108 results about "Breath rate" patented technology

The present application discloses a multifunctional health monitoring method and system based on a millimeter wave radar, and a computer readable storage medium. The method comprises: receiving an echo signal in a space, and on the basis of the echo signal, calculating point cloud data of a user and phase change data caused by the respiration and heartbeat of the user; calculating behaviors of the user on the basis of the point cloud data; calculating vital signs of the user on the basis of the phase change data, wherein the vital signs comprise respiration rate and heart rate; and on the basis of the behaviors and / or the vital signs, determining that the user triggers a set alarm event, and on the basis of the type of the triggered alarm event, performing a corresponding alarm operation.

The invention relates to the technical field of health assessment, in particular to a newborn health assessment method and system based on visual analysis, and the method comprises the following steps: obtaining a newborn image frame sequence, extracting a hue curve, recognizing a variation region to generate a layer, and extracting a mutation region bitmap group in combination with a heart rate RR interval difference value; and analyzing an included angle mapping grid between the center of gravity of the pigment and the heart rate slope, superposing a respiratory rate curvature, performing co-occurrence clustering to form a trend graph block, and evaluating a risk distribution interface formed by joint mutation. According to the method, a dynamic coupling relation between an image and a physiological signal is established through slope direction angle change and included angle deviation, cross-dimension mapping of heart rate trend change and visual feature change is achieved, the heart rate, skin color tracks and curvature change of respiratory frequency are fused, a trend gathering map is constructed, and the dynamic coupling relation between the image and the physiological signal is obtained. The risk judgment and display are completed according to the joint grading of the block density, the time span and the parameter quantity in the atlas, and the health level fluctuation trend is visually presented in the risk evolution sorting.

The invention relates to the technical field of health data monitoring, in particular to a health monitoring method based on data analysis and a smart watch, and the method comprises the steps: obtaining the physiological parameters of heart rate, blood pressure, blood oxygen and respiratory rate of a user, analyzing the interaction among the physiological parameters, judging whether the physiological parameters have the imbalance of abnormal increase or decrease or not, and sending the judgment result to the smart watch; and obtaining an individual health state distribution result. According to the invention, based on dynamic analysis of physiological parameters, in combination with multi-dimensional data measurement and calculation, the change characteristics of the health state are accurately described, and errors caused by single data point judgment are avoided. By measuring and calculating the interaction among different physiological parameters, the health state can be evaluated more comprehensively, and it is ensured that judgment of the individual health state is not affected by fluctuation of a single physiological index. The distance between the health state and the steady state interval is calculated by means of the multi-dimensional coordinate space, so that the evaluation of the health state change is more quantitative, and the identification capability of abnormal fluctuation is improved.

The invention provides a heart rate and breath prediction method, a program product, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original signal sequence acquired from a human body through an ultra-wideband radar; determining a thoracic cavity displacement signal according to a mapping relation between the phase change and thoracic cavity displacement; performing multi-domain feature extraction on the thoracic cavity displacement signal to obtain multi-domain features; the multi-domain features comprise time domain features, frequency domain features and time-frequency domain features; on the basis of the multi-domain features, prediction values are obtained through a pre-trained prediction model, and the prediction values comprise the heart rate and the respiratory rate in the future time period. The robustness of the algorithm is enhanced through multi-dimensional feature fusion, high precision is still kept in an electromagnetic interference or multi-target scene, and the reliability of heart rate and respiratory rate prediction in a complex environment is improved. The physical signal advantage of the ultra-wideband radar is complementary with the environmental adaptability of the neural network, and the prediction accuracy of the heart rate and respiratory rate is improved.

The invention relates to a multi-mode human body respiration prediction method and system. The method comprises the steps that a difference value time sequence T is obtained through a visible light video and a thermal infrared video, and then the respiration frequency f1 of a target human body is obtained; collecting an electrophysiological signal containing a surface diaphragm electromyogram from the thoracic skin surface by means of an electromyographic signal collection system, and processing the electrophysiological signal to obtain a separated diaphragm electromyographic signal E; the heart rate f2 of the target human body is obtained through an electrocardiosignal collecting system; acquiring an environmental parameter Y of the target human body by using an environmental parameter acquisition system; the parameters obtained in the step S1-4 are processed, and a breathing state evaluation model is built; and S4, optimizing and constructing a BP neural network, and predicting the respiratory movement state of the human body by matching the sample data obtained in the step S5 with the BP neural network.

A smart object may be used to monitor physiological parameters of a user. The object has at least one capacitive sensor to sense a change in capacitance when a tissue of the user comes into contact with the at least one capacitive sensor. The change in capacitance can be used to detect physiological parameters of a user such as heart rate, inter-beat interval and respiratory rate. The smart object may also be used with another smart object to determine the identity of the user or other physiological parameters of the user such as blood pressure.

The invention discloses a vital sign monitoring system and method based on a multi-sensor helmet, and relates to the technical field of safety helmets.The vital sign monitoring system comprises a data acquisition module, the data acquisition module comprises a sensor unit and a data acquisition unit, and the sensor unit specifically comprises a heart rate sensor used for monitoring the real-time heart rate of a wearer in real time; the body temperature sensor is used for monitoring the body surface temperature of the wearer in real time; the respiratory rate sensor is used for monitoring the respiratory rate of the wearer in real time; according to the latest sensor data, the dynamic base line value is recalculated according to the calculation method, so that the dynamic base line can adapt to the change of the physiological parameters in time, accurate reflection of the current physiological state is always kept, and if the physiological parameters of the wearer change in a short time, the wearer can be prevented from being damaged by the dynamic base line. The changes can be taken into consideration in time, and the dynamic baseline is updated, so that more accurate reference is provided for subsequent physiological status monitoring and analysis.

The invention discloses a data processing method and device for an electronic injury condition sheet. The method comprises the following steps: acquiring first data information and first position information; the first data information comprises an injury condition evaluation value, injury condition state information, timestamp information and second position information; the injury state information comprises heart rate information, respiratory rate information and blood pressure information; preprocessing the first data information to obtain preprocessing result information; and performing fusion processing on the preprocessing result information, the first data information and the first position information to obtain target processing result information. Visibly, the method is beneficial to analyzing and processing the data information of the electronic injury condition sheet, so that the rescue priority can be set more accurately and efficiently, and the accuracy and timeliness of rescue decision making can be remarkably improved.

The utility model discloses a chest pressurizing strap with a monitoring function, and belongs to the technical field of medical auxiliary tools. Comprising a pectoral girdle body, shoulder girdles, an intelligent monitoring unit and a control terminal, the shoulder girdles are elastic girdle bodies with adjustable lengths, pressurizing air bags are arranged on the pectoral girdle body and comprise a first air bag, a second air bag and a third air bag, the first air bag is arranged at the left chest position of the pectoral girdle body, and the second air bag is arranged at the right chest position of the pectoral girdle body; the third air bag is arranged on the back of the pectoral girdle body, the first air bag, the second air bag and the third air bag are connected with the air pump through independent air valves, and the intelligent monitoring unit is used for monitoring the heart rate and the respiratory rate of a patient and feeding monitoring data back to the control terminal. And the control terminal regulates and controls each group of air valves and air pumps to work according to feedback data. According to the chest pressurizing belt, the pressurizing strength of the chest pressurizing belt can be automatically adjusted, and the requirement for adjusting the pressurizing strength in real time according to the physical condition of a patient is met.

Featured are bracelets including a geolocation sensor and one or more physiological sensors. The physiological sensors may be selected from a heart rate sensor, an oxygen saturation sensor, a blood pressure sensor, a respiratory rate sensor, a temperature sensor, a vital sign monitoring (VSM) sensor, an impact detection sensor, a position sensor, a contact sensor, a hydration sensor, an electrolyte sensor, and a movement sensor. The geolocation sensor and / or the one or more physiological sensors may be integrated within the bracelet. In some embodiments, the geolocation sensor and / or the one or more physiological sensors may be configured to be activated upon contact with a user and / or deformation of the bracelet.

The invention belongs to the technical field of life information monitoring, and particularly relates to an FMCW radar-based non-contact breathing and heartbeat combined detection method, which comprises the following steps of: identifying the chest of a target to be detected by utilizing a target detection algorithm to obtain a target area; controlling a steering engine to enable the FMCW radar to move to a corresponding target area, transmitting a linear frequency modulation signal, receiving a reflection signal, and performing frequency mixing to obtain an intermediate frequency signal; performing distance fast Fourier transform on the sampling data of the intermediate frequency signal to obtain a distance feature, and determining a distance unit corresponding to the target according to an extreme value of the distance feature; a phase value of a distance unit where a target is located is extracted to perform phase unwrapping and phase difference operation, then a phase difference signal is subjected to signal decomposition by using a variational mode decomposition (VMD) algorithm, and a respiration and heartbeat signal is processed by using an independent component analysis (ICA) algorithm to obtain a respiration signal and a heartbeat signal; and S4, estimating the respiratory frequency and the heart rate through the frequency spectrums of the respiratory signal and the heartbeat signal. According to the invention, the signal processing efficiency and accuracy are improved.

The invention discloses a physiologically-driven standardized intelligent simulation patient system, which comprises an adult simulation patient, a computer, a computer, a computer, a computer, a computer, a computer, a computer and a computer, wherein the adult simulation patient is made of a simulation man-made high polymer material, is environment-friendly and pollution-free, has obvious anatomical marks, touches two nipples, ribs, sternum and xiphoid processes, is flexible in joints, and realizes various operation body positions; wherein the physiological data comprises the heart rate, the blood pressure and the respiratory rate, the data is combined with a built-in physiological model to simulate the physiological state of a patient in real time, a monitor is simulated, real-time physiological data monitoring and feedback are provided, and the vital signs, the heart rate, the blood pressure and the oxyhemoglobin saturation of the simulated patient are displayed; the system has strong physiologically-driven standardized intelligent comprehensive simulation patient, can perform situational teaching according to a real clinical environment, effectively assesses the clinical thinking and personal operation skills of students, can greatly improve the team cooperation spirit of the students, and simulates the real physiological response and clinical scene.

This invention discloses a feedback-based pulmonary rehabilitation assessment and treatment device, relating to the field of medical device technology. It includes a bicycle assembly connected to an expiratory mask and a detection wristband. The detection wristband is worn on the patient's wrist, and the expiratory mask is worn over the mouth and nose. The bicycle assembly is equipped with sensors and a resistance component, which is controlled by a central processing unit. The expiratory mask has a data acquisition module that collects the patient's respiratory rate. The patient's heart rate is then controlled by adjusting the resistance of the bicycle assembly. Through this technical solution, the sensor assembly can detect the resistance of the bicycle assembly in real time. The respiratory rate and the heart rate detected by the wristband are then assessed, and the central processing unit controls the resistance to help the patient achieve a stable respiratory rate and heart rate.

The utility model relates to a health monitoring system. The system comprises indoor equipment equipped with a sensor module, a processor in communication connection with the sensor module, and an alarm system. The embodiment of the utility model discloses breakthrough integration of radar technology and artificial intelligence in common household equipment (ceiling fan). The system is composed of a radar module and a decorative antenna housing which are seamlessly integrated, and a fan is converted into cautious and unnoticeable health monitoring equipment. The radar module remotely transmits signals to a monitored area, captures signals reflected from an individual body, and provides continuous real-time data about vital signs such as heart rate and respiratory rate. After the information is processed by the artificial intelligence processor, the system can detect abnormal conditions, and an alarm conveyed to the user through the user interface is triggered. The innovative system can adapt to different living environments and has deployment flexibility, and a new era of comprehensive, continuous and user-friendly health monitoring is created.

To provide a watching system capable of reducing the burdens of both a caregiver and a care receiver and obtaining enough information.SOLUTION: A watching system S according to one aspect of the present invention comprises a microwave measuring device M which transmits a microwave to a watching object O and receives a reflection wave of the microwave, a body temperature measuring device T which measures a body temperature of the watching object O, and an information processing device C which processes outputs from the microwave measuring device and the body temperature measuring device. A watching program and a watching method according to another aspect of the present invention include a biological information recording step of recording biological information data containing any of respiration rate data and heart rate data of a person to be watched in a computer on the basis of the output data of the microwave measuring device and a body temperature recording step of recording body temperature data of the person to be watched, and executes watching.SELECTED DRAWING: Figure 1

The invention relates to an intelligent low-altitude economic scene passenger emotion pacifying method and system based on a large model. The method comprises the following steps: constructing a multi-modal emotional state data set of low-altitude travel passengers; wherein the multi-mode emotional state data set comprises passenger face video sequence data, in-cabin voice and audio data, heart rate variability time sequence data, respiratory frequency time sequence data and external situation context data; performing emotional depth feature extraction based on multi-source space-time signals on the multi-modal emotional state data set to obtain multi-source emotional features; emotion recognition is carried out based on the multi-source emotion features; based on the emotion recognition result, constructing a pacifying strategy generation model; and utilizing a pacifying strategy generation model to intelligently pacify the emotion of the passenger, and evaluating and feeding back the pacifying effect in real time. According to the emotion and specific inducements recognized in real time, pacifying measure suggestions which conform to safety specifications, fit scenes and are natural in language can be generated, and timeliness and specialty of passenger cabin service are improved.

The present application relates to the technical field of psychophysiological monitoring, and discloses a video psychophysiological index analysis method and system; the method comprises the following steps: positioning facial key points and partitioning from a video, extracting remote optoelectronic plethysmography pulse wave and action unit breathing characteristics, outputting heart rate and breathing signals through Kalman filtering; obtaining a heartbeat interval through peak value detection, calculating heart rate variability indicators and breathing frequency, and normalizing by establishing an individual baseline in an initial calm period; fusing emotional characteristics and normalized indicators to form an observation vector, inferring a stress state through an unsupervised model and a forward algorithm; detecting index mutation points to identify stimulus events, and adopting a gated recurrent unit network to predict excessive stress risk and grade early warning; storing multi-session data into a database, quantifying desensitization rate, tolerance and recovery capacity through linear regression, weighted scoring and model updating. The present application realizes contactless monitoring, real-time stress evaluation and early warning, and supports quantitative analysis of cross-training effects.

The invention discloses a track speed control method of an intelligent treadmill, and particularly relates to the technical field of motor control, which comprises the following steps: S1, user information acquisition: acquiring user input information to obtain an initial speed; s2, setting the initial speed of the track: generating a speed control instruction, and sending a driving signal to a motor driving terminal to control the track to reach the set speed; and S3, real-time information collection: collecting physiological information of the user in the exercise process. And S4, speed rationality analysis: carrying out crawler speed rationality analysis. And S5, adjusting the crawler speed. And S6, motion state continuous monitoring and data storage: the motion state of the user is monitored in real time, and the speed in the normal motion state is stored in a database. The motion state of the user is analyzed by monitoring the heart rate and the breathing frequency of the user in the motion process in real time, and the track speed of the intelligent treadmill is dynamically adjusted according to the motion state of the user. The probability that the user is injured due to speed mismatching in the movement process is effectively reduced.

This invention relates to the field of multi-organ dysfunction (MOD) early warning technology, specifically to an MOD early warning method for patients with severe burns. The method includes the following steps: acquiring heart rate, mean arterial pressure, respiratory rate, blood oxygen saturation, lactate concentration, and urine output per unit time; calculating the rate of lactate change at adjacent time points to form a sequence; comparing the rate of change across time intervals to obtain the difference in change; combining heart rate and blood oxygen saturation to calculate circulatory load; and counting the number of early warning triggers that meet risk conditions. In this invention, by calculating the rate of change of lactate concentration at adjacent time points and combining it with interval direction statistics, tissue metabolic changes show trend characteristics; the rate of change of urine output combined with mean arterial pressure forms a record of renal perfusion changes, providing a continuous characterization of perfusion status; heart rate and blood oxygen saturation are used to calculate circulatory load; multiple physiological signals are linked for judgment; and recurring abnormalities are expressed through the number of triggers. This improves the stability and predictability of MOD risk identification in critically burned patients.

The invention provides a user motion data analysis method, device and system and computing device.The method comprises the steps that user motion data are preprocessed, and the user motion data comprise blood pressure data, heart rate data, respiratory rate data, oxyhemoglobin saturation data and position data of a user in the walking testing process; the preprocessed user motion data is input into a motion data analysis model, a physical condition analysis result is obtained, the motion data analysis model comprises a double-layer nested LSTM model composed of an input layer, a network formed by connecting a specific number of LSTM networks in parallel, an LSTM network, three full-connection layers, two Batch Normalization layers and a Softmax layer in series, and a physical condition analysis result is obtained. The specific number is the ratio of the test duration to the unit duration. The invention provides a user motion data analysis method, device and system and computing equipment, which are used for solving the problem that the existing method for evaluating the body function of a user according to artificial experience is not accurate and not high in precision.

Techniques and systems include predicting various health conditions using a photoplethysmography (PPG) signal or a video signal based on images of a patient's fingertip or other body portion captured using a mobile device. The video signal may be transformed into a pseudo PPG signal to measure blood volume changes in the patient's blood flow to derive data indicating a disease state or health-related characteristic, such as blood oxygen level, blood glucose level, heart rate variability, hemoglobin, respiration rate, or arrhythmia. Techniques involve real-time environment assessment and problematic issue detection, training an artificial intelligence (AI) model to measure signal quality so as to select high-quality signals from a range of signals, and domain adaption and transfer learning to make use of publicly available datasets.

The invention relates to the technical field of data processing, and provides a driver state monitoring method and device and an intelligent safety belt. The method comprises the following steps: collecting a physiological signal and a behavior signal of a driver through a multi-mode sensor group; performing feature extraction on the physiological signals to obtain real-time physiological state parameters of the driver; the physiological state parameters at least comprise a heart rate value and a breathing frequency value; performing feature extraction on the behavior signal to obtain a real-time behavior state parameter of the driver; the behavior state parameters at least comprise a body movement intensity value and a pressure distribution mass center position; determining current state information of the driver according to the physiological state parameters and the behavior state parameters; and executing an early warning operation corresponding to the current state information.

A method and apparatus for estimating vital signs includes obtaining data including physiological data relating to a user 32, for example PPG measurements; estimating user profiles based on the obtained data including a respiratory rate profile and an heart rate and / or blood pressure profile 34; determining a confidence level in the estimated user profiles based on differences between the estimated profiles and corresponding stored data profiles for an activity of the user 36; and performing signal enhancement on estimated user profiles with a determined confidence level below a threshold 38, wherein the enhancement is based at least in part on: one of the estimated user profiles with a higher confidence level and a stored cardiorespiratory coupling ratio for the user. The cardiorespiratory coupling ratio may be activity-specific, and there may be sensors for identifying the user activity. Where more than one of the estimated profiles has a confidence below the threshold, the enhancement is performed on the profile with the lowest confidence level. The confidence level may be determined based on the comparison of cross correlations of estimated and template data with standard deviations of template signals.

A mechanism for guiding the length of a multitude of different segments across a multitude of breathing cycles. A recorded length of each of the multitude of segments is obtained in a sample breathing cycle. The recorded length of each segment, along with a sample breathing rate and a desired breathing rate, is used to define a target length for each segment in each of the multitude of breathing cycles. Each target length is proportional to the corresponding recorded length.

A control system is provided for controlling a pressure applied by a pressure source of a positive airway pressure support system. A progressive increase is provided in the pressure applied during a fall asleep time period of a user of the system. A pressure modulation is applied during the progressive increase. The modulation is for promoting a target breathing rate of the user,

The invention discloses a feedback type lung rehabilitation evaluation therapeutic apparatus, and relates to the technical field of medical instruments, the feedback type lung rehabilitation evaluation therapeutic apparatus comprises a bicycle assembly, the bicycle assembly is connected with an expiration mask and a detection bracelet, the detection bracelet sleeves the wrist of a patient, the expiration mask is worn at the mouth and nose position, and the bicycle assembly is provided with a sensor and a resistance assembly. The resistance assembly is controlled through the central processing unit, a data acquisition module is arranged on the expiration mask, the data acquisition module acquires the respiratory rate of a patient, and then the heart rate of the patient is controlled by controlling the resistance of the bicycle assembly. According to the technical scheme, the resistance of the bicycle assembly can be detected in real time through the sensor assembly, then evaluation is conducted through the respiratory rate and the heart rate detected by the detection bracelet, and then the resistance is controlled through the central processing unit so that a patient can achieve the stable respiratory rate and heart rate.

The present application relates to the technical field of health data monitoring, in particular to a health monitoring method based on data analysis and a smart watch, which obtains physiological parameters such as heart rate, blood pressure, blood oxygen and respiratory rate of a user, analyzes the interaction between the physiological parameters, judges whether the physiological parameters are abnormally high or low, and obtains the individual health state distribution result. Based on the dynamic analysis of physiological parameters and combined with multi-dimensional data measurement, the present application accurately depicts the change characteristics of the health state, avoiding the errors caused by single data point judgment. By measuring the interaction between different physiological parameters, the health state can be more comprehensively evaluated, and the judgment of individual health state is not affected by the fluctuation of single physiological index. With the help of multi-dimensional coordinate space, the distance between the health state and the steady state interval is calculated, so that the evaluation of health state change is more quantitative, and the identification ability of abnormal fluctuation is improved.

The invention relates to the technical field of televisions, in particular to a user preference content recommendation method, a control system, a television, a medium and a product. The user preference content recommendation method comprises the following steps: detecting state data when a user watches a program; wherein the state data comprises external data and / or physical sign data, the external data comprises facial expression, body form and respiratory rate, and the physical sign data comprises blood pressure, pulse and heart rate; judging whether the program watched by the user is favorite content of the user or not according to the state data; and when the program is the favorite content of the user, recommending the content matched with the favorite content to the user. According to the method and the device, whether the program is the favorite program of the user is judged according to the state data when the user watches the program, and subjective random content selection of the user is replaced by an objective body reaction data (namely, state data) identification result, so that the really favorite content is recommended to the user; and therefore, the problem of poor user experience caused by subjective selection uncertainty can be avoided.

The application discloses an ear-mounted heart rate and respiration real-time monitoring method and system capable of resisting motion interference. The method comprises the following steps: collecting audio data and IMU data in an ear canal of a target user by using an ear-mounted wearable device; extracting heartbeat features and respiration features from the audio data; inputting the respiration features into a respiration waveform reconstruction model to obtain a reconstructed respiration waveform; inputting the heartbeat features into a spectrum reconstruction model to obtain a reconstructed electrocardiogram spectrum graph, and converting the reconstructed electrocardiogram spectrum graph from a frequency domain representation to a time domain representation to obtain a time domain electrocardiogram waveform; pre-processing the IMU data to obtain an IMU chest fluctuation waveform; and based on the time domain electrocardiogram waveform, the reconstructed respiration waveform and the IMU chest fluctuation waveform, obtaining an estimation result of a respiration frequency and a heart rate through peak-to-peak value detection. The application can effectively eliminate the influence of motion interference and improve the estimation accuracy of heart rate and respiration.

The invention discloses an intelligent child ward nursing system, relates to the technical field of ward nursing, and solves the technical problems that monitoring is not timely, nursing decision is lack of accurate data support, and nursing is difficult. The body temperature, the heart rate, the respiratory rate, the blood pressure, the oxyhemoglobin saturation and other sign data of a child patient are collected in real time through the sensors and the monitoring equipment, data abnormity can be found in time, the data value is deeply mined through the modes of matching with historical data, analyzing the time period change of abnormal data and the like, and the safety of the child patient is improved. The nursing decision analysis module is used for generating rule information, irregular information, stable change information, unstable change information and the like, more comprehensive and deep data support is provided for nursing decisions, the nursing decision analysis module selects the most suitable nursing decision based on multi-dimensional data and information through multiple rounds of screening and analysis, and the accuracy of the nursing decision is improved.