressure fluctuation characteristics in the historical period range; preliminarily judging the air pressure stability of the air mattress according to the air pressure fluctuation characteristics of the current monitoring period and the fluctuation reference value; obtaining an average pressure difference according to the air pressure difference characteristics of different air chambers; obtaining the difference degree according to the average pressure difference between any moment in the current monitoring period and any historical reference moment; according to the heart rate fluctuation characteristics and the acceleration fluctuation characteristics of the user, the body position change degree is obtained; and obtaining a change correlation degree according to the difference degree and the body position change degree. The air pressure stability characteristic value is obtained according to the change correlation degree and the difference degree, the air pressure of the air mattress is monitored, and the monitoring accuracy is improved.

The present application relates to the technical field of nursing decision, specifically to a nursing scheme auxiliary selection system for orthopedic patients, in the present application, in the rehabilitation training process, the moving track of the patient is collected in real time and deviation analysis is conducted with the ideal path, the deviation risk fragment set is established by means of the spatial coordinate sequence and the Hurst index calculation mode, the identification and positioning of the deviation trend of the rehabilitation action are strengthened, and the accuracy of the walking control stability evaluation is enhanced. The heart rate signal is mapped to a multi-dimensional density space, the local abnormal interval in the heart rate change is accurately calibrated through the local abnormal factor operation mode under the sliding window, and the identification of the slight physiological change is facilitated. The heart rate abnormal interval is classified and processed according to the duration, a heart rate fluctuation typing system is constructed, the transient physiological fluctuation and the continuous stress state can be effectively distinguished, and the identification degree of the pain inducement is improved.

This invention relates to the field of data monitoring technology, specifically to a method for monitoring the air pressure stability of an anti-decubitus air mattress. The method involves: obtaining periodic characteristic values ​​based on the air pressure change characteristics of the air chambers; obtaining a historical period range based on all periodic characteristic values; obtaining a fluctuation reference value based on the air pressure fluctuation characteristics within the historical period range; preliminarily judging the air pressure stability of the air mattress based on the air pressure fluctuation characteristics and fluctuation reference value of the current monitoring period; obtaining an average pressure difference based on the air pressure difference characteristics of different air chambers; obtaining the degree of difference based on the average pressure difference between any time in the current monitoring period and any historical reference time; obtaining the degree of positional change based on the user's heart rate fluctuation characteristics and acceleration fluctuation characteristics; and obtaining a correlation degree between the degree of difference and the degree of positional change. This invention obtains air pressure stability characteristic values ​​based on the correlation degree and the degree of difference and monitors the air pressure of the air mattress, improving monitoring accuracy.

The present invention discloses a portable autonomic nervous function assessment device, method, equipment, and program product. Specifically, the device utilizes an intelligent respiratory monitoring and guidance program to individually adjust a user's breathing to a specific frequency, utilizes a heart rate acquisition device to record the user's heart rate fluctuations during this process, quantitatively analyzes the user's cardiopulmonary coupling strength based on a feature extraction algorithm, and constructs a mapping model between physiological characteristics and clinical indicators to achieve quantitative assessment and portable monitoring of autonomic nervous function. Based on accurate decoding of the cardiopulmonary coupling process, the present invention realizes a portable autonomic nervous function assessment device, method, equipment, and program product that is fast, inexpensive, easy to operate, and has widespread application. The device requires a simple measurement device and can be used for personalized home health assessments.

The present invention relates to the field of dynamic heart rate monitoring technology, and specifically discloses a dynamic heart rate monitoring method and system based on a smart watch. The user's heart rate data is collected in real time through the smart watch, and a bandpass filter is used for noise filtering. The signal processing is optimized in combination with a dynamic threshold adjustment method to ensure that the data is accurate and reliable. The pre-processed heart rate signal is time-series aligned and segmented for analysis, and time domain and frequency domain features are extracted, including the heart rate fluctuation amplitude and the instantaneous heart rate change rate. According to the analysis results, the user's heart rate abnormality is evaluated, and the abnormalities are divided into normal, mild abnormalities, and severe abnormalities. For severe abnormalities, the system issues an early warning in a timely manner; for mild abnormalities, a gradient boosting decision tree model is used to predict the heart rate change trend and potential abnormalities in the future monitoring period, and a personalized health report containing a heart rate fluctuation trend graph and abnormality analysis is generated to provide health guidance for the user.

The present application belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation method for postoperative lung function rehabilitation training, which comprises: acquiring an electrocardiosignal, a respiratory signal and a limb movement signal of a patient; identifying a heart rate fluctuation pattern of the electrocardiosignal to generate an abnormal fluctuation period, analyzing a time difference between an inhalation phase and an exhalation phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiorespiratory risk level; automatically adjusting a movement intensity of a rehabilitation training device based on the cardiorespiratory risk level, and dynamically selecting a regulation mode according to a ratio change between a chest fluctuation amplitude and an abdominal fluctuation amplitude; gradually increasing the movement intensity when a heart rate recovery rate is lower than a preset reference and a respiratory rhythm stabilization time exceeds a critical value; calculating a movement intensity correction coefficient and triggering a movement posture correction instruction; and generating a cardiorespiratory function adaptability rating report. The present application solves the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art.