913 results about "Saturation oxygen" patented technology

The invention relates to a blood oxygen and heart rate monitoring system and method based on PPG signals and motion data, and relates to the technical field of medical health and intelligent wearable equipment. The system comprises a PPG signal acquisition module, a motion sensor module, a signal preprocessing module, a dynamic signal segmentation and quality evaluation module, an oxyhemoglobin saturation calculation module, a heart rate calculation module, a motion state recognition module, a self-adaptive Kalman filtering module and an output display module. The method and the device have the effects of realizing a function of identifying the motion state by the wearable equipment, improving the signal interference suppression capability and widening the application scene.

The invention discloses a perioperative hypothermia prediction and prevention system based on multi-modal data and deep learning. The system integrates skin temperature, physiological parameters (such as heart rate, blood pressure and oxyhemoglobin saturation) and operating room environment data (temperature, humidity, airflow and the like), and adopts a deep learning method (CNN-LSTM model, Transformer model and multi-modal fusion model) to carry out data analysis and hypothermia prediction. The system comprises a data acquisition module, a data processing module, a deep learning prediction module and a personalized temperature control module, can realize real-time low body temperature early warning, and provides a personalized intraoperative temperature control strategy. Experimental results show that the system abandons complexity, invasiveness and hysteresis of traditional hypothermia monitoring, hypothermia occurrence can be predicted 20-30 minutes in advance, and the accuracy of intraoperative management is improved. The method can be widely applied to the fields of anesthesia monitoring, operating room intelligent management and perioperative patient body temperature optimization control.

The invention provides an early-stage automatic identification method and system for acute kidney injury related to cardiac surgery, and relates to the technical field of physiological parameter detection. The method comprises the following steps: acquiring average arterial pressure, cardiac blood discharge volume, central venous pressure and blood oxygen saturation, regularly recording and extracting data of adjacent time points, calculating a variation range, screening a mutation region, judging whether a reference range is exceeded or not, analyzing a multi-parameter synchronous variation condition, and marking a hemodynamics abnormal state. According to the method, abnormal changes of hemodynamics and urine metabolism are accurately recognized through real-time monitoring and multi-parameter analysis, the urine flow rate and the metabolite excretion rate are combined, misjudgment of a single index is avoided, the fineness of renal function change analysis is improved, the high-risk time period is accurately positioned through time cross comparison, the diagnosis accuracy is improved, and the diagnosis accuracy is improved. Trend analysis and risk grade division enable the change of the illness state to be more visual, and a real-time early warning system effectively monitors the fluctuation trend, optimizes clinical management and improves the timeliness of medical response.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention discloses a portable respiratory function rehabilitation training device and a method thereof, and belongs to the technical field of medical rehabilitation. The device comprises a data acquisition module which integrates a plurality of micro sensors to acquire respiratory airflow, heart rate, oxyhemoglobin saturation and environmental parameters of a user in real time; the data preprocessing and fusion module is used for cleaning, standardizing and performing time synchronization on the collected original data and fusing the collected original data into a uniform data format; the intelligent analysis module is responsible for performing real-time analysis on the fused data, identifying a breathing mode, evaluating a current health state and predicting a rehabilitation trend; the training scheme generation module is used for dynamically generating and adjusting a personalized breathing training scheme; the real-time feedback and interaction module is used for providing real-time training guidance and progress feedback for the user; the effect evaluation and optimization module is used for evaluating the rehabilitation effect and continuously optimizing a training strategy by using a reinforcement learning algorithm; according to the portable hardware integration module, all modules are integrated into small-sized and low-power-consumption portable equipment.

The invention relates to the technical field of sewage treatment, in particular to an aerobic aeration treatment system for plateau domestic sewage treatment, which comprises a pretreatment unit, a biological treatment unit, an advanced treatment unit and a recycling unit which are connected in sequence, the system further comprises a data acquisition module, an aerobic aeration intelligent regulation and control module, a biochemical environment constant-temperature maintenance module, a carbon-nitrogen proportion and backflow dynamic regulation and control module, a digital twinning and sludge characteristic simulation module and an energy collaborative scheduling module. Compared with the prior art which adopts a standard aeration strategy and cannot adapt to the special environment of low air pressure and low oxygen partial pressure of the plateau, the system can accurately adapt to the plateau environment by integrating real-time atmospheric pressure and water temperature monitoring, dynamically calculating the oxygen saturation solubility and correcting the aeration control target according to the oxygen saturation solubility, so that the aeration control target is optimized. Stable supply of dissolved oxygen required by biochemical reaction is ensured, and the technical bottleneck of low efficiency of traditional sewage treatment in plateau regions is fundamentally overcome.

The invention relates to the technical field of blood oxygen detection, in particular to a pulse blood oxygen saturation detection method based on image processing, a transfer learning strategy and a domain adaptation network are introduced, adjustment can be carried out according to personalized physiological parameters of a user, and therefore the applicability to different crowds is improved. Especially for people with deep skin color, the measuring error is obviously reduced, and the error rate is reduced by more than 30%. A set of mechanism for calculating the current signal quality index Q in real time is designed, dynamic calibration is carried out in combination with a Bayesian filtering model, and it is ensured that a reliable blood oxygen saturation degree estimation value can be obtained even under the condition of the low signal-to-noise ratio. And when the signal quality is reduced, the current detection value is compensated by using historical data through the LSTM network prediction module, so that the accuracy and continuity of the result are further ensured.

The invention provides a vehicle-mounted oxygen production control method and device, a vehicle-mounted oxygen production system and electronic equipment, and relates to the technical field of vehicle control. The method comprises the steps that the oxygen partial pressure in a cockpit, the altitude of the environment where the cockpit is located and the oxyhemoglobin saturation of passengers in the cockpit are obtained; determining an oxygen supply demand value based on the oxygen partial pressure, the altitude and the oxyhemoglobin saturation; and based on the oxygen supply demand value, an oxygen generation module is controlled to start oxygen generation or stop oxygen generation. The starting and stopping of the oxygen generation module are controlled based on the oxygen supply demand value, waste caused by starting of the oxygen generation module when oxygen supply is not needed can be avoided, and therefore the energy consumption of vehicle-mounted oxygen generation can be greatly reduced.

The invention discloses a sleep monitoring method and system based on a non-contact video data sequence. The method comprises the steps that S1, video data streams of human body sleep are collected through a camera system; utilizing a YOLOv11 network to obtain face video data and thoracoabdominal video data; s2, the RPPG signal extraction model extracts and obtains an RPPG signal by using the face video data; s3, the breathing signal extraction model extracts thoracic and abdominal micro-motion change characteristics in the thoracic and abdominal video data by using an optical flow method, and noise filtering processing is carried out to obtain thoracic and abdominal motion signals as breathing signals; s4, the oxyhemoglobin saturation extraction model detects and outputs an oxyhemoglobin saturation signal by using the RPPG signal; and S5, performing multi-modal fusion analysis on the multi-physiological index fusion recognition model according to time slice T1 division to obtain a long-time-sequence sleep stage staging result. According to the invention, the physiological index signals are extracted and recognized by adopting the non-contact video data sequence, and the sleep stage staging result with a long time sequence is obtained, so that high-precision sleep monitoring and sleep stage recognition are realized.

The invention relates to a multi-parameter integration and AI fusion postpartum hemorrhage intelligent monitoring, prevention and control method and system. The method comprises the following steps: acquiring blood flow data; the blood flow data comprises blood loss volume and blood flow velocity, and the blood flow data is obtained through measurement of an optical fiber sensor; acquiring vital sign information; the vital sign information comprises blood pressure, heart rate and oxyhemoglobin saturation, and the vital sign information is obtained through continuous monitoring of a vital sign patch; according to the blood flow data, the vital sign information and the medical history information, a bleeding risk grade evaluation result is calculated in real time through an AI risk evaluation model, and the AI risk evaluation model is used for dynamically evaluating the bleeding risk; and triggering corresponding grading early warning according to the bleeding risk grade evaluation result, wherein the grading early warning is used for indicating bleeding risk processing. By adopting the method, the early warning accuracy and treatment efficiency of postpartum hemorrhage can be remarkably improved, and complications and death risks of pregnant and lying-in women are effectively reduced.

The invention relates to a cardiac arrest risk grade prediction method and system based on a fuzzy logic method, and the method comprises the steps: obtaining real-time vital sign data of a patient, the real-time vital sign data comprising at least one of heart rate, blood pressure, oxyhemoglobin saturation and respiratory rate; on the basis of the real-time vital sign data, feature selection and importance evaluation processing are carried out through a machine learning algorithm, and target features most related to the sudden cardiac arrest risk are obtained; performing fuzzification processing based on fuzzy C-means clustering on the basis of the target features so as to convert the accurate numerical data into a fuzzy set; according to a fuzzy logic principle, in combination with a case-based reasoning method, performing matching calculation on the fuzzy set and known sudden cardiac arrest case characteristics to obtain a sudden cardiac arrest risk level; based on the cardiac arrest risk level, risk level evaluation is carried out, and a corresponding early warning mechanism is triggered. The implementation of the method can improve the accuracy and comprehensiveness of prediction.

The invention relates to the technical field of anesthesia patient physiological monitoring, in particular to an anesthesia preoperative risk assessment method and system, which continuously acquire physiological parameters of an anesthesia patient in a resting state, monitor physiological parameter changes in a dynamic physiological load state, screen physiological parameters exceeding a steady state range and generate a preoperative physiological monitoring data set. According to the method, the individual physiological steady state range is set through data comparison between the resting state and the dynamic physiological load state, accurate screening of parameters such as the heart rate, the blood oxygen saturation degree and the blood pressure is achieved, and the monitoring accuracy is improved. In combination with a load state fluctuation value, a resting mean value and a weight parameter, an individual physiological regulation range is calculated, so that evaluation is more personalized. And the preoperative risk adjustment coefficient is dynamically calculated, so that the risk assessment has the real-time adjustment capability. The heart rate recovery time, the blood oxygen fluctuation amplitude and the blood pressure recovery time are introduced, the physiological load ratio and dynamic load index classification are combined, and the risk assessment fineness is improved.

The invention relates to the technical field of medical diagnosis, and discloses a blood oxygen monitoring system fused with sleep rhythm modeling, which comprises the following steps: identifying a sleep stage conversion moment through a photoelectric volume pulse wave signal, detecting a blood oxygen saturation valley value in a window after conversion, and calculating response lag duration; according to the method, the physiological rhythm is converted into a natural probe for the compensatory ability of the respiratory system, dual diagnosis of sleep staging and respiratory function is realized through a single-path signal, and meanwhile, body movement interference is converted into an observation window for the respiratory stress recovery ability. The diagnosis reliability in a home monitoring scene is remarkably improved, the system completes core analysis at an edge node, and a respiratory function evaluation report which can be directly used for clinical decision making is output.

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 medical instruments, and discloses an intelligent device for monitoring the cardio-pulmonary function of a heart failure patient, the intelligent device comprises a multi-mode sensor module, an AI analysis module, a biological feedback module and a wearable carrier, the multi-mode sensor module is used for collecting heart rate, respiratory rate, oxyhemoglobin saturation and body movement data in real time, and the AI analysis module is used for analyzing the heart rate, the respiratory rate, the oxyhemoglobin saturation and the body movement data; monitoring modes can be dynamically switched in rest, motion and sleep scenes; the AI analysis module is used for detecting abnormity based on real-time data and predicting a heart failure deterioration risk in combination with historical data; and the biological feedback module is used for providing personalized intervention in a vibration or audio mode according to the analysis result. The heart rate, the respiration rate, the blood oxygen saturation and the body movement data are collected at the same time through the multi-mode sensor module, sleep apnea or periodic respiration is recognized in combination with the AI analysis module, the defect of separation of cardiopulmonary function monitoring and sleep mode analysis in the prior art is overcome, and therefore early intervention and treatment adjustment are supported.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention discloses an adjustable blood transfusion reaction monitoring system. The system comprises a multi-mode physiological signal acquisition module, a data processing and analysis module, a user interaction and parameter configuration interface, a wireless communication module, an alarm and feedback module and a power management module. By integrating an electrocardiogram (ECG) sensor, an oxyhemoglobin saturation (SpO2) detection probe, a non-invasive blood pressure measuring device, a body temperature sensor, a respiratory rate monitor and a skin impedance change sensor, real-time acquisition of various physical parameters is realized; a rule-based threshold judgment model and a time sequence prediction model are built in the data processing and analysis module, and the data processing and analysis module is used for identifying potential blood transfusion reaction risks; and the user interaction and parameter configuration interface provides a touch display screen and remote terminal equipment to realize information input, data display and operation recording. Continuous dynamic monitoring, intelligent early warning and graded response in the blood transfusion process are achieved, blood transfusion safety and nursing efficiency are improved, and the system has wide clinical application prospects.

A wearable device for comprehensive bio-monitoring of physiologic metrics to determine metabolic, pulmonary and cardiac function and oxygen saturation measurements from breathing mask apparatuses. The device non-invasively monitors the physiologic profile of the subject, and is capable of detecting physiologic changes, predicting onset of symptoms, and alerting the wearer or another person or system. In some embodiments, the device comprises both a wearable sensor suite and a portable gas composition and flow analysis system. In preferred embodiments, it comprises a miniaturized non-invasive sensor suite for detecting physiologic changes to detect dangerous breathing or other health conditions. The acquired physiologic profile is used to generate alarms or warnings based on detectable physiological changes, to adjust gas delivery to the subject, alter mission profiles, or to transfer control of the craft or other duties away from a debilitated subject. The device is compact, portable, vehicle independent and non-encumbering to the subject.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention discloses a multi-mode postoperative child pain identification method. The method aims at improving the accuracy of child postoperative pain recognition by fusing ECG (electrocardiogram), SPO2 (blood oxygen) and EEG (electroencephalogram) multi-mode data. The method comprises the following steps of: firstly, respectively acquiring and preprocessing each modal data, and converting EEG (electroencephalogram) into two types of two-dimensional frames; then, feature extraction is automatically conducted on the data of all the modes through specific neural networks including the frequency domain flow neural network, the time domain flow neural network, the oxyhemoglobin saturation CNN-GRU neural network and the electrocardio CNN-GRU neural network; and finally, inputting the extracted features into an MLP feature fusion layer to carry out pain identification. Compared with a traditional single-mode identification method, the postoperative child pain related characteristics can be more comprehensively captured, the pain identification accuracy is effectively improved, and powerful support is provided for medical staff to accurately evaluate the postoperative child pain degree in time.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention discloses an intelligent blood oxygen saturation measuring method and device and electronic equipment, and relates to the field of data processing. According to the method, an infrared light emitter is used for emitting a plurality of emitted light rays with different wavelengths; receiving reflected light rays corresponding to the emitted light rays by utilizing a photoelectric receiver, and converting the reflected light rays into photoelectric signals; extracting a time domain characteristic parameter and a frequency domain characteristic parameter of the photoelectric signal; inputting the time-domain characteristic parameters and the frequency-domain characteristic parameters into a preset blood oxygen saturation degree estimation model to obtain a blood oxygen saturation degree estimation value of the to-be-measured individual; and acquiring historical detection data of the to-be-detected individual, and correcting the blood oxygen saturation degree estimated value by adopting a Kalman filtering algorithm based on the historical detection data to obtain a final blood oxygen saturation degree estimated value. By implementing the technical scheme provided by the invention, the accuracy of blood oxygen saturation measurement is improved.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

The embodiment of the invention provides a continuous blood oxygen monitoring system in extracorporeal circulation and a control method thereof. The continuous blood oxygen monitoring system comprises a vein sensor, an artery sensor, a blood flow sensor, an oxygen sensor, a carbon dioxide sensor, a controller and a display, the vein sensor monitors the vein blood oxygen saturation degree; the artery sensor monitors arterial oxygen saturation and hematocrit; the blood flow sensor monitors the blood flow of the artery pipeline; the oxygen sensor monitors the oxygen concentration of the oxygenator; the carbon dioxide sensor monitors the carbon dioxide concentration of the oxygenator; the display provides an interaction control, responds to a trigger operation acting on the interaction control, sends an interaction instruction to the controller, and updates information displayed in an interaction interface according to the control of the controller; the controller receives the interaction instruction and executes processing corresponding to the interaction instruction; the processing includes controlling display information of the display; the display information comprises information corresponding to the monitoring parameters. The scheme ensures the safety and effectiveness of the treatment process.

The invention discloses a sudden physiological danger detection method and system used in a plateau sleep scene, belongs to the technical field of medical health monitoring, and solves the problems that existing PSG equipment is expensive and complex in operation, needs professional personnel to carry out data analysis and is not suitable for long-term or large-scale monitoring in a plateau environment. The method comprises the steps of collecting physiological monitoring data in real time by adopting bracelet detection equipment, performing feature extraction on a preprocessed data set, performing blood oxygen, heart rate and sleep apnea detection on a feature set, performing sudden physiological danger detection based on a Transform model, and outputting a sudden physiological danger detection result. According to the embodiment of the invention, PPG and 6-axis IMU data are utilized, a deep learning algorithm is combined, the detection accuracy of the sleep apnea event is remarkably improved, compared with a traditional PSG method, false alarm and missing alarm are reduced, and meanwhile, through a multi-task learning strategy of a Transform model, prediction of the blood oxygen saturation degree and the heart rate and detection of sleep apnea are optimized.

This disclosure presents a method for reducing the time required to initiate an alert on a suspected acute myocardial infarction (AMI) and improving the accuracy and reliability of the alert. The method involves acquiring and processing physiological signals, including multi-lead ECG and PPG signals captured using a wrist-worn device equipped with biooptical sensors and biopotential electrodes. These signals are utilized to extract both static and dynamic AMI features, such as ST segment abnormality index, QRS-T angle, the occurrence of bundle branch block, ECG signal quality, reduction in oxygen saturation, the number of ventricular premature beats, the occurrence of ventricular arrhythmias, and PPG signal quality index. These extracted AMI features are then employed as inputs to train a reservoir computing model, specifically an echo state neural network, using individualized data. The model computes an alert score, and if it surpasses a predefined threshold, an alert for suspected AMI is generated.

The present invention relates to a system (100) for monitoring one or more physiological parameters of an individual. The system (100) comprises an optical fibre assembly (110) configured to measure one or more physiological parameters of an individual, and a pressure sensor (120) configured to measure a contact pressure of the optical fibre assembly (110) on the individual. The pressure sensor (120) comprises an optical fibre (122) comprising a transducer fibre Bragg grating (127) embedded in a matrix (121). The matrix (121) is configured to cause longitudinal strain in the transducer fibre Bragg grating (127) in response to the matrix (121) being subject to a transverse load. The one or more physiological parameters that the system (100) is for monitoring may include blood oxygen saturation (Sp O2), capillary refill time (CRT), heart rate, blood flow and CO2 emissions from skin.

The invention discloses an exercise health monitoring method and system of a smart watch, and relates to the technical field of smart watches, and the method comprises the steps: carrying out the time synchronization of continuous data, and enabling all monitoring indexes to be aligned on the same time axis; carrying out real-time analysis and anomaly detection on the heart rate and blood oxygen saturation data by utilizing a machine learning algorithm; the heart rate variability index of the user is evaluated, the blood oxygen saturation data and the body surface temperature data are integrated, and a pressure evaluation model is constructed; in combination with height and weight information input by the user, constructing a deep learning model to identify a motion mode of the user; the heart rate and blood oxygen health indexes of the user are tracked and analyzed by utilizing a time sequence analysis technology, the future health trend is predicted, and potential health problems are explored. By arranging the data prediction module, the heart rate, blood oxygen and other health indexes of the user can be tracked and analyzed, the future health trend is predicted, and potential health problems are explored.