48 results about "Respiration rhythm" patented technology

The invention discloses a pet wearable vital sign monitoring method based on a flexible sensor, which comprises the following steps: constructing a flexible multi-mode sensing interface, embedding a sensing layer into a fitting layer and fixing the sensing layer at the neck or chest position of a pet; a low-power thermal pulse is output to detect the contact characteristic, and a stable fitting interface is formed; multi-mode signals are collected and synchronously corrected, and filtering and baseline processing are executed to obtain a processed signal set; multi-modal features are extracted, a physical tensor is constructed, and vital sign parameters are calculated; body temperature, heart rate and respiratory rhythm components are extracted, a rhythm resonance curve is generated, and target configuration parameters are determined; sampling and communication are adjusted according to the target configuration, and an alarm is triggered and uploaded to a remote terminal when abnormity occurs. According to the pet vital sign monitoring system, high-precision, low-power-consumption and long-term stable monitoring of pet vital signs is realized by constructing a flexible multi-mode sensing interface and combining a physical consistency driven time sequence reasoning and rhythm energy scheduling mechanism.

The invention provides a vital sign detection method and device based on a millimeter wave radar, electronic equipment and a storage medium. The method comprises the following steps: obtaining a distance frequency domain signal based on a radar echo signal; performing spectral analysis on the distance frequency domain signal, positioning the position of the to-be-detected object, and extracting phase information at the position; filtering through a band-pass filter to obtain a breathing frequency band interval signal and an initial heartbeat frequency band interval signal; performing real-time respiration rate estimation based on the respiration frequency band interval signal to obtain a respiration frequency, and eliminating respiration harmonics in the initial heartbeat frequency band interval signal by using an adaptive wave trap based on the respiration frequency to obtain a heartbeat frequency band interval signal; and with the minimum fuzzy entropy as an optimization target, decomposing the heartbeat frequency band interval signal by using a variational mode decomposition algorithm to obtain a heartbeat signal, and carrying out real-time heart rate estimation based on the heartbeat signal to obtain a real-time heart rate. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the accuracy of heart rate measurement can be improved.

The invention provides a driver emotion information determination method which can be applied to the technical field of radars. The method comprises the following steps: transmitting a radar signal to a driver through a radar module mounted at a preset position in a driver seat, and receiving an echo signal obtained by reflecting the radar signal by the driver; extracting a heartbeat cycle and a breathing cycle of the driver from the echo signal, wherein the heartbeat cycle comprises I heartbeats; calculating a heartbeat interval change rate representing heart rate variability based on every two adjacent heartbeat cycles in the I heartbeats and the I heartbeats; calculating a respiratory rate based on the respiratory cycle; and determining the emotional state of the driver based on the heartbeat interval change rate and the breathing rate. The invention further provides a driver emotion information determination device, equipment and a storage medium.

This invention belongs to the technical field of sleep quality analysis, specifically relating to a sleep quality analysis system and method based on sensors in a medical bed, to address the technical problem of existing methods relying too heavily on duration statistics while neglecting the continuity and circadian rhythm of sleep. The analysis method includes: S1, acquiring vital sign signal data collected by sensors in the medical bed within a sleep cycle and dividing it into multiple continuous time segments; S2, extracting physiological indicators such as respiratory rate, heart rate, and body movement amplitude within each time segment, and calculating the variability depth of the corresponding time segment; S3, generating a variability depth baseline; calculating non-steady-state disturbance factors based on the frequency and duration of micro-awakening disturbance segments; and S4, generating a comprehensive sleep quality score. The analysis method of this invention improves the comprehensiveness and objectivity of the assessment results, more realistically reflecting sleep health status.

The invention discloses a wearable sensing system and a respiratory wave generation and respiratory rate estimation method thereof, relates to the field of respiratory processing, solves the problem that respiratory waves and respiratory rates cannot be accurately analyzed based on electrocardiograms, and comprises a data processing module, a stacking convolution module, a respiratory discrimination module, a signal demodulation module and a data calculation module. The data processing module is used for performing initial processing on an electrocardiogram of a person to be detected; the stack convolution module is used for performing stack convolution on the voltage intensity sequence of the heart of the to-be-detected person and the convolution kernels in different convolution layers to obtain a respiratory wave sequence of the to-be-detected person; the respiration discrimination module is used for discriminating a respiration wave sequence of a person to be detected; the signal demodulation module is used for carrying out signal demodulation on the respiratory wave of the person to be detected to obtain a final respiratory wave; and the data calculation module is used for calculating the respiratory rate of the to-be-detected person. According to the invention, accurate analysis of the respiratory wave and the respiratory rate based on the electrocardiogram is realized.

The present application relates to the field of respiratory heart rate monitoring, and discloses a non-contact heart rate and respiration recorder, which comprises a host computer, a wireless charging base, a charging cable and a terminal device, wherein the host computer is internally provided with a non-contact sensor module, a processor, a battery and a wireless communication module, and the shell adopts a waterproof sealing structure. In use, the host computer is placed under a pillow, and a heart beat and respiration channel signal is collected through the non-contact sensor, and after filtering, normalization and artifact removal, the signal forms a heart beat observation and a respiration observation under the action of parameters such as a sampling rate, a time window length, a vertical offset and a coupling stiffness, and is subjected to joint optimization under the constraints of spectral matching and cross-modal coherence, and finally outputs a heart rate and a respiration rate. The device does not need to wear an electrode, is stable in detection, and is suitable for long-term home health monitoring and clinical assistance.

The invention relates to the field of respiration heart rate monitoring, and discloses a non-contact heart rate respiration recorder which comprises a host, a wireless charging base, a charging cable and terminal equipment, a non-contact sensor module, a processor, a battery and a wireless communication module are arranged in the host, and a shell is of a waterproof sealing structure. During use, a host is placed under a pillow, cardiac and respiratory channel signals are collected through a non-contact sensor, and after filtering, normalization and artifact removal, under the action of parameters such as a sampling rate, a time window length, vertical bias and coupling rigidity, a cardiac observed quantity and a respiratory observed quantity are respectively formed; joint optimization is carried out under spectrum matching and cross-modal coherence constraints, and finally the heart rate and the respiration rate are output. The device does not need to wear an electrode, is stable in detection, and is suitable for long-term family health monitoring and clinical assistance.

Technologies and implementations for determining a respiration rate of a person from heart rate monitoring signal is disclosed.

The invention discloses a non-contact respiratory rate estimation method based on a human body trunk video, and the method comprises the steps: building a two-color reflection model of a human body trunk region, selecting a plurality of ROIs of the trunk region, carrying out the spatial averaging of pixels in each ROI, and generating a corresponding RGB time sequence signal; performing normalization processing on the generated RGB time sequence signal to obtain an RGB three-channel signal vector, and projecting the obtained RGB three-channel signal vector to a plane orthogonal to the vector [1, 1, 1] to obtain a multi-channel signal set of the ROI; performing de-mixing processing on the multi-channel signal set of the ROI by utilizing principal component analysis to obtain candidate respiration signals; based on a waveform factor, kurtosis and an autocorrelation periodic stability index, the obtained candidate respiration signals are screened, and screened respiration signals are obtained; and performing fast Fourier transform on the screened respiratory signals, and calculating the respiratory rate based on the main frequency of the signals. According to the method, the accuracy and the stability of respiratory rate estimation under the strenuous exercise condition are improved.

The invention discloses an AI sleep monitoring intelligent ring based on dynamic breath feedback and a health early warning system, and relates to the technical field of intelligent wearable equipment, and the AI sleep monitoring intelligent ring comprises an acquisition module which is used for acquiring photoelectric volume pulse waves, skin electrical activity and body temperature and body movement signals during sleep; the respiratory phase module extracts and constructs a time alignment frame of respiratory rhythm synchronization based on photoelectric volume pulse waves, the phase segmentation alignment and signal enhancement module performs specific filtering enhancement on multi-mode signals according to respiration, the enhanced signals are guided into the recognition module, a personalized health reference module is established by combining results, the physiological state is monitored in real time, and the recognition module is used for recognizing the physiological state. And when the reference is deviated, the health early warning module is triggered, the personalized respiration guiding module is started according to the abnormal type, the respiration rhythm is adjusted through tactile feedback, the guiding intensity is dynamically adjusted according to the intervention effect, progressive separation is achieved, new data is fed back to the personalized health reference model, and continuous self-adaptive updating is completed.

A method includes acquiring a video using a camera. The method further includes determining a motion-based respiratory rate (RR) and a motion-based respiratory signal based on a face of the person, the face of the person being identified based on the video. The method further includes determining a remote photoplethysmography (rPPG)-based RR and an rPPG-based respiratory signal based on a face of the person, the face of the person being identified based on the video. The method further includes selecting one of the motion-based RR or the rPPG-based RR by inputting the motion-based respiratory signal and the rPPG-based respiratory signal as inputs into a trained machine learning model. In addition, the method includes presenting a selected one of a motion-based RR or an rPPG-based RR based on the prediction.

The invention relates to a pectoral girdle type physiological monitoring method for monitoring air arrhythmia and flight fatigue, belongs to the field of aeronautical medicine and machine learning, and solves the problem that the existing method cannot accurately monitor the air arrhythmia and the flight fatigue in real time. Comprising the steps that physiological data of a pilot are obtained in real time through chest strap type monitoring equipment, and the physiological data comprise single-lead electrocardiosignals, oxyhemoglobin saturation and respiratory wave signals; obtaining a Scalogram wavelet scale map and an HRV (High Rate Velocity) feature on the basis of the preprocessed electrocardiosignal; the respiration rate, the respiration amplitude and the respiration irregularity are obtained based on the respiration wave signals; inputting the Scalogram wavelet scale diagram into the trained heart rhythm classification model to obtain the heart rhythm grade of the pilot; inputting the HRV features, the breathing rate, the breathing amplitude, the breathing irregularity and the oxyhemoglobin saturation into a trained fatigue classification model to obtain a pilot fatigue level; based on the pilot heart rhythm level and the fatigue level, the comprehensive risk level of the pilot is obtained through double-layer threshold value fusion and used for evaluating the physiological state of the pilot.

A biomedical parameters monitoring system for the diagnosis of sleep disorders provides a plurality of sensors reading a series of biometric parameters, one or more external devices collecting the data read by the sensors for making the diagnosis, and a facial support on which at least one sensor is mounted for reading the electroencephalographic signal and the electrooculography signal and a sensor for reading the electromyographic signal; the facial support has a central part, suitable for being located at the mouth and the nose, on which a sensor is mounted to read the respiration rhythm and the emitted carbon dioxide signal; each of the sensors is provided with a radio transceiver to transmit the reading data to said one or more external devices. The result is a monitoring system without wiring and efficient from a diagnostic point of view.

A system and method for monitoring respiratory movements and generating real-time audio feedback reflects a user's breathing pattern. The system includes a respiratory sensor, a signal processor, an audio synthesis unit, a wireless transmitter, and an audio output device. Respiratory signals are captured, filtered, and analyzed to determine inhalation and exhalation phases. The audio synthesis unit produces corresponding sound signals that are dynamically modulated based on the user's breathing intensity and rhythm. The audio output provides a continuous, natural-sounding auditory representation of breathing, which can be used for caregiver reassurance, sleep monitoring, or relaxation purposes. Abnormal events such as breathing cessation or muscle spasms may trigger audio alerts or spoken messages. Processed physiological metrics, including respiratory rate, heart rate, and sleep state, being transmitted to a cloud platform for long-term analysis. The system may also interface with smart lighting or external alerting devices to support hearing-impaired caregivers.

The application relates to the technical field of human rhythm, in particular to an intelligent sleep-aiding method and device, equipment and a storage medium, wherein the method comprises the following steps: determining a fluctuation degree adaptive threshold and a coefficient of variation adaptive threshold based on an initial heart rate and an initial respiration rate collected and a preset resting time length; determining a heart rate instantaneous fluctuation degree based on the initial heart rate corresponding to a heart rate sliding window; determining a respiration rate coefficient of variation based on the initial respiration rate corresponding to a respiration rate sliding window; and starting an electromagnetic field generator in response to the existence of the heart rate instantaneous fluctuation degree being greater than the fluctuation degree adaptive threshold, the respiration rate coefficient of variation being greater than the coefficient of variation adaptive threshold and the duration being greater than a preset judgment time length. The application facilitates improving sleep-aiding precision, sleep-aiding effect and sleep-aiding intelligent level of the user.

The present invention provides a respiratory waveform processing method, comprising: pre-processing the collected respiratory waveform to obtain a respiratory waveform to be processed; detecting the peak point of the respiratory waveform to be processed to obtain a respiratory waveform peak point sequence; calculating the RR sequence and the RR sequence according to the respiratory waveform peak point sequence; fd sequence; through the RR sequence and RR fd The respiratory rate variability (RRV) characteristic parameters are calculated through sequence processing; based on the deep learning long short-term memory network (LSTM), the RRV characteristic parameters obtained from the normal respiratory waveform and the RRV characteristic parameters obtained from the abnormal respiratory waveform are input into training to obtain a classification model for the identification and analysis of abnormal respiratory waveforms; through the technical solution of the present invention, the deep mining of respiratory waveform characteristics and the timely and accurate classification and identification of abnormal respiratory waveforms are realized.

The present invention relates to a non-contact respiratory rate monitoring method, device, and storage medium based on NIR video streams in complex scenarios, wherein the method comprises: acquiring a near-infrared image sequence; extracting facial key points based on the acquired near-infrared image sequence, and determining a face detection frame area and facial posture based on the extracted facial key points; determining multiple chest cavity candidate regions based on the face detection frame area and facial posture, combined with pre-configured bias information; acquiring the optical flow intensity of each chest cavity candidate region, and determining the chest cavity region based on the optical flow intensity of each chest cavity candidate region; obtaining an optical flow signal of the chest cavity region based on the near-infrared image sequence; and obtaining the respiratory rate based on the obtained optical flow signal of the chest cavity region through filtering and wavelet transform correlation frequency selection. Compared with the existing technology, the present invention has the advantages of wide application range and high accuracy.

The invention relates to an apparatus for determining a respiration rate of a subject. A pressure signal providing unit provides a measured pressure signal of a subject over a time, wherein the measured pressure signal is indicative of blood pulsations and comprises a plurality of pressure pulses, and a processor determines, for a respective pressure pulse of the plurality of pressure pulses (9), a respective dicrotic notch position and determines the respiration rate based on the dicrotic notch positions determined for the plurality of pressure pulses (9). This allows for a very accurate determination of the respiration rate.

A breathing rate estimation system includes at least one radar type sensor installed in a monitoring area, an information processing device configured to execute person detection processing of acquiring sensor data output from the sensor and detecting a person present in the monitoring area using the acquired sensor data and breathing rate estimation processing of estimating a breathing rate of the person, and a display device configured to display the breathing rate of the person estimated in the breathing rate estimation processing. The information processing device determines whether the person is in a specific state based on a detection result of the person by the person detection processing, and executes the breathing rate estimation processing when it is determined that the person is in the specific state.

This application relates to the field of respiratory detection technology, and more particularly to a respiratory detection system, method, electronic device, and storage medium. The system includes: a PPG sensor for acquiring raw PPG data; a wavelet transform module for decomposing the raw PPG data into signal-related frequency band data to extract respiratory-related frequency band data; and a respiratory rate prediction module for feature extraction and respiratory rate prediction of the respiratory-related frequency band information to obtain target detection results. The wavelet transform module can effectively extract respiratory-related frequency band data from the PPG signal. This decomposition can distinguish physiological fluctuations of different frequencies caused by heartbeat and respiration, especially accurately capturing low-frequency changes caused by respiration, thereby improving the accuracy of respiratory rate prediction and ensuring the precision of target detection results.

The application discloses a heart rate extraction method based on an adaptive comb filter. The method comprises the following steps: 1, obtaining a life signal by processing a radar echo; 2, extracting a breathing rate and other features therefrom, and initializing parameters of the filter; 3, designing an ALE module for evaluating a filtering effect, and initializing parameters of the module; 4, passing the life signal through the adaptive comb filter, and iterating until the parameters to be optimized are basically converged; and 5, selecting a heartbeat frequency from a spectrum of an output signal of the life signal through the filter. The heart rate extraction method based on the adaptive comb filter considers the intermodulation characteristics of breathing and heart rate components of the life signal in a Doppler radar system, and effectively reduces the interference of breathing and higher harmonics on the selection of the heart rate. The experimental results show that when the higher harmonics of breathing in the life signal affect the measurement of the heart rate, the adaptive comb filter can accurately measure the heart rate of a person to be measured.

The invention relates to the technical field of signal processing, in particular to a respiratory rate calculation method. The method comprises the following steps: acquiring an original respiration signal; performing low-pass filtering processing on the original breathing signal to obtain a filtering signal; carrying out down-sampling processing on the filtering signal to obtain a down-sampling signal; performing first-order differential operation on the down-sampling signal to obtain a differential signal; peak point detection is carried out on the differential signals, and characteristic peak points of the differential signals are determined; and calculating the respiration rate corresponding to the original respiration signal based on the characteristic peak point. According to the method, low-pass smoothing, down-sampling, first-order difference, peak point detection and the like are sequentially carried out on the original respiration signals, and the real-time respiration rate can be simply and accurately calculated in the time domain.

This application discloses a method and apparatus for assessing breathing training. The method includes: acquiring physiological signals of a target user before, during, and after breathing training, and performing feature extraction and quantification to form feature arrays for pre-training, during-training, and post-training; obtaining normalized resting heart rate and normalized resting respiratory rate of the target user in the current state based on the target user's historical resting heart rate and historical resting respiratory rate; normalizing the feature arrays before and after breathing training based on the target user's current resting heart rate and resting respiratory rate; and performing a breathing training assessment based on the feature arrays during and after breathing training, as well as the normalized feature arrays before and after breathing training. By incorporating resting heart rate and resting respiratory rate into the three parts of the breathing training process (pre-training, during, and post-training), a comprehensive assessment of the entire breathing training cycle is achieved.

The application discloses a wearable sensing system and a respiration wave generation and respiration rate estimation method thereof, relates to the field of respiration processing, and solves the problem that respiration wave and respiration rate cannot be accurately analyzed based on electrocardiogram, and comprises a data processing module, a stacked convolution module, a respiration discrimination module, a signal demodulation module and a data calculation module; the data processing module is used for initial processing of electrocardiogram of a person to be detected; the stacked convolution module is used for stacked convolution of voltage intensity sequences of the heart of the person to be detected and convolution kernels in different convolution layers to obtain respiration wave sequences of the person to be detected; the respiration discrimination module is used for discriminating the respiration wave sequences of the person to be detected; the signal demodulation module is used for signal demodulation of the respiration waves of the person to be detected to obtain final respiration waves; and the data calculation module is used for calculating the respiration rate of the person to be detected, and the application realizes accurate analysis of respiration wave and respiration rate based on electrocardiogram.

The embodiment of the present invention proposes a method, device and medium for processing respiratory rate. The method part includes: obtaining a PPG signal of a PPG sensor; obtaining a first signal feature of at least one dimension based on the PPG signal; processing the first signal feature to obtain a second signal feature, converting the second signal feature from the time domain to the frequency domain to obtain a frequency domain energy map; based on the frequency domain energy map, extracting at least two respiratory rate values by selecting the frequency domain energy value with the highest score; fusing and calculating at least two respiratory rate values to obtain a target respiratory rate value. The present invention first obtains two or more first signal features of at least one dimension, and then obtains at least two frequency domain energy maps corresponding to the first signal feature, obtains the respiratory rate value corresponding to the frequency domain energy value with the highest score for each frequency domain energy map, ensures the obtained respiratory rate value, and then fusing and calculating at least two respiratory rate values, and finally obtains the target respiratory rate value, thereby improving the accuracy of the respiratory rate measurement value.

The invention relates to the technical field of intelligent wearing, and discloses a wearable heart rate and respiration rate detection method, device and equipment and a storage medium. The method comprises the steps that primary filtering processing is conducted on an original electrocardiosignal obtained through a wearable electrocardiograph sensor in real time, so that a first electrocardiosignal is obtained; performing secondary filtering processing on the first electrocardiosignal to obtain a second electrocardiosignal, judging whether the current waveform of the second electrocardiosignal is an R wave or not according to an R peak judgment threshold value, judging whether the RR time interval of the current waveform is within a preset interval range or not when the current waveform is the R wave, and judging whether the RR time interval of the current waveform is within the preset interval range when the RR time interval is within the preset interval range. The heart rate is determined on the basis of the RR time interval, the three-axis acceleration signal is obtained in real time through the three-axis acceleration sensor, the respiration rate is determined on the basis of the three-axis acceleration signal and the first electrocardiosignal, and therefore the heart rate and the respiration rate can be continuously and accurately detected in real time for a long time through the wearable heart rate and respiration rate detection device.

This disclosure generally relates to systems and methods for video detection of breathing rates in a subject, such as a sleeping baby. One or more video cameras may capture video of the subject. The video may be corrected and then a featured signal may be generated from the corrected video. A respiratory rate detector may be built to output a signal that represents breathing of the subject in the video. The signal may be corrected for any double detections. A breathing rate of the subject may be determined, and then the breathing rate of the subj ect may be output. In some examples, if the breathing rate is determined to be abnormal, an alarm or notification of the abnormality may be generated, or a moving platform on or in which the subject is sleeping may be moved.

The invention discloses a video-based non-contact respiratory rate measurement method. According to the method, a reasonable and effective respiratory rate detection framework is constructed. The frame comprises three core modules: a self-adaptive edge enhancement module used for solving high-quality angular point selection of a long-distance low-pixel ROI region, a self-adaptive signal quality evaluation module used for preventing performance reduction caused by blind denoising, and a multi-strategy fusion self-adaptive noise removal module used for continuous motion removal and based on signal characteristics. According to the framework, SOTA is obtained through verification on three data sets, the excellent performance of the framework is shown, and the feasibility that a non-contact physiological signal detection technology is popularized to a wider and universal application scene is promoted.

The invention relates to the technical field of biosensing and health monitoring, in particular to an integrated multi-parameter information acquisition sensor and an information acquisition method, and the information acquisition method comprises the following steps: carrying out noise reduction and baseline correction on an acquired original resistance signal to eliminate environmental noise interference; establishing a resolving model based on the respective temperature-sensitive coefficients and pressure-sensitive coefficients of the first measurement unit and the second measurement unit; coefficient parameters are determined through a calibration experiment, and cross calculation is carried out to obtain the body temperature and indirectly obtain a respiratory rate calculation formula; according to the time sequence characteristics of the piezoresistive variable quantity, the respiratory rate parameter is obtained by combining the calculation formula of the body temperature and the piezoresistive variable quantity, so that the collected body temperature data and the piezoresistive variable quantity can construct and form a resolving model, and the requirement for collecting the body temperature and the respiratory rate at the same time is met; the invention also correspondingly discloses a structure of the integrated multi-parameter information acquisition sensor, and the sensor comprises a flexible substrate and a data acquisition module, so as to meet the requirement of space occupation.

A photoplethysmogram (PPG) signal may be used to determine values representing a pulse rate, oxygen saturation, or respiration rate of a user by determining data points of the signal that correspond to local maxima (peaks) and local minima (valleys). Peaks and valleys are identified based on the directional components of sets of points that precede and follow the peak or valley, and values for one or more of those points relative to the value for the peak or valley. Once peaks and valleys of the signal are identified, lengths of time between adjacent peaks or valleys may be used to determine pulse rate. Differences in values represented by an adjacent peak and valley for different wavelengths of the PPG signal may be used to determine oxygen saturation. Changes in distances between adjacent peaks or valleys and amplitude of the PPG signal over time may be used to determine respiration rate.