95 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 non-contact respiratory rate detection method and system based on face thermal imaging, a storage medium and electronic equipment, and relates to the field of non-contact respiratory rate detection. According to the method, a thermal infrared video is utilized, and a deep learning technology is combined to accurately and stably extract a high-quality respiration signal from a thermal infrared spectrum. Meanwhile, the self-adaptive respiratory signal noise reduction network based on the attention mechanism is designed to carry out self-adaptive automatic noise reduction processing on the initial respiratory wave signals, local details and global features in the respiratory signals can be deeply analyzed, noise can be automatically distinguished and effectively suppressed, key physiological information is reserved, and the accuracy of noise reduction is improved. And the quality of respiratory signals and the precision of respiratory rate estimation are obviously improved. Through training of the deep learning model, the method can adapt to diversified breathing characteristics of different crowds and environments, the generalization ability in various practical application scenes is enhanced, and the stability and high accuracy of breathing rate detection are ensured.

The invention provides an intelligent toilet seat life information parameter monitoring system and method, the system is applied to a toilet, the system comprises an optical fiber sensing module and a piezoelectric sensing module, the optical fiber sensing module and the piezoelectric sensing module are used for collecting ballistocardiogram and phonocardiogram signals during defecation so as to monitor the heart rate and the respiration rate in real time; the infrared sensor or the microwave radar sensor and the infrared sensing module or the microwave radar bed module comprise defecation detection and fall monitoring probes and are used for defecation frequency detection and iterative judgment alarm; the blood oxygen monitoring module comprises a photoelectric detector and is used for monitoring a photoplethysmogram and blood oxygen fluctuation change of the heart cycle of the user in real time; the non-inductive transmission module is used for providing wireless transmission, uploading data monitored by the intelligent toilet seat to the data management system and displaying the data on a display screen; and the master controller is used for uploading the data monitored by the sensing module to a data management system. According to the invention, automatic monitoring of the defecation process is realized, and invisible health monitoring of old people is realized.

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.

The invention discloses a respiratory rate measuring method and system fusing thoracic motion and non-contact signals, and the method comprises the steps: carrying out the feature point recognition of a video frame, connecting the positions of feature points of left and right shoulders, and calculating the distance from the position of a nose feature point to the connecting line; the distance change serves as a thoracic motion signal, the signal is filtered, and a breathing signal based on thoracic motion is obtained; obtaining a region of interest based on the facial feature point positions; calculating an RGB signal in the region of interest, and extracting a non-contact PPG signal from the RGB signal; extracting a breathing mode signal from the non-contact PPG signal; performing fusion filtering on the breathing mode signal to obtain a breathing signal based on the non-contact PPG signal; and respectively extracting supplementary information of the signals from the breathing signal based on thoracic movement and the breathing signal based on the non-contact PPG signal, and simultaneously inputting the extracted supplementary information and the two signals into the trained neural network to obtain the breathing rate.

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

A computer-implemented method for determining a score indicative of mindfulness of a user is provided. The method includes obtaining heart rate variability data of the user during a guided breathing exercise in which the user inhales and exhales to mimic a respiration rate associated with mindfulness. The method includes filtering the heart rate variability data to generate filtered heart rate variability. The method includes determining a first standard deviation of interbeat intervals indicative of respiratory sinus arrythmia and included in a first segment of the filtered heart rate variability data that spans a discrete interval of time. The method includes determining a second standard deviation of all interbeat intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicative of mindfulness of the user based on the first standard deviation and the second standard deviation.

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 detection method and device based on drone video, belonging to the technical field of drone image processing. The method obtains a video stream containing the respiratory action signs of the target object, selects the chest and abdomen of the target object in the video stream as the region of interest; grayscale processing is performed on the region of interest, the chest and abdomen vibration region of the target object is taken as the object, and the respiratory vibration region is subjected to time phase processing to extract the original signal; since the original signal is mixed with the interference signal caused by the drone jitter, the original signal is used to obtain the candidate respiratory modal signal by modal decomposition, the interference signal caused by the drone shaking is removed, the noise interference is suppressed, and finally a good respiratory rate waveform fitting is obtained, and at the same time, the spatial domain weighting of the amplitude information to the phase information can improve the signal-to-noise ratio of the respiratory signal, and realize the dynamic detection of accurate respiratory rate.

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.

The invention provides a breathing state monitoring method and equipment. The breathing state monitoring method comprises the following steps: extracting a breathing signal s (t) aiming at an echo signal of a millimeter wave radar in a single monitoring time period; a plurality of window breathing signals are extracted from the breathing signal s (t) in a sliding window mode, and the breathing rate BPM of each window breathing signal is determined; calculating probability density information Pi of respiration rates BPM of the plurality of window respiration signals; generating probability density baseline information B by utilizing the probability density information Pi of the same monitoring object in the plurality of monitoring time periods; comparing the probability density information Pj of the monitoring object in the target monitoring time period with the probability density baseline information B to obtain a deviation feature b; and determining whether the breathing state of the monitored object in the target monitoring time period is normal or not according to the deviation characteristic b.

A respiration monitoring system can include a respiration sensor configured to continuously monitor respiration data of a user. The respiration monitoring system can include a computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to determine a respiratory rate of the user using the respiration data. The instructions can cause the one or more processors to, using the respiratory rate, determine a respiratory rate distribution in a first respiratory rate band and a second respiratory rate band over a duration of time. The respiratory rate distribution can indicate, over the duration of time, relative proportions of time the respiratory rate falls into the first or second respiratory rate bands. The instructions can cause the one or more processors to cause display of the respiratory rate distribution.

To provide improved medical devices or apparatus for the detection, diagnosis, treatment, prevention, etc. of respiratory-related diseases.SOLUTION: A method used in a regulator or the like associated with a respiratory therapy device may include determining whether a high flow therapy is being used by a patient and determining whether a property of a flow of air being delivered by a respiratory therapy device along an air circuit to an unsealed patient interface contains a significant oscillation within a breathing rate frequency band. The method may also include generating, dependent on the determination, a notification of whether high flow therapy is being used by the patient.SELECTED DRAWING: Figure 7a

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.