311 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 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 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 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 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 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 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.

The invention discloses wrist blood pressure measuring equipment, a wrist blood pressure measuring method and a storage medium. The equipment comprises a pulse wave signal acquisition sensor, an air pressure sensor, a wrist strap and a processor, the pulse wave signal acquisition sensor is arranged on the wrist strap, and the pulse wave signal acquisition sensor is provided with a plurality of pulse wave signal acquisition channels; the air pressure sensor is arranged on the wrist strap; the processor is used for acquiring multiple pulse wave signals at different positions simultaneously acquired by the multiple pulse wave signal acquisition channels within a first time period; a first blood pressure measurement value is calculated based on the pulse wave signals, collected at the same time, of the multiple different positions; acquiring an air pressure signal acquired by an air pressure sensor in a first time period; calculating a second blood pressure measurement value based on the air pressure signal; and calculating the blood pressure measurement value of the measured object based on the first blood pressure measurement value and the second blood pressure measurement value. When the blood pressure is measured, the blood pressure measurement value of the measured object is calculated according to the first blood pressure measurement value and the second blood pressure measurement value, so that the blood pressure measurement precision is improved.

The invention provides a method and a system for reading human body pulse health data based on a face video. The method comprises a method for reading the human body pulse health data and a pulse condition health intelligent detection system. The invention discloses a method for reading human pulse health data based on a face video. The method comprises the steps of S1, collecting the video, S2, positioning and tracking an area of interest, S3, extracting a photoelectric volume pulse wave PPG original signal, S4, carrying out pulse wave signal denoising processing, S5, carrying out pulse wave signal feature analysis, S6, carrying out a mixed feature selection algorithm, S7, carrying out optimal feature classification, and S8, outputting a pulse condition classification analysis result. The pulse condition health intelligent detection system comprises a physiological acquisition layer, a signal transmission layer, a cloud server layer and an application interaction layer. The method is convenient and fast to operate, can visualize the physiological indexes such as the pulse condition of the human body, helps a user of the health detection system to conveniently and timely know the own physiological health condition and timely see a doctor for treatment under the condition that the body has a health problem, and reduces the delay of an illness state.

The invention discloses a steel slag content measuring method based on a phase change detection technology, and relates to the technical field of ferrous metallurgy, the method comprises the following steps: constructing an electromagnetic detection device consisting of a coil sensor, a pulse excitation generator, a phase detector and a computer, and installing the electromagnetic detection device at a steel ladle tapping hole; a pulse excitation generator is controlled by a computer to generate pulse waves with fixed frequency, the pulse waves are amplified and then act on a coil sensor, an alternating electromagnetic field is generated, and then alternating eddy current is generated in molten steel; a phase detector is used for detecting phase change caused when steel slag in molten steel flows through the coil sensor, and a detected signal is preprocessed and sent to a computer to be processed; and the computer receives and analyzes the signal transmitted by the phase detector, extracts the phase change quantity from the signal, calculates the steel slag content in the molten steel, sets a steel slag content alarm threshold value, and sends an alarm signal and triggers corresponding operation when the steel slag content exceeds the threshold value.

The invention discloses an intelligent bracelet integrated with photoelectric volume pulse wave detection and a data processing method thereof, and belongs to the field of intelligent bracelets.The intelligent bracelet comprises a host, a display screen is arranged on the upper surface of the host, an acquisition sensor is arranged on the lower surface of the host, and a control knob is arranged in front of the outer surface of the host; through the detachable structure of the watchband, the mounting groove and the inner groove are formed in the left end and the right end of the host and matched with the clamping block, the pinch bar, the spring and the button of the watchband to form a quick dismounting mechanism, the pinch bar can be driven to be separated from the clamping groove of the clamping block by pressing the button, the watchband can be dismounted by hands without tools, and the operation time is shortened to be within 3 seconds; in order to solve the problem that a traditional watchband is tedious to replace and easy to damage, the method comprises the following steps: in a signal preprocessing stage, combining band-pass filtering and adaptive Kalman filtering, and synchronously introducing motion data of an acceleration sensor and a PPG signal signal-to-noise ratio evaluation mechanism, when SNRlt; and the acquisition time is automatically prolonged or a user is prompted to stabilize the hand at 10dB.

The invention provides a high-precision low-jitter waveform generation method and device based on an FPGA, and relates to the technical field of signal processing. According to the phase accumulated value obtained through preprocessing, calculation is carried out according to a preset mathematical model, and a model output value is determined; performing phase comparison according to the phase accumulated value, and performing waveform selection in combination with a model output value to obtain an initial output waveform; according to the phase accumulated value, generating a sine wave through a preset CORDIC IP; according to the initial output waveform and the sine wave, waveform selection is carried out, and a secondary output waveform is determined; and performing digital-to-analog conversion according to the secondary output waveform and a preset reference clock signal, and outputting a final waveform. By combining the high-precision characteristic of DDS frequency control and the flexibility of edge interpolation, low-jitter pulse waves with adjustable parameters such as edge time, pulse width, duty ratio and degree of symmetry are generated; advantages of linear interpolation and nonlinear interpolation are integrated, and edge jitter of pulse signals is effectively reduced.

The invention provides a filling pipeline leakage monitoring and positioning method, device and equipment and a storage medium, and relates to the technical field of pipeline monitoring. The method comprises the following steps: acquiring time-varying data of pressure in a pipeline through a pressure sensing unit, and identifying leakage triggering characteristics; transmitting a pressure pulse wave at the starting end of the pipeline and receiving an echo signal, and calculating a pressure wave propagation speed under the current working condition according to the echo signal; and the arrival time of the pressure waves arriving at the sensors is extracted, and based on the pressure wave propagation speed and the pressure wave arrival time difference, leakage point coordinates are obtained through calculation. According to the method, the leakage characteristics are rapidly identified through real-time pressure collection, the real-time wave velocity under the current working condition is obtained through the active pulse echo technology, and the wave velocity calculation deviation caused by medium characteristic changes is effectively eliminated; and the actually measured wave velocity and the time difference of arrival are combined for calculation, so that error accumulation in long-distance transportation is overcome, and the precision and reliability of pipeline leakage positioning are remarkably improved.

The present invention relates to the field of intelligent medical technology, and in particular to an arrhythmia diagnosis system based on multi-sensor fusion, the system comprising: an electrocardiogram (ECG) signal module, comprising an ECG signal sensor for collecting ECG signals; a pulse wave signal module, comprising a pulse wave sensor for collecting pulse wave signals; a three-axis acceleration module, comprising an acceleration sensor for collecting acceleration signals; a main control chip module, serving as a data processing module, for receiving and analyzing data to obtain an arrhythmia diagnosis result; a Bluetooth module, serving as a communication module, for transmitting the ECG signal, the pulse wave signal, the acceleration signal, and the arrhythmia diagnosis result to a mobile terminal; and a power management module, comprising a charging, power supply, and switching circuit for providing power supply for the system. The system provided by the present invention is stable, portable, and has low power consumption, and is of great significance for the prevention of cardiovascular diseases and ECG monitoring via the Internet of Things.

The invention discloses a multi-sensor monitoring system and method based on time alignment, and belongs to the technical field of multi-sensor monitoring, and the system comprises a sensor collection module which is used for obtaining an electrocardiosignal, a photoelectric volume pulse wave signal, a heart sound signal and a respiratory impedance signal containing a main clock receiving timestamp; the time alignment module is used for performing acquisition time alignment on the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal containing the main clock receiving timestamp to form the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal which are in time alignment; the multi-modal fusion analysis module is used for extracting local morphological features in the electrocardiosignal, the pulse oximeter signal, the heart sound signal and the respiratory impedance signal after time alignment to form a feature map, and extracting dynamic weights between different signals and between different time points / features in the same signal; according to the invention, the interpretability and accuracy of judgment are improved.

The present application relates to the technical field of medical monitoring, and discloses a portable blood oxygen signal OSA intelligent detection system, which comprises the following steps: collecting a pulse wave signal through a blood oxygen sensor, generating an amplitude difference sequence and a period difference sequence in parallel, analyzing the rhythm synchronism of the two, and determining a respiratory effort event when a real-time relationship index deviates from a reference value. The present application captures the pulse wave timing-amplitude decoupling phenomenon, realizes direct sensing of the specific mode process of the invalid respiratory effort signal, avoids the traditional lag monitoring mode which relies on blood oxygen decline, has the inherent ability to distinguish physiological fluctuations from the specific mode struggle of the signal, and provides a new solution for early screening of mild and moderate OSA.

A processing method and a plasma processing apparatus that apply a pulsed wave of radio frequency power while maintaining plasma density are provided. The processing method uses a plasma processing apparatus having a first electrode and a second electrode, and includes a process of applying a pulsed wave of a first radio frequency power to either of the first electrode or the second electrode, and a process of applying a pulsed wave of a second radio frequency power having a frequency lower than the first radio frequency power to the first electrode with a given phase difference from the first radio frequency power, wherein different first and second on times of the second radio frequency power are controlled in such a manner that the on time of the second radio frequency power does not overlap with the on time of the first radio frequency power, and the first on time is controlled to be off immediately before the first radio frequency power is turned on.

An image pickup apparatus capable of correctly determining an exposure permission period from a pulse wave signal. The image pickup apparatus comprising an image pickup portion, and a pulse wave detector configured to detect a pulse wave of a photographer who is holding a main body portion including the image pickup portion. The image pickup apparatus determines, based on a change amount of a pulse wave signal output from the pulse wave detector, a permission period in which exposure by the image pickup portion is permitted and a non-permission period in which the exposure by the image pickup portion is not permitted, and controls the exposure by the image pickup portion, based on the determined permission period and the determined non-permission period.

The invention discloses a silicon controlled rectifier dimming device of an LED driving power supply. The silicon controlled rectifier dimming device comprises a voltage transformation unit, a rectification filtering unit, a first switch unit, a first filtering unit, a first voltage stabilization unit, a phase detection module and a control unit, the pre-stage circuit carries out voltage conversion and outputs steamed bun waves subjected to phase cutting control, reflected voltage of the voltage transformation unit is rectified into pulse waves through the rectification unit, the pulse waves are subjected to voltage division through the voltage distribution unit to drive the second switch unit, the pulse waves are converted into square waves with a duty ratio, and the square waves are converted into square waves with the duty ratio. The control unit outputs a corresponding driving waveform to the first switch unit according to the size of the square wave duty ratio, and the first switch unit is driven to carry out voltage chopping, so that the output current is changed in a range of 1-100% for dimming. The phase detection module is matched with the rectification filtering unit to filter clutters in the voltage signals so as to improve the detection precision, and the first filtering unit is matched with the first switch unit to fill up the energy lost by the waveform, so that the output current is smoother.

The application discloses an STO safety torque off circuit, comprising: the first shutdown circuit and the second shutdown circuit both comprise: the output end of an STO signal input circuit is connected with the input end of a delay shaping circuit, and the output end of the delay shaping circuit is connected with an or gate circuit; the output end of a DSP processor is connected with the input end of a high pulse wave generating circuit, and the high pulse wave generating circuit is connected with the STO signal input circuit; the input end of a diagnosis circuit is connected with the output end of the STO signal input circuit; the output end of the diagnosis circuit is connected with the or gate circuit; the or gate circuit is connected with a servo drive circuit; the output end of the DSP processor is also connected with the input end of the servo drive circuit; the output end of the STO signal input circuit, the output end of the delay shaping circuit and the output end of the diagnosis circuit in the first shutdown circuit and the second shutdown circuit are also connected with the input end of the DSP processor respectively. Through the application, the shutdown reliability and the fault positioning accuracy are improved.

The invention discloses a plateau driving emergency response method based on steering wheel biosensing, and relates to the technical field of intelligent safety vehicle control, and the plateau driving emergency response method comprises the following steps: arranging a dual-wavelength optical sensor and an annular capacitor array on a steering wheel to collect an original PPG pulse wave signal and a holding contact area of a driver, and calculating a final blood oxygen saturation value of the driver; performing altitude self-adaptive blood oxygen threshold value matching according to the real-time altitude during driving, and calculating an early warning threshold value and an emergency threshold value of the final blood oxygen saturation degree; according to the obtained final oxyhemoglobin saturation value, the early warning threshold value and the emergency threshold value, plateau driving emergency response is conducted, and the driving response state is triggered. Full-coverage safety guarantee can be provided under different oxygen deficit degrees, the monitoring precision and early warning pertinence are improved, and the active safety and reliability of plateau driving are remarkably improved.

This invention discloses a method for detecting link faults in a wearable vital signs simulator (WVSS) based on photoplethysmography (PPG), belonging to the field of intelligent fault diagnosis. The invention acquires the WVSS's set parameters and calculates the expected ideal AC / DC ratio based on pre-stored calibration coefficients. Within a continuous diagnostic time window, it acquires a PPG digital sequence, calculates the measured AC / DC ratio, and differs it from the expected value to obtain the parameter domain residual. Through adaptive pulse template reconstruction, it obtains a microstructure residual sequence and calculates its energy in the key frequency band as the microstructure distortion intensity. It calculates the absolute value of the correlation coefficient between the parameter domain residual sequence and the microstructure distortion intensity sequence within the continuous time window as a link integrity diagnostic indicator. After comparison with a preset threshold, it classifies and determines whether the link is intact, degraded, or faulty. This invention relies solely on the WVSS's own output signal, can distinguish between gain drift and waveform structural distortion, and possesses the ability to locate, classify, and interpret faults. It is suitable for production line calibration, on-site maintenance, and built-in self-test scenarios.

An apparatus for estimating bio-information is provided. The apparatus for estimating bio-information includes: a pulse wave sensor configured to measure a pulse wave signal from an object; a force sensor configured to measure force exerted between the object and the pulse wave sensor; and a processor configured to obtain an oscillogram by using the pulse wave signal and the force, to determine a first mean arterial pressure (MAP) based on the obtained oscillogram, to extract additional information in an interval preceding a point of the first MAP of the oscillogram, to obtain a second MAP based on the first MAP and the additional information, and to estimate bio-information based on the obtained second MAP.