354 results about "Respiratory rate" 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 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 a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The pelvic floor muscle self-adaptive rehabilitation training system comprises a data acquisition unit, a data processing unit, a type identification unit, a degree evaluation unit and a training adjustment unit, and is used for acquiring rectus abdominis data, gluteus maximus data, respiratory rate data and the like of a user in a pelvic floor muscle rehabilitation training process as compensation identification data; then, the type recognition unit can process the compensation recognition data to obtain unconscious compensation types of the user in pelvic floor muscle rehabilitation training, the compensation types comprise abdominal muscle compensation, hip compensation and suffocation compensation, and meanwhile, the degree evaluation unit can evaluate the severity of the compensation types, so that the degree of the pelvic floor muscle rehabilitation training is improved. Finally, the training adjusting unit can generate a multi-modal strategy according to different compensation types and different severity degrees, the multi-modal strategy is used for guiding pelvic floor muscle rehabilitation training of the user, intervention on compensation is achieved, it is guaranteed that the user conducts complete rehabilitation training on pelvic floor muscles, invalid training and even injury are avoided, and the rehabilitation effect is enhanced.

The invention provides an electrocardiogram data analysis method, device, medium and equipment based on multi-modal fusion, which are applied to early warning of acute coronary syndrome (ACS). The method comprises the following steps: acquiring time sequence data of electrocardiosignals and ballistocardiogram signals as well as structural data such as heart rate, respiratory rate and oxyhemoglobin saturation by using a portable device, and preprocessing the data; constructing a double-branch global and local interactive feature extraction model, respectively extracting local and global features of the electrocardiosignal and the ballistocardiogram signal, and simultaneously bridging the isomerism of the two signal types; after feature mapping is carried out on the structural data, inputting the structural data and features of the time series data into a feature fusion model; and finally inputting the fused features into a disease early warning model, outputting an ACS risk probability, and comparing the ACS risk probability with a preset threshold value to realize real-time monitoring and early warning. The method is of great significance to prevention of ACS and guarantee of life health of high-risk groups with cardiovascular and cerebrovascular diseases.

The invention discloses an all-directional monitoring and emergency treatment system for patients in the emergency department, which belongs to the technical field of diagnosis and monitoring of emergency surgery and comprises a data input and acquisition module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data input and acquisition module is used for acquiring emergency treatment features including external stimulation inducements and ages of a current patient before emergency treatment; the monitoring and evaluation module is used for acquiring an epilepsy risk evaluation value, a comprehensive evaluation value and a feedback adjustment value, and collecting all-dimensional monitoring characteristics of a current patient, including amygdalia activeness, a brain activity reference level, abnormal discharge intensity, heart rate, respiratory rate, systolic pressure value and diastolic pressure value, and the monitoring and evaluation module is used for acquiring and outputting the epilepsy risk evaluation value, the comprehensive evaluation value and the feedback adjustment value; the early warning module is responsible for carrying out early warning and inputting an adjusted first-aid strategy to the first-aid execution module when the epilepsy risk assessment value, the comprehensive assessment value and the feedback adjustment value are in a continuous monitoring rising trend on the visualization module, and comprehensive and real-time monitoring and accurate assessment of the illness state of a patient and timely adjustment of first-aid measures are achieved.

The invention belongs to the technical field of sleep quality analysis, and particularly relates to a sleep quality analysis system and method based on a medical bed sensor, so as to solve the technical problem that an existing method excessively depends on duration statistics and neglects sleep continuity and periodic rhythmicity. The analysis method comprises the following steps: S1, acquiring sign signal data acquired by a sensor on a medical bed in a sleep cycle, and dividing the sign signal data into a plurality of continuous time slices; s2, extracting physiological indexes of respiratory rate, heart rate and body movement amplitude in each time slice, and calculating variation depth of the corresponding time slice; s3, generating a variation depth baseline; calculating an unsteady state disturbance factor according to the occurrence frequency and the duration of the micro-awakening disturbance fragment; and S4, generating a sleep quality comprehensive score. According to the analysis method, the comprehensiveness and objectivity of the evaluation result are improved, and the sleep health condition is reflected more truly.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

The invention discloses intelligent pasture livestock health monitoring equipment. The equipment comprises a vital sign monitoring mechanism worn on the legs of livestock, a behavior monitoring mechanism worn on the neck of the livestock and an environment monitoring mechanism vertically extending and arranged on the pasture ground; the vital sign monitoring mechanism comprises a vital sign monitoring fixing ring, a vital sign monitoring supporting shell with an upward opening is connected to the vital sign monitoring fixing ring, and a plurality of body temperature monitoring sensors are connected into the vital sign monitoring supporting shell; a pulse monitoring through hole penetrating in the radial direction of the physical sign monitoring fixing ring is formed in the side face of the physical sign monitoring fixing ring, and a pulse monitor is arranged at the pulse monitoring through hole; the behavior monitoring mechanism comprises a conventional monitoring camera, a high-speed monitoring camera and an infrared monitoring camera; the equipment can accurately collect various physiological data of livestock, such as body temperature, heart rate, respiratory rate and the like, as well as behavior data, such as activity amount, ingestion time, rumination times and the like, so that the accuracy and reliability of the data are ensured, and an accurate basis is provided for evaluating the health condition of the livestock.

The invention provides a terminal data management method and system for medicine sales. According to the method, medicine purchasing historical data, electronic health record data and allergy taboo data of customers are integrated to construct a medicine adaptive sign map, and the map establishes a dynamic association relationship between medicine indications and sign parameters. And acquiring heart rate and respiratory rate parameters of the customer in real time, and matching the heart rate and respiratory rate parameters with the physical sign parameters in the map to generate a physical sign parameter matching result. And generating a personalized recommendation instruction containing an initial recommended drug list and taboo prompts in combination with taboo rules and curative effect data in the drug indication library. According to the real-time change trend of the heart rate and the respiratory rate, the drug priority of the recommendation list is dynamically adjusted, finally, an updated recommendation drug list is generated and sent to the sales terminal equipment, and accurate drug recommendation based on the real-time health state is achieved. According to the technical scheme provided by the invention, the security and accuracy of terminal data management can be improved.

The present invention proposes a breathing frequency detection method based on time-frequency analysis of Hilbert-Huang transform. First, a WiFi link with strong environmental perception sensitivity is selected to construct a channel state information (CSI) ratio model. Secondly, the filtered CSI ratio time series is projected, and candidate sets of different breathing mode signals are generated by combining amplitude and phase information. Thirdly, the selected breathing modes are subjected to signal variational mode decomposition (VMD) and time-frequency analysis of Hilbert-Huang transform, so as to remove non-human breathing frequency components. On this basis, reconstruction is carried out, and the reconstructed signals are fused using principal component analysis (PCA). Finally, the breathing frequency is calculated through a false peak detection algorithm. The breathing frequency detection algorithm designed by the present invention not only expands the sensing range and eliminates "blind spots" while removing time-varying phase offsets and high-frequency noise and selecting the optimal subcarriers, but also improves the breathing frequency detection accuracy, providing a more accurate detection method for non-contact breathing frequency detection.

The invention provides a vital sign detection method and device based on a millimeter wave radar, electronic equipment and a storage medium. The method comprises the following steps: obtaining a distance frequency domain signal based on a radar echo signal; performing spectral analysis on the distance frequency domain signal, positioning the position of the to-be-detected object, and extracting phase information at the position; filtering through a band-pass filter to obtain a breathing frequency band interval signal and an initial heartbeat frequency band interval signal; performing real-time respiration rate estimation based on the respiration frequency band interval signal to obtain a respiration frequency, and eliminating respiration harmonics in the initial heartbeat frequency band interval signal by using an adaptive wave trap based on the respiration frequency to obtain a heartbeat frequency band interval signal; and with the minimum fuzzy entropy as an optimization target, decomposing the heartbeat frequency band interval signal by using a variational mode decomposition algorithm to obtain a heartbeat signal, and carrying out real-time heart rate estimation based on the heartbeat signal to obtain a real-time heart rate. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the accuracy of heart rate measurement can be improved.

An apparatus for vital sign extraction. The apparatus may receive a vital signal of a subject from a sensing device. The apparatus may also perform a preprocessing procedure on the vital signal via bandpass filtering and normalization to obtain a preprocessed signal. The apparatus may also perform a time-frequency analysis of the preprocessed signal, and estimate a heart rate of the subject from a dominant component of the preprocessed signal by finding location of a maximum spectral energy of the time-frequency analysis. In addition, the apparatus may identify guard components in the preprocessed signal in view of the dominant component, and derive a respiratory rate of the subject from a length of an interval between the dominant component and each of the guard components.

A non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG, iPPG, and rPPG) and Ballistocardiography (BCG and iBCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols. Applications include telehealth, fitness monitoring, public health screening, and automotive safety systems.

The invention discloses an infant state recognition method and system based on traditional Chinese medicine five-tone monitoring analysis, and the method comprises the steps: synchronously collecting the crying sound, physiological signals and behavior videos of an infant, extracting the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate of the physiological signals, and the facial expression and limb movement features, and carrying out the recognition of the state of the infant through the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate. Performing structured integration by using a multi-modal feature fusion model; in combination with the five-tone theory of traditional Chinese medicine, a corresponding relation between audio features and five-organ states is established, and the robustness of five-tone and five-organ mapping is improved through fuzzy reasoning and a Bayesian mechanism; the system dynamically adjusts the weight coefficient of each mode, adapts to the individual and emotion historical trend, achieves more accurate emotion recognition and physiological evaluation, achieves cross-mode and multi-layer information fusion, improves the accuracy and interpretation ability of infant emotion and five-internal-organ state recognition, and provides a scientific basis for clinical evaluation and health management.

The invention discloses personal life support equipment in a high-altitude environment and a cooperative control method thereof, and relates to the technical field of personal life support equipment, and the equipment comprises a respiration sensor which is arranged on a mask and is used for detecting respiration frequency and phase; the blood oxygen and heart rate sensor is worn on the wrist of the user and used for detecting the blood oxygen saturation degree and the heart rate; the body temperature sensor is attached to the chest of the user and used for detecting the core body temperature; the automatic oxygen supply device is used for providing oxygen; the intelligent heating device comprises partition heating pieces attached to the chest and the four limbs of the user and is used for partition heating; the microcontroller unit is used for formulating an optimal life support strategy according to the sensor data and intelligently and cooperatively controlling the working states of the automatic oxygen supply device and the automatic heating device according to the optimal life support strategy; the sensor data comprises respiratory frequency and phase, oxyhemoglobin saturation, heart rate and core body temperature, and dynamic oxygen supply and body temperature adjustment can be cooperatively controlled.

The invention aims to provide a portable automatic adjustment negative pressure sputum suction device based on multi-parameter monitoring, which comprises a negative pressure sputum suction device main body used for generating adjustable negative pressure and connected to an airway of a patient through a sputum suction pipe; the respiratory rate sensor is mounted at the inlet of the sputum suction tube and used for monitoring the respiratory rate of the patient in real time; the sputum suction resistance sensor is mounted in the sputum suction tube and used for monitoring the resistance change in the tube in the sputum suction process in real time; the sputum viscosity sensor is arranged between the sputum suction tube and the sputum collection bottle and is used for monitoring the viscosity of sputum in real time; the control unit controls the negative pressure sputum suction device main body to output corresponding negative pressure; the sputum suction device aims at monitoring the respiratory rate, the sputum suction resistance and the sputum viscosity of a patient in real time, a dynamic negative pressure adjusting mode is adopted, the sputum suction efficiency can be improved, and meanwhile damage of excessive suction to an airway can be avoided.

Various example of the present disclosure provide sensing apparatuses configured for wearable and wireless use for deep tissue physiological monitoring. The sensing apparatuses may be embodied by a thin flexible patch configured to conform with a skin surface of a subject. A sensing apparatus may include a plurality of microneedles oriented to extend towards and penetrate into the subject to a shallow depth. The microneedles may be configured as waveguides for a given sensing modality (e.g., light, ultrasound), such that sensing wave signals propagate to deep tissues. For the sensing, the sensing apparatus includes waveform generators (e.g., light-emitted diodes) and waveform detectors (e.g., photodiodes). Machine learning models may be used to process and denoise sampled data from the waveform detectors and to generate accurate and reliable physiological measurements, including heart rate, respiratory rate, pulse intensity, respiratory intensity, blood oximetry, tissue oximetry, blood flow rate, and / or the like.

A human body breathing and heartbeat signal estimation method based on a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) is characterized by comprising the following steps of (1) obtaining an intermediate frequency signal of a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) system, (2) carrying out distance dimension FFT and phase extraction and unwrapping on a Chirp intermediate frequency signal channel by channel, and (3) carrying out phase extraction and unwrapping on the Chirp intermediate frequency signal channel by channel. (3) calculating signal energy of interested breathing and heartbeat frequency bands in the phase time sequence under all distances, and weighting amplitudes of all distance dimension FFT under all distances to realize accurate positioning of a human body target, and (4) regarding the phase time sequence under the accurate distance and the adjacent distance of each channel as potential multipath signals, and performing fusion enhancement on the signals to realize accurate positioning of the human body target. And (5) processing the enhanced phase time sequences of the channels on the basis of multivariate variational mode decomposition, separating and estimating respiratory signals and heartbeat signals of the human body, and calculating an average respiratory rate and an average heart rate in an observation time period.

The invention relates to the technical field of breathing machine off-line evaluation, in particular to a breathing machine intelligent off-line evaluation system based on multiple parameters, which is characterized in that a data acquisition unit acquires breathing and physiological parameters of a patient in real time; the data analysis unit extracts a respiratory rate instantaneous value and a blood oxygen saturation continuous measurement value through preprocessing, inputs the respiratory rate instantaneous value and the blood oxygen saturation continuous measurement value into an intelligent analysis model composed of a deep residual network and a time convolution network, fuses a physiological hysteresis compensation coefficient to generate an offline index, and outputs a preliminary offline risk index; the data processing unit is embedded into a clinical rule base through a decision tree algorithm, generates a risk correction factor through a taboo check factor, allocates differentiation weights according to severity levels, corrects initial risk deviation and generates a comprehensive offline risk index, and the clinical rule base is continuously optimized through online learning, so that the offline risk assessment accuracy is improved; and a reliable basis is provided for clinical decision-making.

The invention provides a pension service supervision method and system based on an image recognition AI model algorithm. The method comprises the following steps: aiming at respiratory arrest, pressure sores and limb stiffness risks in a bedridden old-age care scene, firstly, establishing a physiological abnormality library containing parameters such as a respiratory rate threshold value and a body pressure critical value; a physical sign monitoring device is deployed to collect body pressure hot area distribution data of the bedridden person in real time, breathing interval abnormity, heartbeat attenuation and body pressure exceeding areas are analyzed through an AI model, and a retention risk index is generated. And when the monitoring data exceeds an abnormal library threshold value, triggering multi-modal early warning information, fusing risk area positioning, abnormal duration and temperature gradient change, outputting a multi-modal early warning report combining images and texts, and guiding the nursing personnel to perform targeted intervention. According to the technical scheme provided by the invention, real-time monitoring and multi-mode risk early warning of health hidden dangers in bed are realized, the occurrence rate of complications such as respiratory arrest and pressure sores is reduced, and the precision and response efficiency of nursing for the aged are improved.

The invention relates to the technical field of millimeter wave radar vital sign detection, and discloses a millimeter wave radar vital sign signal processing system and method. According to the method, distance dimension processing and phase extraction are carried out on millimeter wave radar echo signals, heartbeat signals are enhanced through phase difference, and the time domain sparse characteristic of random motion interference is highlighted; then, a sparse optimization model combining a time domain and a frequency domain is constructed, differential sparse constraints are applied to a breathing frequency band, a heartbeat frequency band and a heartbeat harmonic frequency band in the frequency domain, and two-norm morphological constraints are applied to sparse peaks of random motion interference in the time domain; and finally, the model is solved to realize separation of interference and vital sign signals. Complex hardware configuration is not needed, weak heartbeat signal error suppression can be effectively avoided, the stability and accuracy of respiratory rate and heart rate estimation in a random human body motion scene are remarkably improved, and the method is suitable for a non-contact vital sign monitoring scene.

The invention relates to the technical field of health monitoring equipment, in particular to a high-precision multi-dimensional health monitoring body fat scale which comprises a pressure sensor used for accurately measuring the weight of a user; the bioelectrical impedance sensor is used for measuring electrical impedance values of different parts of the human body; the millimeter-wave radar module integrates a millimeter-wave radar transmitter and a millimeter-wave radar receiver and is used for monitoring the heart rate and the respiratory rate of the user in a non-contact manner; the main control chip is used for receiving signals transmitted by the pressure sensor, the bioelectrical impedance sensor and the millimeter-wave radar module, operating an improved BIA algorithm, a millimeter-wave radar signal processing algorithm and a multi-sensor data fusion algorithm, and processing and analyzing health data; the display module is used for displaying body weight, body fat rate, heart rate, respiratory rate and other health data; and the power supply module provides a stable power supply for each component. The body fat scale solves the technical problems that an existing body fat scale is insufficient in measurement precision and stability, and multi-dimensional data monitoring is limited.

The wearable electronic device may be equipped with sensors to measure heart rate, blood oxygen levels, respiratory rate, blood pressure, blood glucose levels, and skin temperature. The device may include graphene-based sensors to measure heart rate, blood oxygen levels, respiratory rate, blood pressure, blood glucose levels, and skin temperature. The device may include a process that uses capacitive measurements to determine reliability of biosensor measurements. Capacitive measurements may be made on the device and a quality of contact between the device and the wearer may be determined. When the quality of contact between the device and its wearer is insufficient for reliable biosensor measurements, the device may display an alert to the wearer.

The invention discloses an inpatient alarm bracelet system and method based on multi-parameter vital sign monitoring, and relates to the technical field of inpatient alarm bracelets. Matching corresponding vital sign parameter initial weights from a preset disease-parameter sensitivity mapping library, wherein the vital sign parameters comprise heart rate, respiratory rate, oxyhemoglobin saturation, body temperature and heart rate variability; the method comprises the following steps: establishing an individualized physiological fluctuation baseline model based on vital sign data collected at the initial stage of wearing in combination with a resting state time period identified by an acceleration sensor, and generating a dynamic reference interval; collecting photoelectric volume pulse waves, electrocardiosignals, body temperature and acceleration data, judging whether motion artifacts exist or not by analyzing the spectrum correlation between physiological signals and motion signals, and generating a signal quality mark; and calling the initial weight and the dynamic reference interval under the condition that the signal quality mark indicates that the data is valid.

The invention discloses a diet intervention method and system for crowds with cardiopulmonary health risks. The system comprises a database, an information acquisition module, a risk assessment module, a calculation module, a diet recommendation module, an auditing module and an information pushing module. The database comprises a personal information database, a suggestion database, a recipe database and a different-energy-level food intake database; the information acquisition module is used for acquiring intelligent bed sleep health data and personal information data in a database, and generating a personal user data set for the risk assessment module and the calculation module; wherein the sleep health data collected by the intelligent bed comprises but is not limited to the whole late heart rate, the respiration rate, the heart rate variability and the like; according to the invention, through combination of static and dynamic health data, through risk assessment and early warning, diet health management is carried out on crowds with cardiopulmonary health risks, and the problem that the public cannot effectively utilize knowledge to carry out self-health management actually is accurately solved.

The invention provides a sleep physiological parameter real-time monitoring method based on flexible piezoelectric sensing, and relates to the technical field of biomedical signal processing.The method comprises the steps that a human body sleep biological mechanical signal is collected through a flexible piezoelectric sensor array, and after the signal is converted through a charge amplifier, phase-space reconstruction denoising and self-adaptive gain control are carried out; wherein the gain is dynamically adjusted based on the weight of the user, the rigidity coefficient of the mattress and the real-time signal amplitude; extracting heartbeat wave crest intervals, respiratory cycles and body movement frequency domain energy characteristics from the preprocessed signals; inputting the features into a cascaded convolutional neural network and a long-short-term memory network model, and synchronously outputting the heart rate, the respiration rate and the heart rate variability; in combination with the historical health data of the user and the cardiovascular health mapping model, sleep quality indexes and disease risk scores are generated through graph convolutional network analysis, and the monitoring frequency is dynamically adjusted and an apnea alarm is triggered.

The invention relates to the technical field of risk scoring tools, in particular to a non-invasive simple risk scoring tool for traumatic hemorrhagic shock for wearable equipment. The method has the advantages that a non-invasive simple risk scoring tool for traumatic hemorrhagic shock is combined with clinical practice, economic benefits and time benefits are comprehensively considered, five vital sign indexes including systolic pressure, diastolic pressure, heart rate, respiratory rate and oxyhemoglobin saturation are used as the basis, a multi-factor Logistic regression method is used for constructing the risk scoring tool, and the risk scoring tool is used for evaluating the risk of traumatic hemorrhagic shock. The evaluation process does not need to depend on laboratory detection or highly specialized operators, and the first-aid personnel can master the use method in a short time and quickly complete evaluation. Due to the light design and the low operation threshold of the tool, the tool can be effectively applied in a non-professional scene, meanwhile, extra wounds to the wounded are reduced, the operation complexity is greatly reduced, and the risk scoring tool is more suitable for pre-hospital first aid and rapid deployment and application requirements in special scenes.