7 results about "Heart frequency" patented technology

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 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 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.

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 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.

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.