1054 results about "Heart rate" patented technology

The invention relates to the technical field of physiological status monitoring, in particular to a rehabilitation assessment method and system based on patient status awareness, and the method comprises the following steps: collecting myoelectricity, heart rate and skin conductance signal data of a patient, normalizing time sequence data, setting a time window, calculating a mean value and a mean square error, and analyzing a signal covariance variation trend. And generating a cross-domain signal stability list. According to the method, the physiological signals of the patient are collected and analyzed, the method has important significance on improvement of the rehabilitation effect, the physiological signals such as myoelectricity, heart rate and skin conductance are collected and analyzed in real time, tiny changes of the state of the patient can be carefully captured, more accurate state evaluation and rehabilitation progress monitoring are achieved, and the rehabilitation effect is improved. The accuracy of data processing and the efficiency of signal decoding are ensured by analyzing the signal stability and optimizing and adjusting the time window, and the rehabilitation scheme is further adjusted according to the specific response of the patient, so that the rehabilitation process is more personalized and targeted.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention discloses a driving style quantitative evaluation method based on multi-modal perception and a knowledge graph, and the method comprises the steps: employing a multi-modal data source, which comprises various sensor data, vehicle motion data, and physiological reaction and behavior data (such as heart rate change and operation intervention frequency during emergency braking) of a driver and a passenger in a real scene; a user emotion mapping algorithm is constructed by the model, subjective experience is converted into quantifiable numerical indexes (such as a trust index and a comfort score), a driving style deviation index (SDI) is calculated by using an algorithm in combination with a constructed driving behavior knowledge graph, quantitative evaluation of brand driving genes is realized, and a data driving basis is provided for function and algorithm optimization. And a personalized intelligent driving mode is realized. Therefore, the driving style quantitative evaluation method based on the multi-modal perception and the knowledge graph has the advantages of being high in intelligent degree and high in individuation degree.

The invention discloses a non-contact heart rate detection method, system and device based on visual Transform and multi-scale feature aggregation and a medium. The method comprises the steps that a face visible light video is obtained, the unified video frame rate is re-sampled through the frame rate, face key point positioning and region division are carried out on each frame of image, and a face visible light video sequence is obtained; performing time migration operation on the video sequence to obtain a difference frame sequence, performing channel fusion on the video sequence and a difference frame, and performing down-sampling on a spatial dimension to obtain a low-resolution feature tensor; constructing a non-contact heart rate extraction model, inputting the low-resolution feature tensor into the non-contact heart rate extraction model, and outputting a heart rate value of the target; the non-contact heart rate extraction model comprises a feature enhancement module, a multi-scale mask feature aggregation module, a Transform time sequence modeling module and an rPPG predictor, the system, the device and the medium are used for achieving the non-contact heart rate detection method based on visual Transform and multi-scale feature aggregation, and the precision of rPPG signal extraction is improved.

The invention discloses a driver state sensing system based on physiological indexes and external behavior analysis, which relates to the technical field of vehicle active safety control, and comprises a sensor group integrated in a driver contact or close area and a vehicle control part and used for collecting physiological signals and behavior characteristic signals of a driver in real time, the physiological signals at least comprise heart rate and skin electric signals, and the behavior characteristic signals at least comprise eyelid movement, head posture and steering wheel operation signals. According to the driver state sensing system, the comprehensiveness and accuracy of driver state sensing are remarkably improved by integrating physiological indexes and external behavior analysis, the limitation of single signal judgment is effectively overcome through a multi-source signal fusion mechanism, the reliability of state evaluation is ensured, and the advanced signal preprocessing technology adopted by the system is high in reliability. The data anti-interference capability is enhanced, the feature extraction precision is improved, and the robustness of the model is enhanced.

The invention relates to the field of hospital patient safety management, and particularly discloses a hospital patient safety management system and method based on data analysis, and the method comprises the steps: firstly obtaining the hospitalization record of a patient, the heart rate values, collected by an intelligent device, of the patient at a plurality of preset time points, and the blood pressure values, collected by the intelligent device, of the patient at a plurality of preset time points; and then feature extraction and correlation analysis are carried out on the three by using a deep learning technology, and finally whether a patient safety early warning is sent out is judged through a classifier, so that the patient nursing quality is improved, and the response speed to the health condition of the patient is improved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention provides a depression virtual reality auxiliary diagnosis and treatment method fused with multi-modal data, and the method comprises the steps: obtaining the multi-modal data, the multi-modal data comprises the peripheral physiological data, such as heart rate, body temperature and skin electricity, monitored by a flexible sensor based on epidermal electrons; and switching to a personalized interactive virtual scene according to the emotional state of the patient in real time, and in the interaction process, obtaining eyeball movement data by the eye movement tracking instrument, and knowing attention distribution, psychological state and micro facial expression change of the user. In combination with multi-modal data of peripheral physiological data, eye movement data and micro-expression change, key features are automatically extracted from multi-modal input data, and emotion analysis and recognition are performed through a multi-modal deep learning method with an attention mechanism. Data can be collected and analyzed in real time, the emotional state of a patient is objectively reflected through the combination of peripheral physiological data and micro-expression analysis, the requirements of the patient are met in a personalized mode, and the treatment effect is improved.

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 discloses a non-contact respiratory sleep syndrome monitoring and early warning device, and belongs to the technical field of medical equipment. The non-contact respiratory sleep syndrome monitoring and early warning device comprises a monitoring sensing module, a data analysis module and an abnormity early warning module. The problem that certain limitation exists in the prior art is solved, breathing data, blood oxygen data and heart rate data of a patient are accurately collected through the infrared sensing function, the data collection quality can be judged, the accuracy and reliability of the collected data are ensured, and the accuracy and reliability of the patient are improved. The collected breathing data, blood oxygen data and heart rate data are compared with preset warning values, whether breathing, blood oxygen and heart rate of a patient are abnormal or not is judged, and the preset warning values can be dynamically adjusted based on historical monitoring data of the patient, so that the monitoring flexibility and accuracy of the device are improved, and the monitoring efficiency is improved. Warning sound can be individually set according to abnormal grading and patient characteristics to wake up the patient, and adverse events are prevented.

The invention discloses an STM32F407-based intelligent walking stick system and a control method, which are used for solving the problems of single function, high false alarm rate and insufficient data security of the traditional walking stick. The system integrates a posture detection module, a heart rate and blood oxygen monitoring module and an environment sensing module, constructs a user health risk pre-judgment model through multi-modal data fusion, realizes a three-level adaptive alarm mechanism in combination with a dynamic decision algorithm, and triggers acousto-optic warning, cloud communication and emergency contact linkage according to risk levels. The payment and data transmission reliability is guaranteed by adopting a physically isolated security architecture and a national secret encryption technology; the dynamic power management system optimizes energy consumption distribution according to scene requirements, and the endurance of equipment is remarkably prolonged. According to the system, the monitoring accuracy is improved through a closed-loop protection system (risk identification-adaptive response-energy optimization), bus payment and smart home control are supported, the system has the characteristics of low false alarm rate, high environmental adaptability and low power consumption, and a safe and convenient life auxiliary solution is provided for old users.

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 discloses a blood glucose management method, a user interface and a related device, and the method can determine a first meal time based on a historical blood glucose value of a user, and determine a second meal time based on one or more of the meal time input by the user, the limb movement of the user and the heart rate. Third meal time is determined based on the first meal time and the second meal time, and the blood glucose health condition of the user is evaluated according to the third meal time. Therefore, according to the method, the dining time of the user can be predicted in combination with the dining time, determined in various modes, of the user in the historical diet event, and the electronic equipment can evaluate the blood glucose condition of the user more accurately.

The invention discloses an intelligent medical rehabilitation physiotherapy instrument control system, which relates to the technical field of medical instruments, and comprises the following steps: acquiring physiological data of a patient, the physiological data comprising electromyographic signals, heart rate and body temperature, and performing real-time processing on an edge end to obtain physiological features; the edge calculation unit uploads the physiological features to a cloud end, a preliminary state decision model is constructed at the cloud end according to the physiological features to judge the preliminary state of a patient, actual physiotherapy records are grouped according to the preliminary state of the patient, effective physiotherapy records are counted, and an initial physiotherapy parameter set quantum optimization physiotherapy parameter is generated; and inputting the quantum optimization physiotherapy parameters into a digital twin model for secondary simulation, generating a muscle state quantum vector, calculating a Hilbert space distance, carrying out collaborative optimization on the quantum optimization physiotherapy parameters, obtaining optimal physiotherapy parameters, returning the optimal physiotherapy parameters to an edge end, and starting physiotherapy. Through centralized processing of the cloud, the model can be dynamically updated to adapt to demand changes of different patients.

The invention provides a driver anti-fatigue monitoring alarm method, medium and system, and belongs to the technical field of image data processing. A vehicle-mounted camera device is used for collecting a facial image sequence, and a Dlib key point screening function is used for feature point positioning; the feature extraction accuracy is improved by adopting an adaptive illumination compensation algorithm and a minimum spanning tree algorithm, eye features such as an eye closing proportion value and a blinking frequency and head motion features such as a head posture angle value and a nodding frequency are calculated, and physiological data such as blood pressure, heart rate and body temperature are collected by utilizing a smart watch. And inputting the multi-dimensional features into a pre-trained FatgueNet model through an adaptive feature selection gating mechanism, outputting a fatigue state evaluation index, and dividing alarm levels. When the severe fatigue state is recognized and the driver enters the sleepy state, the system sends a pulse stimulation instruction to the smart watch to wake up the driver, and the adaptability and accuracy of the system are improved.

The embodiment of the invention provides an exercise heart rate detection method. The exercise heart rate detection method comprises the steps that an initial PPG signal and an initial ACC signal are obtained; filtering processing is carried out; calculating a resultant acceleration average value and an energy concentration degree; determining whether it is in a motion state; if not, determining heart rate information based on the time domain information; if so, judging whether a same-frequency condition exists or not; if yes, spectrum subtraction processing is carried out; and determining heart rate information in the PPG spectrum after spectrum subtraction processing and the PPG spectrum without spectrum subtraction processing based on a frequency tracking algorithm. According to the scheme, the motion state is judged more comprehensively and accurately, the heart rate information is determined based on the time domain information when the user is not in the motion state, the calculation amount is small, and the heart rate result can be obtained quickly. When the user is in the motion state, the same frequency condition between the ACC frequency spectrum and the heart rate at the previous moment is firstly judged, targeted spectrum subtraction processing is conducted on the PPG frequency spectrum, then the heart rate information is determined through a frequency tracking algorithm, motion artifact interference can be effectively eliminated, and the heart rate during motion can be accurately obtained.

The invention relates to the technical field of medical treatment, in particular to a multi-parameter collaborative infusion process intelligent monitoring and priority calling method, which comprises the following steps of: acquiring four-dimensional monitoring data through a liquid level pressure sensor array, an infrared dripping speed detection module, a flexible temperature patch and a photoelectric heart rate sensor, and combining an LSTM (Long Short Term Memory) anomaly prediction model, and outputting the pipeline state score, the allergy risk index and the infusion progress prediction value in real time. And generating a real-time risk value through a weight configuration mechanism and stage identification logic, judging a risk level according to a dynamic threshold value, and outputting a corresponding early warning level signal. And the system further maps the early warning level signals into common, emergency and critical call instructions, and distributes the common, emergency and critical call instructions to a multi-channel execution terminal to construct a multi-level linkage response mechanism of closed-loop feedback. According to the invention, the accuracy and response efficiency of infusion risk identification can be significantly improved, and the method is suitable for intelligent reconstruction and automatic early warning response scenes of a clinical nursing information system.

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 field of anesthetic concentration regulation and control, in particular to a method for analyzing and evaluating the administration concentration of an inhalation type anesthetic. The method comprises the following steps: firstly, according to characteristic data such as basic physiological data, previous anesthesia sensitivity and operation information of a patient, applying a fuzzy matching algorithm to obtain an initial administration concentration; physiological sign data such as blood pressure, heart rate, pulse oxyhemoglobin saturation and end-expiratory carbon dioxide partial pressure of the patient are monitored in real time in the anesthesia process and compared with a normal range, whether the physiological sign data are abnormal or not is judged, and an adjustment scheme of the administration concentration is determined according to the administration concentration-physiological sign correlation model; after the administration concentration is adjusted, the electroencephalogram double-frequency index is monitored to evaluate the anesthesia depth, the anesthesia depth matching degree is analyzed to judge the adjustment effect, and therefore closed-loop control over the anesthetic administration concentration is achieved. According to the method, the administration concentration can be accurately regulated and controlled, the concentration regulation mechanism is optimized, the safety and effectiveness of the anesthesia process are effectively improved, and a reliable guarantee is provided for clinical anesthesia.

Optical patient monitoring systems are disclosed. The system may comprise an optical coupling system configured to transmit to and receive light signals from one or more locations on a subject; an optical processing system configured to generate optical data using the received light signals; and a computer programmed to receive the optical data; determine, using the optical data, at least one indicator of blood pressure; estimate an estimated blood pressure using the at least one indicator of blood pressure; and generate a report indicative of the estimated blood pressure. The at least one indicator of blood pressure comprises one or more of near-infrared spectroscopy (NIRS) data; photoplethysmography (PPG) data, diffuse correlation spectroscopy (DCS) data, speckle contrast optical spectroscopy (SCOS) data, speckleplethysmography (SPG) data, first derivative PPG data, second derivative PPG data, first derivative SPG data, second derivative SPG data, inflow (Fin) data, outflow (Fout) data, heart rate data, physiological data, and combinations thereof. Methods for estimating blood pressure are also disclosed.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention discloses an emotional interactive intelligent nursing system and method based on multi-modal perception, and belongs to the technical field of intelligent medical treatment and intelligent old-age care crossing. The matrix type pressure sensor array is integrated in the nursing mattress, multi-point pressure distribution of the body of the old people on the bed surface is obtained in real time, and high-resolution body pressure mapping is achieved. Compared with an existing single-point type or linear pressure monitoring structure, the system can accurately recognize the spatial position of a local pressed area, and early recognition and quantitative evaluation of a bedsore risk area are achieved. By fusing the pressure matrix, the heart rate, the blood oxygen, the environment temperature and humidity, the urine wetting and other multi-mode sensing data, the monitoring dimension is remarkably expanded, the health condition can be comprehensively evaluated from multiple physiological indexes, compared with a single physiological parameter monitoring mode, the more comprehensive information obtaining capacity is achieved, and the monitoring precision and the nursing efficiency are effectively 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 discloses an electrocardiosignal-based sleep apnea detection method and system and a storage medium. The method comprises the following steps: acquiring a target electrocardiosignal to be analyzed, performing R peak identification on the target electrocardiosignal, calculating an RR interval between two adjacent R peaks, and acquiring a voltage amplitude of the electrocardiosignal at the R peak position as an R peak amplitude; on the basis of the RR interval, the RR interval is converted into an instantaneous heart rate sequence, resampling operation is carried out on the heart rate sequence, interpolation processing is carried out on non-uniform sampling points obtained after resampling so as to obtain a continuous and equally-spaced heart rate curve, and signals of a breathing-related frequency band are extracted through filtering to serve as electrocardio derived breathing signals; and splicing the RR interval, the R peak amplitude and the electrocardio-derived respiration signal to form a multi-channel feature, and inputting the multi-channel feature into a trained sleep respiration detection model to obtain sleep apnea state judgment information. Therefore, the apnea state can be detected more comprehensively, and the accuracy of sleep apnea detection is improved.

The invention relates to the technical field of health state monitoring, in particular to a real-time health state monitoring system and method based on a biosensor. According to the scheme, through multi-mode data fusion, physiological data such as postures, heart rates and blood oxygen collected by the biosensor are combined with postures and environment videos of image collection, the limitation of single data source monitoring is solved, and the situation that traditional falling early warning is misreported due to normal actions such as stooping is avoided; or altitude stress early warning only prompts oxygen deficit but cannot be associated with the falling risk. A pre-trained risk assessment model is combined to output grading risk levels, targeted early warning such as low-risk prompt deceleration and high-risk sound-light alarm is triggered, more accurate and timely health monitoring service is provided for the elderly in the plateau area, and the falling risk caused by interaction of the environment and the physiological state is effectively reduced.

The invention discloses a method and system for sensing health based on a fingertip wearable spectrum sensor and a computer program product, and the method comprises the steps: controlling a spectrum chip to collect characteristic spectrum data corresponding to an artery blood vessel projection region of a user according to a received index monitoring instruction; the characteristic spectrum data is obtained based on emission and reception of one or more light from ultraviolet light to near-infrared light; receiving the characteristic spectrum data sent by the spectrum chip, and calculating a calibration spectrum value based on the characteristic spectrum data; and the calibration spectrum value is sent to the intelligent terminal, so that the intelligent terminal calculates the physiological health index, analyzes and processes the physiological health index, and pushes a personalized adjustment scheme. The fingertip wearing type spectrum sensing glove is convenient to wear, rich characteristic spectrum data can be collected according to user requirements, the intelligent terminal can quickly and accurately calculate various physiological health indexes, the intelligent terminal is not limited to traditional heart rate and blood oxygen monitoring, and the intelligent terminal can further analyze and process the physiological health indexes.

The invention discloses a user state evaluation method and system based on heart rate characteristics, and relates to the technical field of data analysis, and the method comprises the steps: collecting heart rate time sequence data; performing phase-space reconstruction on the heart rate time series data, and extracting chaotic attractor features representing heart rate complex dynamic characteristics; constructing a heart rate dynamic graph according to the chaotic attractor features, and performing feature aggregation on the dynamic graph by using a graph convolutional network to obtain global topological dynamic features; inputting the global topology dynamic feature, the attention mechanism and the user individual baseline feature into a pre-trained neural network, and outputting an intermediate state value reflecting the current state of the user; and performing adaptive state adjustment on the intermediate state value to generate a final state evaluation result. The method not only improves the precision and robustness of state evaluation, but also provides scientific and reliable technical support for intelligent health management and risk early warning.