699 results about "Oxygen saturation" patented technology

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 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 discloses a surface water prediction system and method based on a hydrological optimization Informer model. The method comprises the following steps: collecting multi-source data of an upstream monitoring point and a downstream monitoring point of surface water; preprocessing the multi-source data to obtain preprocessed data; performing feature extraction on the preprocessed data to obtain water quality features and hydrological spatial-temporal features of the upstream monitoring point and the downstream monitoring point, and constructing accumulated dissolved oxygen saturation features; inputting the upstream and downstream water quality characteristics, the hydrological spatial-temporal characteristics and the dissolved oxygen saturation characteristics into a pre-trained multi-modal prediction model of an improved Informer model to obtain a prediction result of the upstream surface water and a prediction result of the downstream surface water; the multi-modal prediction model of the improved Informer model outputs a prediction result of the surface water obtained through fusion; according to the method, the Informer model is optimized, the GRU model is combined, hydrological spatial-temporal characteristics and accumulated dissolved oxygen saturation are introduced by utilizing an attention mechanism, the prediction precision is improved, errors are reduced, and the early warning capability for sudden pollution events is enhanced.

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 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 discloses portable sleep breathing disorder monitoring equipment based on electroencephalogram, oxyhemoglobin saturation and electrocardio, and belongs to the technical field of sleep monitoring equipment. The monitoring equipment comprises a wireless sleep monitoring module and a sleep breathing disorder diagnosis module, the wireless sleep monitoring module is used for collecting EEG, SpO2 and ECG signals in the sleep process and transmitting the EEG, SpO2 and ECG signals to the sleep dyspnea diagnosis module in real time for signal processing and analysis. The equipment collects multiple modes directly related to accurate diagnosis of sleep breathing disorder, and huge deviation of evaluation of sleep breathing disorder caused by a single mode is avoided. A sensor for collecting forehead electroencephalogram and oxyhemoglobin saturation is integrated on the flexible circuit board, so that the integration level and portability of the device are higher. A multi-modal fusion algorithm is designed for multi-modal signals collected by equipment, and multi-task and accurate detection of automatic sleep staging and sleep breathing events is achieved.

A photoplethysmogram system and method are disclosed. The system includes at least three light sources of different frequencies configured to emit light into a subject's skin. A PPG sensor detects the light from the multiple light sources after it has passed through the subject's skin and generates a PPG signal for each light source. A processor, communicatively coupled to the PPG sensor, receives the PPG signals and estimates physiological parameters from each PPG signal. The light sources may include a red light source, an infrared light source, and a green light source, with the green light source used for calibration in the measurement of blood oxygen saturation. The processor may dynamically perform amputation or imputation of the PPG signals based on the consistency of the signals from the different light sources. The physiological parameters may include respiratory effort and pulse rate and pulse rate variability.

The invention belongs to the technical field of physiological state estimation, and discloses a blood oxygen estimation method based on combination of successive variational mode decomposition and adaptive filtering, which comprises the following steps of: removing high-frequency noise and baseline drift from a collected pulse wave signal; performing successive variational mode decomposition and adaptive filtering on the pulse wave signal to remove motion artifacts; accurate peak values and valley values are extracted according to the denoised pulse waves; the R value is calculated according to the denoised pulse wave and the extracted peak value and valley value, the blood oxygen saturation degree is solved according to the R value, and the R value refers to the absorbance ratio of the red light and the infrared light in the blood. According to the blood oxygen estimation method based on the combination of the successive variational mode decomposition and the self-adaptive filtering, the effect of removing motion artifact noise of photoelectric volume pulse wave signals is achieved, multi-peak-value joint detection is used, peak values and valley values are accurately extracted, and therefore the R value is determined, the blood oxygen saturation degree is obtained, and the accuracy of blood oxygen estimation is improved. And the precision of estimating the blood oxygen saturation is greatly improved.

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 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 control method of an oxygen generation system and an adjustable oxygen generation system, and belongs to the technical field of medical instruments. The control method of the oxygen generation system comprises the steps that 1, the threshold value interval of a pulse rate normal value and the threshold value of an oxyhemoglobin saturation normal value are obtained; 2, receiving a pulse rate monitoring value and a blood oxygen saturation monitoring value of a user of the oxygen generation system; 3, according to the threshold value interval, the threshold value, the received pulse rate monitoring value and the received oxyhemoglobin saturation monitoring value, the oxygen output quantity of the oxygen generation system is controlled, and the oxygen output quantity is the oxygen generation pressure and / or the oxygen output rate. According to the method, the performance parameters of the oxygenerator can be timely and automatically adjusted according to the physiological parameter change of the user of the oxygenerator, so that the requirements of the user at the current moment are met.

The invention discloses a child health information tracking management method and system for a kindergarten institution, and relates to the technical field of kindergarten information management. According to the child health information tracking management method for the kindergarten institution, basic information and health data of each child are collected, the health condition of each time period is evaluated, a time period health index is generated, whether the time period health index of each child is within a normal range or not is judged in real time according to a preset health threshold value, and if the index is abnormal, the kindergarten institution is started. The method comprises the following steps of: immediately marking health abnormity, comprehensively analyzing health, intake, activity and interaction data of the whole day at the end of each day, analyzing daily health comprehensive indexes of each infant, performing health assessment, and taking corresponding health intervention measures according to a result. By continuously tracking and analyzing key health indexes such as the body temperature, the heart rate, the respiration rate and the blood oxygen saturation degree of the infant, the health index of each time period is generated in real time, and a young supporting institution is allowed to recognize potential health risks in real time in the initial stage of the problem.

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 oxyhemoglobin saturation detection method and device, noise reduction equipment, a storage medium and a product, and relates to the technical field of artificial intelligence. The oxyhemoglobin saturation detection method comprises the steps that an initial pulse wave signal collected by photoplethysmography detection equipment is received, and whether a noise signal exists in the initial pulse wave signal or not is judged; if the noise signal exists in the initial pulse wave signal, the initial pulse wave signal is input into a noise reduction model to obtain a target pulse wave signal, and the noise reduction model is obtained through training by taking a historical pulse wave signal as input data and taking the denoised historical pulse wave signal as a label; the target pulse wave signal is sent to the photoplethysmography detection equipment, and the photoplethysmography detection equipment carries out blood oxygen concentration detection based on the target pulse wave signal. The accuracy of blood oxygen saturation detection is improved.

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.

The invention relates to the technical field of data processing, in particular to a clinical test data analysis method and system. The method comprises the following steps: presetting observation ranges of blood oxygen saturation data points in a blood oxygen saturation time sequence of a human body and respiratory frequency data points in a respiratory frequency time sequence; eliminating variation coefficient deviation before and after a blood oxygen saturation data point in an observation range of the blood oxygen saturation data point to obtain a mutation index, and obtaining a mutation factor of the blood oxygen saturation data point according to the mutation index and data variation of all adjacent blood oxygen saturation data points in the observation range; calculating false factors of the blood oxygen saturation data points; a corrected target threshold value is obtained through the product of the false factor of the blood oxygen saturation degree data point and a preset threshold value; and the target threshold is used in the Chebyshev inequality to obtain the analysis result of the blood oxygen saturation data point, so that the accuracy of the analysis result is effectively improved.

In the breathing state evaluation model training and breathing state evaluation method and system, breathing-related physiological parameter data is collected; the physiological parameter data comprises a blood oxygen saturation numerical value, a heart rate numerical value, a breathing carbon dioxide concentration numerical value and a breathing rate numerical value; interpreting the physiological parameter data, and giving a breathing state evaluation label corresponding to the physiological parameter data; the breathing state evaluation label comprises an IPI index; inputting the physiological parameter data and the breathing state evaluation label into an artificial intelligence training device, and training by the artificial intelligence training device to obtain a breathing state evaluation feature data set; the breathing state evaluation feature data set is matched with a corresponding artificial intelligence algorithm and is used for breathing state evaluation. The breathing state comprehensive evaluation can be directly carried out based on the complete waveform signal, and the IPI index is closer to reality.

A portable, multimodal dental diagnostic device for assessing the vitality of the dental pulp, consisting of: a detachable diagnostic component comprising a variety of probes, including the following: a heat probe configured to deliver controlled heat stimuli to a tooth surface and measure the heat response of the dental pulp; an electrical probe configured to deliver low-voltage electrical pulses to the tooth surface and measure the electrical impedance of the dental pulp; and an optical probe configured to assess blood oxygen saturation and microcirculation in the dental pulp using near-infrared light; a microcontroller unit configured to process sensor data from the thermal probe, the electrical probe and the optical probe, to calibrate measurements based on the oral base temperature and the ambient conditions, to aggregate multimodal sensor data and to control the operation of the thermal probe, the electrical probe and the optical probe; a cloud platform configured to receive and store aggregated sensor data, run artificial intelligence algorithms to analyze multimodal sensor data and generate diagnostic insights that include vital signs and diagnostic probabilities; a user application that is communicatively connected to the cloud platform and configured to wirelessly receive aggregated sensor data, display detailed diagnostic reports with vital signs and trends to the user, integrate with electronic health data systems, and issue alerts in case of abnormal readings; a wireless communication module connected to the microcontroller unit, configured to transmit the aggregated sensor data to the cloud platform; a display interface configured to show real-time feedback from the probes, including vitality assessments and sensor readings; and a main body containing the microcontroller unit, a rechargeable power supply and the display interface, wherein the rechargeable power supply can be charged via the current provided through a charging port on the main body, and wherein the main body is also equipped with on / off and start / stop switches; the device is configured to provide a non-invasive, quantitative real-time analysis of dental pulp health by combining physiological responses from thermal, electrical, and optical measurements.

The invention discloses an oxygen therapy instrument control system and method and a storage medium, and the system comprises an oxygen saturation obtaining module which is used for obtaining the current blood oxygen saturation value of a user; the air and oxygen flow control module is used for controlling the mixed air and oxygen flow; the oxygen concentration control module is used for controlling the concentration of oxygen in the air-oxygen gas; and the analysis control module is used for inputting the initial air-oxygen flow and the oxygen concentration, and carrying out stepped control adjustment on the air-oxygen flow and the oxygen concentration according to the current blood oxygen saturation value of the user, so that the equipment parameters of the oxygen therapy instrument gradually approach to the body characteristics of the user. Gradient adjustment and real-time calculation can be carried out, adjustment is accurate, intelligent regulation and control are achieved, it is guaranteed that the oxygen therapy instrument is suitable for the physical condition of a user in real time, real-time manual adjustment is not needed, and discomfort of the user or oxygen waste caused by inapplicable parameters is also avoided.

An apparatus for monitoring an oxygen saturation level of a wearer of the apparatus includes a processor, a memory operably coupled to the processor, a first housing portion, a second housing portion, and a connection member. The first housing portion includes at least one light-emitting diode (LED), and the second housing portion includes a photodetector. The connection member is mechanically coupled to each of the first housing portion and the second housing portion. The apparatus is sized and shaped to be worn about a portion of an ear of a wearer of the apparatus. During operation, the at least one LED emits light in a direction toward the photodetector. A portion of the emitted light passes through the portion of the ear prior to arriving at the photodetector. The photodetector detects a signal in response to the portion of the emitted light, and the memory stores instructions to cause the processor to calculate an oxygen saturation level of the wearer based on the detected signal.

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.

Provided in the present invention is a flexible hybrid electronic system for intraoperative intracranial monitoring. The system comprises a flexible front-end signal collection module, a rear-end signal processing module and an upper computer system, wherein the flexible front-end signal collection module comprises a flexible framework body, which is formed by a top encapsulation layer, an intermediate encapsulation layer and a bottom substrate layer from top to bottom, an electroencephalogram layer and a flexible sensor array, which are arranged in the flexible framework body, and a temperature-controlled hydrogel interface layer and an electrothermal film, which are respectively integrated above and below the flexible framework body, the flexible sensor array being used for collecting pressure, temperature and brain tissue oxygen saturation signals, and the electrothermal film regulating the adhesion and peeling of the temperature-controlled hydrogel interface layer and brain tissue; and the rear-end signal processing module is electrically connected to the flexible front-end signal collection module, so as to receive signals collected by the flexible sensor array and the electroencephalogram layer, analyze and process the signals, and then transmit the signals to the upper computer system. The flexible hybrid electronic system can both replace traditional brain patties and monitor the state of brain tissue throughout the process, thereby ensuring intraoperative safety.

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 relates to a brain blood oxygen monitoring method and device, and relates to the technical field of biomedical engineering.The method comprises the steps that a light attenuation signal generated when near-infrared light penetrates through the brain and a motion signal of the brain are synchronously collected, the motion signal and the light attenuation signal are coupled, and an infrared light-acceleration coupling signal is formed; preprocessing the infrared light-acceleration coupling signal to obtain a pure coupling signal; filtering the pure coupling signal through a quality evaluation mechanism to obtain an effective coupling signal; the effective coupling signals are input into a dual-wavelength ratio model, and the percutaneous blood oxygen saturation is calculated by fusing an experience calibration equation. By improving the algorithm, the accuracy of near-infrared brain blood oxygen monitoring can be effectively improved, and the signal error caused by head shaking can be reduced in combination with the motion monitoring function of the three-axis acceleration sensor.