69 results about "Respiration rhythm" patented technology

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

The invention discloses 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 heart rate and respiration rate monitoring method and system based on remote photoplethysmography, and the method comprises the steps: obtaining current image data with a face image based on a model reasoning end; inputting the current image data into a heart rate and respiratory rate monitoring model, and performing monitoring reasoning by using the heart rate and respiratory rate monitoring model to obtain the identity of the person, the heart rate of the person and the respiratory rate of the person in the current image data; wherein the heart rate and respiration rate monitoring model is obtained by training a remote photoplethysmography model at a model training end by adopting historical image data, and then is imported into the model reasoning end; a face detection model and a face tracking model are integrated in the model reasoning end. A mode of combining a model training end and a model reasoning end is adopted, so that a heart rate and respiratory rate monitoring model, a face detection model and a face tracking model are effectively combined, and heart rate and respiratory rate physiological signs of identities of multiple persons can be obtained and monitored in real time.

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.

A method to process signals acquired with at least one accelerometer and one gyroscope worn by a subject for evaluating a heart rate and / or a respiratory rate. The method includes: receiving the accelerometer and gyroscope signals at a sampling frequency in a given time window; combining each pair of accelerometer and gyroscope signals in the given window to output an orientation vector for each sampling time using a quaternion representation; applying at least one filter to the orientation signal defined by the orientation vectors to obtain a filtered signal; calculating an average breath cycle and / or time interval separating two consecutive heart beats using an algorithm for modeling an average signal, which iteratively determines the average between the filtered signal and the average signal from a preceding iteration; estimating a heart and / or respiratory rate from the calculated average breath cycle and / or time interval separating two consecutive heart beats.

A system and method for determining a respiratory rate of a subject is provided. A respiratory signal is received and a derivative of a low-pass filtered version of the respiratory signal is acquired to obtain a derivative signal. In one example, a slope summing filter is applied to transform the derivative signal, and a plurality of breathing intervals are identified from the transformed signal. Respiratory characteristics in the filtered respiratory signal are obtained using the transformed signal. Appropriate breathing intervals are selected based on breathing characteristics, and a breathing rate is calculated based on those selected breathing intervals.

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.

The present invention discloses a respiratory rate measurement method and device. The implementation of the method includes: collecting pressure map data and determining the chest and abdomen regions in the pressure map data; determining a target unoccupied area according to a preset rule, and obtaining first pressure data of the chest and abdomen regions and second pressure data of the target unoccupied area; performing collaborative filtering processing on the first pressure data by using the second pressure data to obtain an adaptive filtering signal; performing band-pass filtering processing on the adaptive filtering signal to obtain a respiratory wave signal; extracting peak points from the respiratory wave signal to obtain peak intervals; and calculating a respiratory rate value based on the peak intervals. By extracting the positions of the chest and abdomen and using the pressure signals of the unoccupied area to perform collaborative filtering on the pressure signals of the positions of the chest and abdomen, the signal quality is improved to a certain extent, the accuracy of the respiratory rate is increased, and the respiratory rate measurement can be realized in complex scenarios such as multiple people and multiple people and multiple objects. The applicable scenarios are extensive and the accuracy is higher.

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 method for computing respiration rate and detecting events in a respiration signal representing the respiration of a monitored patient includes: performing a frequency domain analysis of the respiration signal to identify low frequency portions of the respiration signal that may be caused by one or more artifacts; and performing a time domain analysis to modify thresholds during artifacts to compute respiration rate from the time domain respiration signal. The method may be performed by a physiological monitoring device or a patient monitoring system employing a physiological monitoring device.

A human body core body temperature detection device comprises a fixing band, and a single-channel heat flow method sensor, a respiration sensor, an electrocardio sensor and a data processing unit which are fixed on one side of the fixing band, the single-channel heat flow method sensor, the respiration sensor and the electrocardio sensor are electrically connected with the data processing unit. The invention further provides a human body core body temperature detection method. The method comprises the steps that original body temperature data are collected through the single-channel heat flow method sensor; collecting respiration rate data through a respiration sensor; heart rate data are collected through an electrocardio sensor; the data processing unit dynamically adjusts the weights of the original body temperature, the heart rate and the respiration rate, and a pre-core body temperature and the body temperature compensation amount are calculated through a two-stage fusion algorithm; and correcting the pre-core body temperature data through the core body temperature compensation amount to obtain the core body temperature of the human body. By integrating the dynamic changes of the heart rate (HR) and the respiratory rate (RR), the accuracy and robustness of body temperature detection are remarkably improved.

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.

Embodiments may relate to an air-permeable weight support system, which may include a microclimate fabric layer and an air-permeable capacitive sensor layer below the microclimate fabric layer. The air-permeable capacitive sensor layer may include two air-permeable substrates, each carrying a plurality of electrically conductive pathways that are impermeable to air. The two air-permeable substrates may securely position the electrically conductive pathways to define a sensor grid and position the electrically conductive pathways that are impermeable to air to be spaced apart to define a plurality of air pathways for the air-permeable capacitive sensor layer. The sensor data may be used to determine a sleep state of the person using the weight support system. A computer may identify the poses of the person based on the pressure sensor data. A machine learning model may rely on the poses, the heart rates, the respiration rates to determine the sleep state.

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.