442 results about "Saturation oxygen" patented technology

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 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 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.

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 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 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 invention belongs to the technical field of brain function monitoring, and discloses a three-in-one non-invasive brain function monitoring system and method, a medium, equipment and application, and the monitoring method comprises the following steps: monitoring brain oxygen saturation of a critical patient; carrying out transcranial Doppler ultrasonic measurement on intracranial blood vessel blood flow velocity and ultrasonic measurement on the inner diameter of the optic nerve sheath; monitoring a quantitative electroencephalogram of the patient; various brain function monitoring series indexes and continuous changes of the indexes along with disease evolution are obtained, the pathogenic mechanism and pathogenesis of the indexes are found in time, and primary diseases and related complications of the primary diseases are treated. According to the invention, the brain oxygen saturation can avoid too low brain oxygen or low brain oxygen for a long time and too high brain oxygen due to oxygen utilization disorder; cerebral blood flow can be monitored through a transcranial Doppler (TCD) technology to find an optimal craniocerebral perfusion pressure (CPP), and a cerebral blood flow self-adjusting function is enabled to be in an optimal state; the electroencephalogram can avoid early warning of over-sedation or abnormal discharge and the like. Through organic combination of the three, noninvasive, real-time and dynamic monitoring of critical patients is realized.

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 a non-inductive physical and psychological health monitoring and early warning system and method before a coal miner goes down a well, and belongs to the technical field of coal mine safety production and personnel health monitoring. A physiological monitoring unit comprises a millimeter-wave radar, an infrared camera and an imaging type photoplethysmography device, the millimeter-wave radar is installed in the center of the top of a monitoring channel, and the infrared camera is installed on the imaging type photoplethysmography device; the infrared cameras are symmetrically installed on the two sides of the channel, the imaging type photoplethysmography devices are integrated in the infrared cameras and used for synchronously collecting the core body temperature and the oxyhemoglobin saturation, and the integrated imaging type photoplethysmography devices focus on the forehead and cheek areas through the RGB channel. Reflected light intensity fluctuation signals caused by blood vessel blood volume changes are collected. Through cooperation of the three devices, non-contact synchronous collection of multiple parameters can be completed without deliberate cooperation of workers, comprehensive and continuous basic data are provided for subsequent physical and psychological health analysis, the wellhead passing efficiency is improved, and the cross infection risk of contact type devices is avoided.

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.

An apparatus for evaluating cerebral autoregulation includes a data acquisition unit configured to acquire blood pressure data and oxygen saturation data of a patient undergoing surgery, a correlation coefficient calculation unit configured to calculate correlation coefficients between the acquired blood pressure data and the acquired oxygen saturation data, a filtering unit configured to filter the calculated correlation coefficients using a moving average filter having a predetermined time window, and a cerebral autoregulation evaluation unit configured to evaluate the cerebral autoregulation of the patient undergoing surgery based on the filtered correlation coefficient.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention belongs to the field of artificial intelligence and contact type measurement, and discloses a blood oxygen saturation degree measuring method based on a smart phone camera. The method comprises the following steps: acquiring fingertip video data by using a smart phone under the condition of no external equipment, extracting a pulse photoplethysmography (PPG) signal from the fingertip video data, and constructing a multi-dimensional feature set through feature extraction and calculation; furthermore, the invention provides a two-channel deep learning model fusing a convolutional neural network and a long-short term memory network, the multi-dimensional feature set and the preprocessed PPG signal are processed in parallel, the advantages of the two networks in the aspects of feature extraction and time sequence modeling are fully exerted, and the accuracy of feature extraction and time sequence modeling is improved. And the sufficiency of the extracted features and the generalization ability of the model are ensured, so that the accurate estimation of the oxyhemoglobin saturation is realized. The research provides a feasible scheme for health self-examination in a family environment.

The invention provides a thoracic surgery respiratory training system and method, and the method comprises the steps: constructing a respiratory-physiological feature tensor through the time sequence change of a respiratory airflow parameter during respiratory training in combination with the fluctuation feature of blood oxygen saturation in the respiratory training process, and carrying out the space-time convolution fusion of the respiratory-physiological feature tensor and a chest respiratory movement track, obtaining a respiratory efficiency index; constructing a taboo risk vector based on the risk relationship map of the thoracic surgery postoperative complications and the electronic medical record of the target user, and generating a complication risk entropy of the target user under the current respiratory training according to the taboo risk vector in combination with the pain tolerance physiological data; and according to the respiratory efficiency index and the complication risk entropy, determining a dynamic adaptation margin of the target user to current respiratory training, and based on the dynamic adaptation margin, dynamically adjusting the training resistance of the self-adaptive resistance adjustment respiratory training device. By the adoption of the scheme, a multi-dimensional respiratory training feedback mechanism can be constructed to conduct self-adaptive adjustment on the training resistance.

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.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent bellyband for household respiratory rehabilitation and a management system.The intelligent bellyband and management system comprises the following steps that thoracic and abdominal movement data are collected through a flexible sensor array, and the thoracic and abdominal movement data comprise thoracic expansion amplitude, abdominal pressure change and blood oxygen saturation information; constructing an initial multi-mode signal set; according to the initial multi-mode signal set, a recurrent neural network is adopted to extract time sequence features, and dynamic feature representation is obtained and used for follow-up fusion processing; performing priority evaluation on respiratory training quality from the dynamic feature representation, and determining key signal subsets to identify potential deviations; if the key signal subset meets a triggering condition, video auxiliary analysis is carried out; the invention aims to solve the problems of multi-mode signal complexity and inaccurate real-time feedback in respiration data acquisition and analysis.

The invention relates to the technical field of health data processing, and discloses a night cardiac event early warning method based on pulse rate and blood oxygen mutation, which comprises the following steps: collecting pulse rate time sequence data, blood oxygen saturation time sequence data and original physiological signal waveform; a dynamic change vector is calculated, a personalized dynamic change vector feature library is constructed, a collaborative analysis early warning rule is established, and the rule cooperatively verifies in-library attribution of the dynamic change vector, a signal quality index of an original physiological signal waveform and accumulated duration of the dynamic change vector so as to trigger an early warning signal. According to the method, real pathological mutation, healthy physiological fluctuation, signal source pollution and transient benign anomaly can be distinguished from data processing logic, the technical problem that the false alarm rate is high due to the fact that the states are confused in an existing method is solved, and the early warning accuracy is improved.

The invention relates to a heart rehabilitation management method based on rehabilitation requirements. The method comprises the following steps: acquiring oxygen saturation time sequence data of a patient through wearable equipment, performing biological rhythm feature extraction on the data to construct a fluctuation model so as to capture a physiological cycle rule, and constructing a drug action factor based on a drug taking record to perform drug action calibration on original data so as to eliminate exogenous interference. Candidate time windows with consistent physiological statuses are screened through sliding window stability analysis; dynamic baseline calculation is performed on the candidate window based on long-term monitoring data to establish an individualized reference standard, when the dynamic baseline reaches the standard, the biological rhythm parameters and the drug action factors are fused to score and screen the time window, and finally, an individualized rehabilitation training scheme is generated in combination with physiological feature matching of a patient. Dynamic synergy of drug metabolism, physiological rhythm and historical baselines is achieved, the problem of physiological load imbalance caused by a static training plan is solved, and the rehabilitation efficiency is improved while the heart compensation risk is remarkably reduced.

The invention discloses a vein blood oxygen saturation real-time measurement system and method based on an LED light source. The method comprises the steps that 1, a pulse driving circuit outputs pulse voltage to drive LEDs with different wavelengths in a dual-wavelength LED light source to flicker alternately; collecting a photoacoustic signal by an ultrasonic transducer; 2, the ultrasonic signal processing platform receives the collected sound signals and carries out subsequent processing; 3, controlling the ultrasonic transducer to transmit an ultrasonic signal to the same measured part, and receiving an echo signal of the ultrasonic transducer; reconstructing an ultrasonic image of the measured part based on the echo signal; step 4, in the obtained ultrasonic image, positioning a vein to be analyzed and an adjacent artery thereof, and positioning the vein in the ultrasonic image; 5, the arterial oxygen saturation value of the whole body is measured through a finger clamping type pulse oximeter, and quantitative calculation of the venous oxygen saturation is achieved; and step 6, carrying out pixel-level fusion on the oxyhemoglobin saturation information and the ultrasonic image, and realizing visualization on the same frame of ultrasonic image.

The invention relates to the technical field of health data processing, in particular to a handlebar-based health data real-time analysis and prompt system, which comprises a physiological signal sensing module, a trend feature extraction module, a state level identification module, a prompt scheme selection module and a multimode channel prompt module. According to the method, a multi-source signal sequence is constructed for trend characteristic analysis through synchronous acquisition and time window alignment of four types of signals including pulse beat cycle, grip rate, blood oxygen decreasing time period and contact area frequency, the health state level is judged according to a trend cross-coincidence relation, and a corresponding prompt scheme is matched; through cooperation of the signal change trend, real-time perception and grade classification of the physiological abnormal state of the rider are realized, the pertinence and effectiveness of prompt response are improved, the precision and stability of a continuous prompt intervention mechanism are enhanced, and the problems of potential health risk identification delay and lack of personalized regulation and control of prompt are effectively solved.

The embodiment of the invention discloses a vehicle cabin oxygen supply control method and device, a vehicle and a storage medium, and the method comprises the steps: collecting image data of passengers in a vehicle cabin and pressure data of seats, and determining the types of the passengers based on the image data and the pressure data; based on the passenger type, the passenger blood oxygen saturation degree, the vehicle altitude and the cabin oxygen concentration, whether oxygen needs to be supplied to a vehicle cabin or not is judged; when the judgment result is that oxygen needs to be supplied to the vehicle cabin, determining the total oxygen demand based on the basic oxygen demand of each passenger category and the number of passengers; and oxygen is supplied to the vehicle cabin based on the total oxygen demand, so that the oxyhemoglobin saturation of the passenger is maintained in a blood oxygen reference range, and the oxygen concentration of the cabin is stabilized in an oxygen concentration standard range. According to the embodiment of the invention, the flexibility of a trigger mechanism and the oxygen supply accuracy during oxygen supply can be improved.

The invention relates to an oxygen supply device and an intelligent oxygen supply amount adjusting method, belongs to the technical field of medical equipment, and solves the problems of low resource utilization rate and poor individual comfort caused by extensive oxygen supply in the prior art. The oxygen supply device comprises a vibration sensor used for collecting weak vibration signals of a human body; the signal processing platform is used for extracting a respiration signal, a heart impact signal and related characteristics of the respiration signal and the heart impact signal in the human body weak vibration signals and calculating the blood oxygen saturation degree; regulating and controlling the oxygen supply amount and generating a corresponding regulation and control command by monitoring the change of the oxyhemoglobin saturation and the breathing signal; and the oxygen supply output device is used for adjusting the state of a valve to carry out oxygen supply output according to the regulation and control command of the signal processing platform. Through undisturbed non-intrusive real-time monitoring of vital signs, real-time intelligent adjustment of oxygen supply is achieved, the comfort of oxygen supply is improved, and energy consumption and resource waste are reduced.

The utility model discloses a blood extracorporeal circulation device, which belongs to the technical field of blood extracorporeal circulation equipment and comprises an inner tube and an outer tube, the inner tube and the outer tube are integrally nested, one end of the outer tube is connected with an air inlet tube, the air inlet tube is communicated with oxygen inlet equipment, and the other end of the outer tube is connected with an air outlet tube. A temperature changer and a flow speed controller are connected between one end, far away from the air outlet end, of the outer pipe and the oxygen inlet equipment; one end of the inner tube is a liquid inlet end for transporting blood, the other end of a blood inlet of the inner tube is a liquid outlet end, and the liquid outlet end is connected with a temperature sensor, an oxygen saturation sensor, a blood pump and a microembolus filter; the part, in the outer pipe, of the inner pipe is made of a gas exchange membrane material, and the liquid inlet end of the inner pipe is opposite to the gas inlet pipe of the outer pipe. The blood temperature rising process is safer, meanwhile, in the blood temperature rising process, the oxygen saturation index in the blood can be directly adjusted, the blood oxygen value is simple, convenient and controllable on the basis of convenient temperature rising, temperature control and oxygen saturation of in-vitro blood circulation are carried out at the same time, and operation is easy and safe.

The invention relates to the technical field of respiratory function monitoring, and discloses a pulmonary ventilation blood flow distribution real-time monitoring method and evaluation system based on a sensor. The method comprises the following steps: acquiring multi-modal data such as a blood oxygen saturation waveform, an airflow rate waveform and a thoracic pressure waveform of a lung area; performing time synchronization processing on the data to generate a time alignment sequence; dividing the data into an inspiration phase, an expiration phase and a transition phase according to a preset rule; extracting time-frequency domain features from each phase segment, and constructing a multi-dimensional respiration feature matrix; identifying an abnormal breathing mode and marking an abnormal section through breathing phase switching frequency analysis; decomposing the normal segment feature matrix by using a lung pathological feature library to generate a pathological feature vector; a pathological feature vector is optimized through a ventilation and blood flow coupling model; and carrying out abnormal region positioning based on the lung pathology propagation network, and generating a lung partition function evaluation result. The method realizes non-invasive and real-time monitoring, and is suitable for scenes such as clinical monitoring and rehabilitation evaluation.

The invention relates to the technical field of image processing, and discloses a facial health state assessment method and system based on layered optical modeling. The objective of the invention is to solve the problems of poor face health state evaluation precision and robustness caused by rough optical modeling, mixed reflection components, weak environmental adaptability and insufficient multi-dimensional health index fusion capability in the prior art. The method comprises the following steps: synchronously acquiring a user face video sequence comprising at least two different spectral bands through a multispectral imaging device; constructing a multilayer optical transmission model of the skin tissue, and separating a specular reflection component and a diffuse reflection component from the face video sequence; extracting a physiological time sequence signal related to subcutaneous blood flow activity from the separated diffuse reflection component, and calculating heart rate, heart rate variability and blood oxygen saturation parameters; and fusing the physiological parameters and expression and micro-expression features extracted from the facial video sequence, and outputting quantitative evaluation results of fatigue degree, pressure level and emotional state through a pre-trained deep neural network model. The system comprises a multispectral image acquisition module, a layered optical modeling module, a reflection component separation module, a physiological parameter inversion module and a health state evaluation module. According to the technical scheme, the signal-to-noise ratio and stability of physiological signal extraction in a complex illumination environment can be remarkably improved, the adaptability to individuals with different skin colors and dynamic illumination conditions is enhanced, and more accurate and more robust judgment of the high-order health state is achieved.

A self-regulating oxygen delivery system (100) for COPD, comprising: an oxygen source designed to provide medical oxygen; a sensor module configured to record physiological parameters such as oxygen saturation (SpO2), respiratory rate and pulse rate; a microcontroller-based control unit that is functionally connected to the sensor module and the oxygen source; and an intelligent flow control mechanism controlled by the microcontroller, the system automatically adjusts the oxygen flow in real time based on the recorded physiological parameters to maintain optimal oxygen saturation levels.

Existing pulse oximeters have impaired accuracy in measuring blood oxygen levels (SpO2) in patients of color, where the presence of melanin in the skin tends to interfere with measurements of pulse oximetry, resulting in an overestimation of SpO2. Thus, the pulse oximeter often reports a higher oxygen saturation for patients with darker skin tones, often with increasing bias as the actual saturation (SaO2) decreases. This bias is further reinforced by calibration based on individuals with light skin pigmentation, which may lead to inequitable healthcare for patients of color. Our proposed solution is to modify current pulse oximetry calculations—which utilize the relative tissue absorbance of red and infrared light to estimate SpO2—to account for the concentration of melanin by additionally measuring the skin absorbance of UV˜A light. This derived concentration of melanin can then be used to modify the pulse oximetry algorithm output, thus estimating SpO2 more accurately for patients of color.

The invention belongs to the technical field of medical instruments, and discloses an oxygen saturation monitoring type integrated multi-module textile flexible urine controller which comprises an integrated flexible textile supporting body, a flexible textile skin-friendly layer, a urine control core component and an oxygen saturation measurer. The integrated flexible textile support body comprises a double-layer fabric unit A and a double-layer fabric unit B; the double-layer fabric unit A and the double-layer fabric unit B are respectively formed by selectively penetrating part of warp yarns through weft yarns by adopting a weaving process, and each of the double-layer fabric unit A and the double-layer fabric unit B consists of an upper-layer fabric and a lower-layer fabric; the flexible textile skin-friendly layer is made by adopting a non-woven spunlace process, and the flexible textile skin-friendly layer is fixed on the inner surface of the integrated flexible textile support body; the urine control core part is arranged between the upper-layer fabric and the lower-layer fabric of the double-layer fabric unit A; and the oxygen saturation measurer is arranged between the upper-layer fabric and the lower-layer fabric of the double-layer fabric unit B. By monitoring the oxygen saturation in real time, the pressure of a patient can be adjusted to proper pressure, and tissue necrosis is avoided.