414 results about "Pulse wave" patented technology

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention discloses an electrophysiology and hemodynamics multi-mode intelligent wearable system and a data evaluation method, and belongs to the technical field of biomedical engineering, the system comprises an optical module, a flexible stretchable electric signal module and a central module; the optical module is used for collecting hemodynamic signals including an oxyhemoglobin change signal, a deoxygenated hemoglobin change signal and a pulse wave signal by combining a near-infrared light signal with hemodynamics; the flexible stretchable electric signal module collects electric signals, including electroencephalogram signals, electromyographic signals, electrocardiosignals and electro-oculogram signals, of all parts through electrodes; the central module is used for digitalizing and preprocessing the hemodynamic signal and the electric signal of each part, fusing multi-modal data, evaluating data quality and predicting a dominant external event influencing the data quality; and meanwhile, data quality and dominant external events are output to the terminal to feed back and adjust acquisition parameters of the optical module and the flexible stretchable electric signal module, so that the effectiveness and practicability of monitoring are remarkably improved.

The invention relates to the technical field of medical monitoring, in particular to a wrist signal collecting and processing method and system based on biological tissue analysis. The system is used for generating wrist multi-signal acquisition optimization equipment by designing and integrating wrist wearable equipment corresponding to a photoelectric volume pulse wave sensor, an electrocardio sensor and a skin electric response sensor and performing position structure optimization design; the method comprises the following steps: acquiring PPG signals, ECG signals and GSR signals corresponding to wrist biological tissues in real time by using wrist multi-signal acquisition optimization equipment, and performing high-frequency drift preprocessing and biological tissue characteristic analysis to obtain a wrist biological tissue signal characteristic set; and performing feature redundancy and dimension reduction processing on the wrist biological tissue signal feature set, and performing wrist physiological state assessment analysis to obtain a wrist biological tissue state assessment result. According to the invention, accurate acquisition and analysis of wrist signals can be realized, so that reliable data support is provided for personalized analysis.

The invention provides a cuff-free continuous blood pressure signal reconstruction method and system, and relates to the technical field of blood pressure monitoring, and the method comprises the steps: obtaining a PPG signal and an ECG signal which are synchronized; respectively extracting a PPG feature vector and an ECG feature vector from the PPG signal and the ECG signal by using the trained attention sequential network, carrying out multi-modal fusion on the PPG feature vector and the ECG feature vector, and reconstructing a cuff-free continuous blood pressure signal based on the fused feature vectors; according to feature vector extraction, an information screening machine and a time sequence learning machine are additionally arranged between an encoder and a decoder and are respectively used for learning a channel probability distribution vector and a time sequence weight vector, guiding the decoder to decode high-level semantic features and decoding the sequential relation between PPG and ECG; the attention sequential network is provided on the basis of multi-modal fusion, and continuous blood pressure signal reconstruction is achieved by analyzing photoelectric volume pulse wave signals and electrocardiosignals.

The invention discloses a drilling and milling machining center for hardware product production, and relates to the technical field of mechanical equipment, the drilling and milling machining center comprises an initial adjustment assembly, the side end of the bottom of the initial adjustment assembly is fixedly connected with a bearing base, and the top of the bearing base is provided with an active shock absorption assembly; in the invention, under the cooperation of the active shock absorption assembly, the execution calibrator adjusts the frequency modulation pulse wave generator to generate corresponding phase and amplitude pulse wave signals according to the reverse signal, so that the phase and amplitude pulse wave signals counteract the vibration source signal, the vibration of the vibration source is actively counteracted, and weak vibration offset during processing is effectively avoided; the machining quality of hardware products is affected, the consistency of follow-up products is affected due to the fact that the variation which is difficult to perceive exists during drilling or cutting, under the cooperation of the machining synchronous regulation and control assembly, the machining angle can be actively adapted according to the position of the operation tool, and under the cooperation of the rotating disc rail, the machining requirements of different angles are met; and machining errors are reduced.

The invention discloses a multi-mode-based flexible wearable heart function monitoring method, system and device, and relates to the technical field of health monitoring, and the method comprises the steps: outputting a multi-mode signal through a flexible patch sensing module when the flexible patch sensing module is arranged in a detection area; wherein the flexible patch sensing module comprises an electrocardio sensor, a pulse wave sensor, a heart sound sensor, a strain sensor and a microfluidic biosensor. According to the invention, high-precision synchronous signal acquisition is realized through flexible patch integrated multi-modal sensing, heart multi-source signal features are adaptively extracted in combination with a multi-scale convolutional neural model, and timing sequence relevance is captured by using a bidirectional long-short-term memory network, so that the accuracy and reliability of heart disease diagnosis are remarkably improved, and the accuracy and reliability of heart disease diagnosis are improved. Meanwhile, non-invasive and continuous monitoring and early pathological screening are supported.

The invention provides a pulse wave signal (BVP signal) extraction method based on remote photoplethysmography. The method comprises the following specific steps: step 1, acquiring a face video and detecting a face area; in the process, the system captures, recognizes and extracts all facial feature points; 2, the rPPG signal intensities of different face areas are different, and in order to maximize the signal quality, the face areas are divided according to the intensities of remote photoplethysmography signals (rPPG signals); 3, training a convolutional neural network model, calculating a weighting coefficient of each region according to the characteristics of the signals of each region, and fusing the signals of each region into a single-channel signal; 4, removing low-frequency noise and interference signals from the fused signals by using a filtering technology, wherein the superposition operation helps to enhance the effective rPPG signals; through the processing steps, the quality of the BVP signals can be improved, and high-quality input data is provided for subsequent extraction of blood oxygen, respiration, heart rate variability and other signals.

The embodiment of the invention provides a detection module and electronic equipment. The detection module comprises a pressurizing piece and a film sensor. And the pressurizing piece is used for applying pressure to the measuring part. The film sensor is used for measuring a pulse wave signal of the measuring part and is arranged between the pressurizing piece and the measuring part. According to the detection module and the electronic equipment provided by the embodiment of the invention, the difficulty of miniaturization of the electronic equipment can be reduced.

The invention discloses a work memory measurement and training integrated quantitative evaluation method for piano playing, which realizes the integration of work memory measurement and training by quickly memorizing a music score and reproducing the piano playing of playing music and combining human body physiological monitoring and piano. The method comprises the following steps: collecting pulse waves during individual static sitting and piano playing training, and recording playing performance characteristics such as piano playing accuracy and the like, and response time and accuracy of executing a work memory task normal form N-back after training; extracting physiological features such as heart rate based on the preprocessed pulse wave signal; constructing a distribution model based on N-back reaction time, and obtaining a score for model training reference by combining accuracy; and establishing a random forest evaluation model in combination with the physiological features and the playing performance features, and quantitatively evaluating the work memory of the individual and the work memory training effect of piano playing. According to the invention, music training and physiological signal acquisition are combined, and an objective and generalizable scheme is provided for work memory evaluation and training.

The invention discloses a time domain azimuth wide bunching imaging method based on sub-pulse diversity, and the method comprises the steps: constructing a multi-frequency sub-pulse wide bunching signal model, enabling different sub-pulse beams of a radar to point to different imaging regions, and obtaining a total echo signal received by the radar; according to the characteristics of a radar system, the pulse repetition frequency and synthetic aperture time of the radar are designed; and based on the pulse repetition frequency and synthetic aperture time of the radar, performing wide-range bunching imaging processing on the total echo signal received by the radar to obtain a complete image with large azimuth imaging breadth. According to the invention, the single pulse of the radar is divided into a plurality of sub-pulses, and the different sub-pulses point to different azimuth positions, so that the radar has the azimuth wide bunching capability; the application of a sub-pulse diversity technology in an imaging system is effectively realized, and the problems of high operation complexity and distance resolution loss of a scene edge part of a method for applying a frequency scanning radar to an azimuth array are solved.

The invention provides a blood pressure detection method and device based on a frequency modulated continuous wave radar and electronic equipment, and can be applied to the technical field of signal processing. The method comprises the steps that a radar echo signal is acquired, and the radar echo signal is acquired at a preset height position away from a neck-chest area of a target object; performing beam focusing processing and phase demodulation processing on the radar echo signal to obtain pulse wave signals at different positions and angles in the neck-chest area; pulse wave time features and pulse wave clustering features of the pulse wave signals are extracted, the pulse wave time features represent phase information of pulse waves collected at different positions in the neck-chest area, and the pulse wave clustering features represent the similarity between the pulse waves collected at different angles in the neck-chest area; inputting the pulse wave time features and the pulse wave clustering features into a blood pressure detection network, and outputting a blood pressure detection result of the target object.

The invention relates to the technical field of vehicle safety, and discloses an in-ear EEG and PPG technology-based driver state detection and vehicle control system, which comprises an acquisition module, a data processing and transmission module and a vehicle machine system which are connected in sequence, the acquisition module comprises a camera, an EEG acquisition module used for acquiring in-ear brain wave signals and a PPG acquisition module used for acquiring in-ear heart sound signals and pulse wave signals. The data processing and transmission module comprises a signal processing unit and a transmission unit, the signal processing unit is a WANDmini module, and the transmission unit is Bluetooth; the signal processing unit processes the signal acquired by the acquisition module and then transmits the signal to the vehicle machine system through the transmission unit; and the vehicle machine system receives and processes the data of the data processing and transmission module, divides the data into a normal condition and an abnormal condition, and executes operation according to different conditions. The system monitors the physiological state of a driver in real time, timely finds out dangers such as fatigue and distraction, and performs active intervention, so that the occurrence rate of traffic accidents is reduced.

The invention belongs to the technical field of blood oxygen detection, and particularly relates to a non-invasive blood oxygen detection method. The main machine shell is arranged on the upper portion of the blood oxygen machine, a blood oxygen sensor is arranged below the blood oxygen machine and electrically connected with the main machine shell, the main machine shell comprises a single chip microcomputer, an analog front end driving module, a display module, a circuit module, a signal receiving module, a digital signal processing module and a storage module, and meanwhile a lithium battery is arranged in the main machine shell. A power supply is provided for the oximeter; the blood oxygen sensor is directly connected with the signal receiving module to form a blood oxygen signal receiving module, and the blood oxygen signal receiving module is connected with the digital signal processing module to convert a voltage signal into a digital signal; according to the invention, the problem that detection signal pulse wave output signals are interfered by serious noise such as environmental noise and hardware circuits is solved; the problems of signal loss and signal superposition caused by a traditional filtering method are solved.

The invention discloses an oxyhemoglobin saturation detection method and device, noise reduction equipment, a storage medium and a product, and relates to the technical field of artificial intelligence. The oxyhemoglobin saturation detection method comprises the steps that an initial pulse wave signal collected by photoplethysmography detection equipment is received, and whether a noise signal exists in the initial pulse wave signal or not is judged; if the noise signal exists in the initial pulse wave signal, the initial pulse wave signal is input into a noise reduction model to obtain a target pulse wave signal, and the noise reduction model is obtained through training by taking a historical pulse wave signal as input data and taking the denoised historical pulse wave signal as a label; the target pulse wave signal is sent to the photoplethysmography detection equipment, and the photoplethysmography detection equipment carries out blood oxygen concentration detection based on the target pulse wave signal. The accuracy of blood oxygen saturation detection is improved.

The invention belongs to the field of petroleum drilling, particularly relates to a downloading pulse signal judgment and pulse width adaptive identification method, system and device, and aims to solve the problem that a downloading instruction after the pulse width is changed cannot be automatically identified. The method comprises the following steps: acquiring pulse frequency data according to a set sampling period; filtering the collected pulse frequency data through a first smooth filtering window and a second smooth filtering window in sequence to obtain the average rotating speed and pulse waveform data of the turbine generator; and taking a curve corresponding to the pulse waveform data as a first curve, selecting a set number of points on the first curve, and determining the pulse width according to the distance between every two pulse wave crests. Under the working conditions of different well depths and different mud densities, the high downloading success rate is achieved, the pulse width can be increased at any time on the ground, the situation that a downhole tool is pulled out of the ground for resetting is not needed, and the drilling efficiency is greatly improved.

The invention discloses a diabetes detection system based on multi-sensor pulse characteristics, and relates to the field of medical signal processing, and the diabetes detection system comprises a pulse wave data acquisition module which uses a pressure sensor and a photoelectric sensor to collect wrist pressure pulse waves and fingertip photoelectric volume pulse waves of a user; the preprocessing module is used for obtaining usable single-cycle pulse wave data; the signal fusion module is used for converting the pressure pulse wave signal and the photoelectric volume pulse wave signal into two-dimensional image signals through continuous wavelet transformation so as to reserve time-frequency domain information, and processing two two-dimensional images by using non-subsampled shear wave transformation and a pulse coupling neural network; the feature extraction and calculation module is used for extracting pulse wave image features through a ResNet network and finally performing classification through a random forest; according to the detection system, the fusion effect is improved under the condition that the calculated amount is not increased, the effect of noninvasive detection of diabetes by using multiple sensors is achieved, and the detection accuracy is improved.

The invention provides a method for collecting finger pulse health data based on a mobile phone camera of a mobile terminal, which comprises the following steps: S1, collecting a video, S2, preprocessing a photoelectric volume pulse wave PPG signal, S3, extracting an original photoelectric volume pulse wave PPG signal, S4, denoising the pulse wave signal, S5, averaging a single-cycle pulse wave, S6, calculating a pulse condition signal threshold-free recurrence plot, and S5, obtaining the finger pulse health data based on the pulse condition signal threshold-free recurrence plot. S7, establishing a pulse condition signal ensemble classification model; and S8, outputting a pulse condition classification analysis result. According to the method, the pulse oscillogram with high characteristic signals is obtained through deep algorithm research, in-depth systematic analysis is carried out on the pulse oscillogram in combination with medical theoretical knowledge, and effective and high-accuracy human health data analysis is established.

The invention relates to the technical field of intelligent medical treatment, in particular to an arrhythmia diagnosis system based on multi-sensor fusion, and the system comprises an electrocardiosignal module which comprises an electrocardiosignal sensor and is used for collecting electrocardiosignals; the pulse wave signal module comprises a pulse wave sensor and is used for collecting pulse wave signals; the three-axis acceleration module comprises an acceleration sensor and is used for collecting acceleration signals; the main control chip module serves as a data processing module and is used for receiving and analyzing data to obtain an arrhythmia diagnosis result; the Bluetooth module serves as a communication module and is used for transmitting the electrocardiosignal, the pulse wave signal, the acceleration signal and the arrhythmia diagnosis result to a mobile terminal; and the power management module comprises a charging circuit, a power supply circuit and a switching circuit and is used for providing power supply for the system. The system provided by the invention is stable, portable and low in power consumption, and has important significance on prevention of cardiovascular diseases and electrocardiogram monitoring of the Internet of Things.

The invention provides a continuous identity authentication method based on multichannel PPG signals in an uncontrolled environment. Firstly, a smart watch is used for collecting wrist double-channel green light, red light and infrared light PPG signals and motion sensor data, and original information is obtained. Aiming at the problem that an uncontrolled environment signal is easily interfered, noise reduction and optimization are carried out on the signal through abnormal value processing, FIR high-pass, band-pass and multi-band-pass filtering and ICA independent component analysis, so that motion artifacts are effectively inhibited, and the signal quality is improved. Then, based on pulse wave valley positioning segmentation signals, a multi-channel standardized data set is constructed; and finally, by means of an Inception-LSTM neural network fusing multi-scale feature extraction and time sequence modeling, end-to-end learning of biological features is carried out, and a high-precision identity authentication decision in a dynamic environment is realized. According to the method, the robustness is verified in seven motion state simulation scenes, and the authentication accuracy and reliability in an uncontrolled environment are remarkably improved.

The invention discloses a mobile energy storage ice melting system and method based on intelligent control of a chopper circuit. The ice melting device comprises a switch module, a chopping ice melting module, an energy storage power supply module and a control module, the energy storage power supply module, the chopping ice melting module and the switch module are sequentially connected to form a loop, two ends of an ice coating line are connected to the loop, and the control module is used for calculating current needed by ice melting, calculating voltage needed by ice melting according to the current needed by ice melting and controlling the voltage needed by ice melting. According to the voltage required by ice melting and the actual output voltage of the chopping and ice melting module, the switch module is controlled to select the chopping and ice melting module to boost or buck; the total capacity required by ice melting in the ice melting process of the ice-coated line is calculated according to the current required by ice melting, and the capacity and the number of the energy storage power supply modules are selected according to the total capacity required by ice melting. The output current is direct current, an alternating current power supply does not need to be rectified into a direct current power supply through six-pulse wave or twelve-pulse wave rectification, and the advantages of being high in efficiency, small in size, easy to integrate and low in cost are achieved.

The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.

The invention discloses an underground drilling depth measurement method and system based on time domain reflection, and belongs to the technical field of drilling measurement, and the technical scheme comprises the steps: injecting a first pulse signal at the head end of a to-be-measured cable, and receiving a second pulse signal which is a reflection signal of the first pulse signal at the tail end of the to-be-measured cable, the to-be-tested cable is located in a to-be-tested well, and the head end of the to-be-tested cable is located at the wellhead of the to-be-tested well; the injection time of the first pulse signal and the receiving time of the second pulse signal are recorded, and the time difference between the injection time and the receiving time is obtained; obtaining the depth of the to-be-measured well according to the time difference and the pulse wave velocity; measurement is carried out based on the time domain reflection principle, the principle is simple and easy to understand, the actual operation difficulty is small, the sensitivity is high, the problem of inaccurate measurement caused by cable tension and cable elastic deformation in the prior art is solved, and meanwhile, the signal processing and waveform correction algorithm is adopted, so that the measurement accuracy is further improved.

The invention provides an isolated power supply circuit driven by a transistor based on a mirror voltage source, which outputs two paths of reverse periodic pulse waves through a wave sending module, generates a target driving voltage through a push-pull transformation module according to the periodic pulse waves, and outputs an isolated driving voltage through an isolation module according to the target driving voltage. The power supply output voltage is output through the voltage stabilizing module according to the isolation driving voltage, so that the power supply has relatively high loading capacity; in addition, the invention provides an isolation power supply control method driven by a transistor based on a mirror image voltage source, according to output voltages of two mirror image voltage stabilizing circuits of a voltage stabilizing module, a control quantity is obtained according to a set adjusting quantity calculation rule and is used for adjusting a first resistor of a wave sending module, so that the output voltages of the two mirror image voltage stabilizing circuits are balanced; and the frequency of the output voltage is stabilized by automatically adjusting a second resistor and a third resistor of the wave generating module, so that the reliability of the output voltage of the power supply is improved.

The invention provides a pulse wave identification processing method. The pulse wave identification processing method comprises the steps of data acquisition, period extraction, filtering processing and data identification. Pulse condition data are acquired through data acquisition, and periodic extraction is carried out to determine periodic characteristics of pulse waves; then, processing the data by adopting a filtering processing method so as to remove interference signals and noise, and extracting a target pulse wave signal; and constructing a mathematical model by using the extracted features, and carrying out feature matching and screening so as to realize accurate identification of the pulse condition. According to the method, the identification efficiency can be remarkably improved, and a feasible solution is provided for the medical field.

The invention relates to the technical field of medical instruments, and particularly provides a non-contact blood oxygen monitoring method and system based on mobile phone direct-current component calibration. The method comprises the following steps: acquiring a hand photoelectric volume pulse wave signal of a target user based on a constructed hand reflective light path of the target user, and extracting a hand direct current component and a hand pulsation component; first face video information of the target user is obtained, a face photoelectric volume pulse wave signal of the target user is collected, and a face direct current component is extracted; constructing a blood oxygen calibration model according to the hand direct current component, the hand pulsation component and the face pulsation component; second face video information of the target user is obtained, a direct current component in the second face video information is extracted and input into the blood oxygen calibration model, and current blood oxygen information of the target user is obtained. Through cooperative work of the front camera and the rear camera, the accuracy of continuous monitoring only depending on the front camera when the mobile phone camera is applied to daily blood oxygen monitoring is improved.

The invention relates to the technical field of vital sign monitoring, in particular to an information interaction control method and system of a vital sign monitor. The method comprises the following steps that physiological signals are synchronously collected through a sensor array, wherein the physiological signals comprise electrocardio waveform data, electric volume pulse wave data, motion acceleration data and body surface temperature data; time alignment is conducted on the electrocardiogram waveform data, the electric volume pulse wave data and the motion acceleration data based on a clock synchronization protocol, matrix construction is conducted, and a synchronous vital sign matrix is generated; carrying out risk change trajectory construction of a pathological trajectory on the filtering data and the historical case data, generating a vital sign risk trajectory, and carrying out super-boundary judgment on the vital sign risk trajectory; by means of the high-precision synchronization mechanism, it is ensured that the internal relevance of electrocardio, pulse waves, movement and temperature data is accurately captured.

The invention discloses an indoor and outdoor seamless data synchronization device and method, the device comprises a GNSS module, an inertial measurement module and a programmable control module, the GNSS module is used for receiving a 1PPS signal and GPRMC information generated by an external atomic clock; the inertial measurement module is used for performing fusion processing on the position information generated by the GNSS module to generate position, attitude and speed information; the programmable control module is connected with the GNSS module and the inertial measurement module through a serial port, and is used for receiving a 1PPS signal and GPRMC information of the GNSS module, aligning a square wave generated by a local clock with a square wave of the 1PPS signal of the GNSS module, performing frequency division on the square wave according to a preset frequency, continuously and stably outputting a pulse wave signal and time information of a corresponding frequency through the serial port, and transmitting the pulse wave signal and the time information to the inertial measurement module. Clock signals are calibrated through external calibration factors, an algorithm model is arranged in the programmable control module, and when indoor and outdoor data are synchronized, the algorithm model is called to carry out deviation calculation and smooth processing on signals needing to be aligned, so that the indoor and outdoor data are synchronized.

The invention relates to a living body detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pulse wave signal of a to-be-detected object; performing frequency spectrum transformation on the pulse wave signal to obtain a frequency domain amplitude spectrum in a preset frequency band; acquiring a fundamental wave frequency of the pulse wave signal; determining a plurality of harmonic frequencies in a preset frequency band according to the fundamental wave frequency; and determining whether the to-be-detected object is a living body or not according to the amplitude corresponding to the fundamental wave frequency, the amplitudes corresponding to the plurality of harmonic frequencies and the amplitude corresponding to each frequency in the frequency domain amplitude spectrum. According to the method, frequency spectrum transformation is carried out on the pulse wave signal, whether the to-be-detected object is a living body or not can be identified according to the relation between the amplitude corresponding to the fundamental wave frequency, the amplitude corresponding to the harmonic frequency and the amplitude corresponding to each frequency in the frequency domain amplitude spectrum, only the pulse wave signal needs to be processed, and the living body detection efficiency is improved.

The invention discloses a video pulse wave extraction method and system based on automatic noise recognition. The method comprises the steps that a face area is divided into a plurality of sub-areas; calculating the signal-to-noise ratio of each sub-region in the pulse frequency band, and performing weighted fusion on the original pulse signals of each sub-region according to the signal-to-noise ratio; performing ensemble empirical mode decomposition on the fusion signal; according to the peak-to-peak interval variation coefficient, the amplitude stability index and the spectrum purity, determining a noise component dominated by the motion artifact; performing adaptive filtering processing on the fused signal by using an adaptive filter and taking a reference noise signal as input; carrying out distortion detection on the filtered fusion signal, and carrying out signal restoration processing on a detected distortion region to obtain an extracted pulse signal; in order to solve the problem that in the prior art, when video pulse signals are extracted, motion artifacts and physiological signal frequency spectrums are overlapped, and consequently signal separation of a traditional frequency domain filtering method fails, effective signal separation and accurate pulse signal extraction are achieved.