22 results about "Heart frequency" patented technology

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

The application discloses a kind of based on neural network's acoustic user identification and heartbeat monitoring earphone system and method, the system includes the earphone with microphone, acoustic analog-digital conversion equipment, the back-end service equipment at least includes: data acquisition module is used for acoustic signal transmission and reception;Data processing module is used to obtain preprocessed received signal;Human ear canal feature extraction module is used to extract MFCC feature and ear canal feature based on transfer function;User identity identification module is used to obtain trained neural network model;Heartbeat information extraction module is used to obtain processed phase information;Self-interference elimination module obtains phase signal not containing direct path interference;Heartbeat frequency calculation module is used to obtain heartbeat frequency.The application can reduce the cost of identity recognition and heartbeat monitoring, and robustness, high robustness.

The application discloses a kind of multi-person vital sign monitoring method and system based on space-time coding hypersurface, utilize the harmonic wave beam scanning area of attention generated by space-time coding hypersurface to carry out human detection, and allocate the beam of frequency orthogonal for each detected human target, to accurately estimate their respiratory and heartbeat rate.The system can monitor the vital sign of multiple people simultaneously, and accurately estimate the respiratory and heartbeat frequency of human target.The control ability of space-time coding hypersurface used in the application can reduce the noise reflected by non-related objects, thereby improving the signal-to-noise ratio of human chest echo.The application uses wireless signals, has unique advantages such as not easily disturbed by environmental factors and protecting visual privacy.Compared with traditional vital sign monitoring instruments, the non-contact characteristics of the application can reduce the stress and interference on the user, and can be used for long-term, remote health monitoring.

This application discloses a method and apparatus for acquiring human feature data. The method includes: using an ultra-wideband (UWB) radar array, the acquisition device performs real-time identification of a target human body within a signal-detectable area, and determines a three-dimensional upper body model for the identified target human body; based on the three-dimensional upper body model, the chest and abdomen region of the target human body is determined, and the chest and abdomen region is detected; based on the set of data points obtained from the detection, a contour model of the chest and abdomen region and monitoring control points in the contour model are determined; the monitoring control points include respiratory monitoring control points and / or heart rate monitoring control points; using the UWB radar array, the chest and abdomen region is detected in real time, and based on the coordinate changes of the detected monitoring control points, the respiratory rate and / or heart rate of the target human body are determined. Using this application can improve the comfort of the user being tested during human feature data acquisition.

The application discloses a kind of non-contact multimodal data acquisition and emotion monitoring method, device, system, equipment, medium and product, it is related to emotional recognition technical field.The method is first to start man-machine interaction program and target personnel are interviewed, and receive by millimeter wave biological radar module and audio and video acquisition module in the process of interview the human vital sign signal and audio and video signal collected by target personnel, then on the one hand to human vital sign signal sequentially pre-processing, signal separation processing and frequency estimation processing, obtain respiratory rate and heartbeat frequency, on the other hand to audio and video signal carries out feature extraction processing, obtains audio feature and video feature, finally according to respiratory rate, heartbeat frequency, audio feature and video feature, comprehensive analysis obtains the emotional state of target personnel, thereby can provide high-quality data support for subsequent artificial intelligence emotion early warning and evaluation model establishment, realize higher accuracy and higher reliability of emotional state analysis.

This invention discloses a millimeter-wave radar heartbeat signal extraction method resistant to respiratory harmonic interference. Addressing the challenge of estimation errors caused by high-order harmonics from large-amplitude breathing overlapping the heartbeat frequency in the frequency domain, this invention proposes a time-domain regression cancellation scheme. The method first removes echo clutter and locates the target, acquiring phase micro-motion data; then, empirical mode decomposition (EMD) is used to lock the fundamental respiratory frequency, constructing an interference matrix containing the fundamental frequency and high-order harmonics; subsequently, least squares (OLS) is used for adaptive fitting in the time domain and respiratory harmonics are removed to obtain a clean residual; finally, differential enhancement and adaptive wavelet packet transform (WPD) are combined to reconstruct the weak heartbeat signal. This invention effectively overcomes the shortcomings of traditional frequency-domain filtering in separating overlapping frequencies, eliminates "false positive" interference, and significantly improves the accuracy and robustness of heartbeat monitoring under complex respiratory backgrounds.

The application discloses a kind of respiratory heartbeat detection method, device, equipment and medium.The method comprises: identifying the phase signal of the object to be detected from the target distance image corresponding to the object to be detected;Respectively from the time domain angle and the frequency domain angle, the phase signal is separated, and the corresponding initial time domain signal component and initial frequency domain signal component are obtained;The initial time domain signal component and the initial frequency domain signal component are reconstructed, and the corresponding target frequency domain signal component is obtained;Actual breathing frequency of the object to be detected is obtained based on the target frequency domain signal component, and the actual heartbeat frequency of the object to be detected is obtained by twice harmonic weighting compensation selection to the target frequency domain signal component.The application selects more suitable heartbeat frequency from the target frequency domain signal component by using twice harmonic weighting compensation selection mode, effectively solves the false detection problem of the object to be detected in the interference state, and effectively improves the detection accuracy of breathing and heartbeat frequency.

The application discloses a design method of an alternating current stimulation Hodgkin-Huxley cardiac Purkinje fiber model, and the dynamic characteristics of the model under different alternating current parameters and different initial conditions are observed through a phase plane orbit, a waveform, a bifurcation diagram and a Lyapunov exponent spectrum, and multiple coexisting membrane action potential modes are revealed, corresponding to the same or different periodic coexisting attractors in a phase diagram. The model experiences period, quasi-period and forward or reverse period-doubling bifurcation with the change of an amplitude A or a frequency f, and it is proved that the cardiac Purkinje fiber model with alternating current injection has complex nonlinear characteristics. By changing a current I m , a sodium ion equilibrium potential E Na and a potassium ion equilibrium potential E K , heartbeat frequency rules are observed, and state conversion of the cardiac Purkinje fiber between normal, abnormal and sudden cardiac arrest is found. The application provides a reference for exploring the evolution rules of nonlinear dynamics in the HH CPF model, protecting life and heart health.

The application provides a kind of millimeter wave radar human heart rate measurement method and system based on respiratory harmonic analysis.The method uses the I / Q signal collected by millimeter wave radar, uses the extended DACM algorithm to obtain phase change information;Then analyze the time domain waveform of vital sign information, calculate the non-symmetry parameter of respiration and the pause parameter of respiratory gap, to analyze the bandwidth of dynamic adjustment notch filter, while suppressing the influence of respiratory harmonic on the heartbeat spectrum;Finally, through the time domain number peak way, the heart rate estimation range is reduced, and the Chirp-Z transform is applied to obtain higher accuracy heartbeat frequency.The heart rate measurement method of the application is simple, and compared with the previous respiratory harmonic elimination method, the strength of respiratory harmonic is more effectively analyzed and targetedly suppressed, and the measurement accuracy when the heart rate and the respiratory harmonic frequency coincide can be effectively improved.

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 discloses an FMCW radar vital sign detection method based on combination of SVMD and SSR. The method comprises the following steps: firstly, acquiring an FMCW radar echo signal, and preprocessing the FMCW radar echo signal to obtain a human chest wall micro-motion phase signal; then, an improved SVMD algorithm is adopted to decompose the human body chest wall micro-motion phase signal, the optimal balance parameter upper limit value is determined through a joint evaluation criterion, and a modal component is obtained; and finally, carrying out harmonic detection and signal reconstruction to obtain a pure heartbeat signal. And constructing an SSR model, performing sparse spectrum reconstruction based on an EB-ZA-SLMS algorithm, solving a heartbeat signal, and extracting a heartbeat frequency. Through joint evaluation of the energy loss rate and the physiological frequency band energy ratio, the optimal balance parameter upper limit value can be adaptively searched according to individual characteristics, and the modal aliasing problem is effectively solved; according to the dynamic sparse reconstruction based on energy perception, the zero attraction intensity can be adjusted in real time according to spectrum energy distribution, and high-resolution and high-robustness heartbeat frequency estimation is realized under a short-time observation window.

The application discloses a millimeter wave radar detection method for identifying multiple life breath and heart rate. The method comprises the following steps: collecting breathing signals and heartbeat signals by using a millimeter wave radar for multiple targets, wherein the collected breathing signals and heartbeat signals are signals of the targets in different distance and different angle scenes relative to the millimeter wave radar; pre-processing the collected breathing signals and heartbeat signals to obtain corresponding frequency spectrum signals, and determining target positions through peak value detection, and then separating the breathing signals and the heartbeat signals; for the separated breathing signals and heartbeat signals, performing spectrum peak search on the frequency spectrum graphs respectively to obtain estimated breathing frequency and heartbeat frequency. The application can effectively adapt to the variable and complex home environment, and improve the accuracy and generalization ability of the breath and heart rate monitoring.

The invention discloses an FMCW radar vital sign detection method combining VMD and SSR. The method comprises the following steps: firstly, transmitting a chirp signal through an FMCW radar, capturing a radar echo signal, preprocessing the radar echo signal, and extracting a human chest wall displacement signal; secondly, decomposing the human chest wall displacement signal by adopting a VMD algorithm, determining an optimal decomposition parameter by utilizing variable step size double-stage search through a vital sign judgment mode, and decomposing the human chest wall displacement signal by utilizing the optimal decomposition parameter to obtain a heartbeat signal and breathing signal modal component set; and finally, judging a breathing harmonic modal component in a heartbeat signal frequency range, and reconstructing a heartbeat signal. And constructing an SSR model, performing sparse spectrum reconstruction on the heartbeat signal based on a ZA-EFRLS algorithm, solving the heartbeat signal, and extracting the heartbeat frequency. The method solves the problems of difficulty in heartbeat signal extraction, clutter interference, insufficient frequency resolution under a short-time observation window, long heartbeat frequency extraction time and the like.

The invention discloses a steer-by-wire bilateral controller activity monitoring and diagnosing method, which comprises the following steps that: S1, a bilateral controller generates a verification PWM (Pulse Width Modulation) signal as a heartbeat signal according to the own activity state and sends the heartbeat signal to an opposite side, and meanwhile, specifies the real-time activity state of a microcontroller represented by different heartbeat signals; s2, calculating a bilateral heartbeat activity state according to the heartbeat signal within a specified window time; S3, calculating and waiting for a far side heartbeat activity state, determining waiting time according to the far side heartbeat activity state, completing power-on synchronization and starting a periodic task; s4, when a power supply is cut off, calculating and waiting for a far-side activity state in the periodic circulation task, determining waiting time according to different far-side heartbeat activity states, and completing power-off synchronization; wherein the bilateral controller presets a master side frequency and a slave side frequency, master-slave judgment is carried out by calculating the heartbeat frequency of the bilateral controller, and the heartbeat frequency of the bilateral controller is correspondingly adjusted when the master-slave relation is switched. According to the invention, active monitoring and diagnosis of the steer-by-wire bilateral controller can be comprehensively and reliably realized.

The invention discloses a large-scale non-contact vital sign monitoring system and method based on optical fibers and wireless CSI, and relates to the technical field of health physiological monitoring and intelligent sensing. The system comprises a distributed optical fiber sensing subsystem, a radio channel state information monitoring subsystem, a bimodal cooperative monitoring engine and an edge-cloud cooperative management platform. The distributed optical fiber sensing subsystem is used for outputting an optical fiber displacement time sequence; the radio channel state information monitoring subsystem is used for collecting a channel state information time sequence. And the bimodal cooperative monitoring engine is used for realizing separation of multiple human body signals, constructing a coupling consistency index based on a transfer function residual error, and estimating a respiratory frequency and a heartbeat frequency through minimum search in a respiratory and heartbeat angular frequency range. The edge-cloud collaborative management platform supports multi-room node unified management and long-term health trend analysis. According to the invention, large-scale low-cost heartbeat and respiration monitoring in a multi-human-body complex scene is realized without wearing.

This invention belongs to the field of identity recognition technology and discloses a non-contact identity recognition method, system, medium, device, and terminal. It extracts, demodulates, and differentiates the phase of vital movement signals of a target individual collected by an AWR1642 sensor. Sine fitting is used to denoise the differentiated phase signal to obtain cardiac motion information. Adaptive weight adjustment and local perturbation are used to improve the Wild Dog algorithm's support vector machine optimization, achieving identity recognition. A Butterworth low-pass filter separates respiratory and heartbeat signals, and the obtained heartbeat frequency is compared with that of a smart bracelet to verify the feasibility of using FMCW millimeter-wave radar for vital signal detection. Experimental results show that the heartbeat frequency obtained by this invention has a Pearson correlation coefficient of 0.96 with that obtained by contact methods, indicating the feasibility of FMCW radar collecting vital signals. Furthermore, the accuracy of using the improved Wild Dog optimization algorithm to optimize the SVM for real individual identity recognition can reach 88%, demonstrating that the identity recognition method of this invention can effectively improve the accuracy of recognition.

The invention belongs to the field of vital sign detection equipment, and particularly relates to a breath and heartbeat detection method, a storage medium, a program product and equipment. According to the method, two-stage detection is carried out on a target through a Doppler radar, in the first stage, only an intrinsic signal is emitted, and the respiratory frequency of a respiratory frequency band in an echo signal is positioned and recognized; in the second stage, an elimination signal capable of eliminating the breathing signal is modulated according to the known breathing frequency, the elimination effect is evaluated by combining the analysis of the echo signal, and the amplitude and the phase of the elimination signal are dynamically adjusted; and after the breathing elimination effect reaches the expectation, positioning and identifying the heartbeat frequency of the echo signal center frequency hopping band. In addition, a delay module and a data processing module are introduced into the existing Doppler radar, and then respiration and heartbeat detection equipment applying the method is obtained. According to the invention, the problems of narrow working frequency band, high computing power requirement and insufficient precision and real-time performance in the existing scheme are solved.

The invention relates to a method for imaging a living biological tissue, comprising: obtaining (S1) a plurality of successive colour images of said biological tissue, the processing method comprising a calibration phase (S2) in which at least one zonal time-dependent vector representative of the time evolution of spatial averages of intensity values of each of the colours in an area of the images is determined (S21), a bandpass filter centred on a heartbeat frequency is applied (S24), then a projection basis is determined (S26) by principal component analysis of the filtered zonal time-dependent vector, the method comprising deriving (S33) colour data representative of the time evolution of luminous intensities of the living biological tissue in the tissue images, and projecting (S35) colour data onto the projection basis.

The invention discloses a piezoelectric non-invasive monitoring device and method for pet health detection, and belongs to the technical field of pet physiological monitoring. Weak physiological vibration signals are collected through a piezoelectric sensing assembly, and basic piezoelectric signals and weight information are extracted; constructing a characteristic frequency interval based on the basic piezoelectric signal, and extracting real-time breathing frequency and heartbeat frequency; further calculating a respiratory frequency instability coefficient and a heartbeat micro-jitter amplitude, forming a feature vector, inputting the feature vector into the trained health state discrimination model, and obtaining a health abnormality score of the current time period; judging whether the physiological state is abnormal or not according to whether the score exceeds a preset threshold value or not, and if yes, dynamically correcting the frequency extraction range to realize self-adaptive health monitoring; the system has the advantages of non-contact, noninvasive, high adaptability and continuous discrimination capability, and is suitable for intelligent health management of various pets in a natural state.

The application discloses a piezoelectric non-invasive monitoring device and method for pet health detection, and belongs to the technical field of pet physiological monitoring. Weak physiological vibration signals are collected through a piezoelectric sensing assembly, and basic piezoelectric signals and weight information are extracted. A characteristic frequency interval is constructed based on the basic piezoelectric signals, and real-time respiratory frequency and heartbeat frequency are extracted. Respiratory frequency instability coefficients and heartbeat micro-jitter amplitudes are further calculated, a characteristic vector is input into a trained health state discrimination model, and a health abnormality score of a current period is obtained. Whether the physiological state is abnormal is judged according to whether the score exceeds a preset threshold value. If the physiological state is abnormal, the frequency extraction range is dynamically corrected, and adaptive health monitoring is realized. The application has the advantages of non-contact, non-invasiveness, high adaptability and continuous discrimination capability, and is suitable for intelligent health management of various pets in a natural state.

This invention proposes a high-precision heart rate estimation method and system based on millimeter-wave radar, belonging to the field of radar detection technology. The method includes: processing the target echo signal received by the millimeter-wave radar to extract a phase-time signal reflecting chest cavity vibration; extracting the respiratory fundamental frequency from the phase-time signal and generating a respiratory reference signal containing its harmonic components based on the respiratory fundamental frequency; and using an adaptive filter, with the respiratory reference signal as a reference input, filtering the phase-time signal to suppress the harmonic components generated by the respiratory fundamental frequency and separate the initial heartbeat signal. This invention enhances the high-frequency components of the heartbeat signal while suppressing noise amplification, significantly improving the quality of the heartbeat signal in low signal-to-noise ratio environments. Employing a high-resolution spectrum estimation method, it can clearly extract the heartbeat frequency spectrum peaks from the enhanced signal, thereby achieving high-precision and robust heart rate estimation in complex environments.

The invention belongs to the technical field of electrical impedance imaging, and particularly relates to a self-reference pulmonary pulse blood flow impedance signal gating extraction method and system.The method comprises the steps that an electrical impedance signal is obtained, spectral analysis is conducted on the electrical impedance signal, and a peak frequency is obtained; performing clustering analysis on the peak frequency to obtain a respiratory frequency set and a heartbeat frequency set; determining a reference impedance signal based on the heartbeat frequency set; based on the reference impedance signal, obtaining a single-heart-cycle electrical impedance signal through cutting and superposition; based on the single-cardiac-cycle electrical impedance signals, pulsatile blood flow electrical impedance signals of the average cardiac cycle are determined, the maximum value moment of the pulsatile blood flow electrical impedance signals of the average cardiac cycle serves as a reference frame, the minimum value moment serves as an imaging frame, and a spatial distribution diagram of the average pulsatile blood flow is obtained. Through spectral analysis and clustering analysis, the method does not need to dissect prior information to help select channels which can be regarded as reference signals, and does not need to synchronously collect electrocardiosignals to identify the cardiac cycle.