21 results about "Respiration rhythm" patented technology

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 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 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.

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.

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.

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 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.

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.

The application discloses a non-contact respiratory monitoring method based on multi-distance unit phase fusion, obtains in-phase components and quadrature components corresponding to millimeter wave radar receiving echoes, obtains intermediate frequency signals through frequency mixing, low-pass filtering and discrete sampling; the intermediate frequency data is subjected to distance transformation, and the target distance region corresponding to the human chest is determined based on dynamic echo enhancement and energy statistics; a plurality of adjacent distance units are selected from the target distance region to form a multi-channel phase sequence; the multi-channel phase sequence is input into a phase fusion reconstruction network to reconstruct a target respiratory waveform; frequency domain analysis is performed on the reconstructed respiratory waveform, main frequency components in a respiratory frequency band are extracted, and a respiratory rate estimation result is obtained; the phase fusion reconstruction network is supervised and trained by using a synchronously obtained respiratory true value waveform, and network parameters are updated based on waveform reconstruction error, morphology consistency constraint and timing smoothing constraint. The application aims to improve the accuracy of respiratory waveform reconstruction and the accuracy of respiratory rate estimation.

<pre>The Intelligent Monitoring System is an integrated solution for managing and monitoring chronic respiratory diseases. It combines a patient-facing mobile application with a platform dedicated to healthcare professionals, enabling continuous monitoring and collaborative management. The system collects physiological parameters—such as heart rate, oxygen saturation (SpO₂), and electrocardiogram (ECG) and photoplethysmogram (PPG) signals—using smartwatch sensors. It also records lung sounds via an electronic stethoscope and measures environmental data (air quality, temperature, and humidity) using specialized sensors and a public API. A smart inhaler is integrated to guide patients in its use, thereby boosting treatment adherence and ensuring greater therapeutic efficacy. By leveraging Edge AI, the system predicts key parameters like respiratory rate and analyzes lung sounds directly within the mobile app to detect respiratory abnormalities. Relying on edge computing, it offers real-time processing, reducing cloud dependency while maintaining high accuracy. The system helps patients quickly recognize critical symptoms, monitor vital signs and environmental triggers, and track treatment adherence. Data is shared instantly and securely with healthcare professionals via the platform, facilitating rapid interventions and reducing the need for emergency care. By integrating these features, the system promotes proactive respiratory disease management, prevents exacerbations, and improves patients' quality of life.< / pre>

The invention discloses a method and a device for eliminating respiratory artifacts of uterine myoelectricity. The method comprises the following steps: S1, acquiring an abdominal multichannel signal and a respiratory rate RR to obtain a uterine myoelectricity original data sequence X; s2, subtracting the mean value from the original data sequence to obtain zero mean value data; s3, carrying out linear transformation on the zero mean value data; s4, solving zero-mean data after linear transformation to obtain an orthogonal matrix W by searching for a direction in which negentropy or kurtosis is approximately maximized; s5, constructing a two-dimensional comprehensive scoring function, and identifying a respiratory artifact vector VR; s6, calculating a projection component VRX of the respiratory artifact vector VR in the original hybrid matrix X, and removing the projection component from the original signal to obtain a signal matrix X'after preliminary removal of the respiratory artifacts; and S7, carrying out band-pass filtering on the signal matrix X'to obtain a pure uterine electromyographic signal with a high signal-to-noise ratio. The method can effectively eliminate the interference of the respiratory artifacts on the uterine myoelectricity signals, and improves the signal-to-noise ratio of the collected uterine myoelectricity.

The invention relates to the technical field of human body rhythm, in particular to an intelligent sleep aiding method, device and equipment and a storage medium, and the method comprises the steps: determining a fluctuation degree adaptive threshold value and a variation coefficient adaptive threshold value based on the collected initial heart rate and initial respiration rate and a preset resting time length; determining a heart rate instantaneous fluctuation degree based on the initial heart rate corresponding to the heart rate sliding window; determining a respiratory rate variation coefficient based on the initial respiratory rate corresponding to the respiratory rate sliding window; and starting an electromagnetic field generator in response to the conditions that the heart rate instantaneous fluctuation degree is greater than the fluctuation degree self-adaptive threshold value, the respiration rate variation coefficient is greater than the variation coefficient self-adaptive threshold value and a preset judgment duration is continued. According to the application, the sleep-aiding precision, the sleep-aiding effect and the sleep-aiding intelligent level of sleep aiding of the user can be improved.

The utility model provides a kind of human core body temperature detection device, the device includes: fixed band, and single-channel heat flow method sensor, respiratory sensor, electrocardiosensor and data processing unit fixed to one side of the fixed band;The single-channel heat flow method sensor, respiratory sensor, electrocardiosensor are electrically connected with the data processing unit respectively.The utility model collects original body temperature data by single-channel heat flow method sensor;Respiratory rate data are collected by respiratory sensor;Heart rate data are collected by electrocardiosensor;Data processing unit dynamically adjusts the weight of original body temperature, heart rate, respiratory rate, to calculate pre-core body temperature and body temperature compensation amount by two-stage fusion algorithm;Human core body temperature is obtained by the core body temperature compensation amount correction pre-core body temperature data.The utility model improves the accuracy and robustness of body temperature detection by integrating the dynamic changes of heart rate (HR) and respiratory rate (RR).

The application discloses a pet wearable vital sign monitoring method based on a flexible sensor, and comprises the following steps: constructing a flexible multi-modal sensing interface, embedding a sensing layer into a sticking layer and fixing the sensing layer on the neck or chest of a pet; outputting a low-power thermal pulse to detect contact characteristics and form a stable sticking interface; collecting multi-modal signals and synchronously correcting the signals, performing filtering and baseline processing to obtain a processed signal set; extracting multi-modal features, constructing a physical tensor, and calculating vital sign parameters; extracting a body temperature, heart rate and respiration rhythm component, generating a rhythm resonance curve, and determining target configuration parameters; and adjusting sampling and communication according to the target configuration, triggering an alarm when an abnormality occurs, and uploading the alarm to a remote terminal. Through the construction of the flexible multi-modal sensing interface and the combination of the physical consistency driven time sequence reasoning and rhythm energy scheduling mechanism, the pet vital sign can be monitored with high precision, low power consumption and long-term stability.

The present application relates to a kind of chest band type physiological monitoring method for monitoring empty center arrhythmia and flight fatigue, belong to aviation medicine and machine learning field, solve the problem that existing method cannot carry out real-time accurate monitoring to arrhythmia and flight fatigue.The chest band type monitoring equipment is used to obtain the physiological data of pilot in real time, and the physiological data includes single-lead electrocardiogram signal, blood oxygen saturation and respiratory wave signal;Scalogram wavelet scale diagram and HRV feature are obtained based on the preprocessed electrocardiogram signal;Respiratory rate, respiratory amplitude and respiratory irregularity are obtained based on the respiratory wave signal;Scalogram wavelet scale diagram is input into trained heart rhythm classification model to obtain pilot heart rhythm grade;HRV feature, respiratory rate, respiratory amplitude, respiratory irregularity and blood oxygen saturation are input into trained fatigue classification model, and pilot fatigue grade is obtained;Based on pilot heart rhythm grade and fatigue grade, pilot comprehensive risk grade is obtained by using double-layer threshold fusion, and is used to evaluate the physiological state of pilot.

The subject technology provides a framework for estimating respiratory rates from audio data recordings. A multi-task learning network may be trained to output respiratory rates, breathing conditions, and / or noise conditions based on input audio data recordings. The audio data recordings may be generated using wearable audio devices with near-field microphones. The respiratory rates may be provided along with other workout information by a health application of an electronic device. Additional sensor data and / or health data may be used in combination with the audio data and / or the respiratory rates and / or breathing conditions for respiratory and / or other health monitoring by an electronic device.