522 results about "Motion artifacts" patented technology

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The present disclosure pertains to head-mounted dual-sided photoplethysmography (PPG) systems and methods for blood flow measurement. In one embodiment, a system comprises a frame designed to be worn on a user's head, integrating a first PPG device positioned on the first side of the user's nose and a second PPG device on the opposite side. Each PPG device includes a light source and photodetector, enabling the capture of optical signals indicative of blood flow within the nasal tissue. A computer is operatively connected to both PPG devices, responsible for obtaining first and second signals from each side and processing these signals to calculate comprehensive measurements of the user's blood flow. This dual-sided configuration enhances measurement accuracy and reliability by mitigating motion artifacts and improving signal quality compared to traditional single-sided PPG systems.

The invention relates to a plastic film defect detection device in the field of detection equipment, which is provided with a fixed clamping assembly and a movable clamping assembly to tension intermittently conveyed plastic films section by section instead of a floating roller tensioning mode adopted by a traditional film detection device, so that film vibration and motion artifacts during continuous conveying and shooting of the films are reduced, and the detection efficiency is improved. The shot plastic film has better flatness, the quality of the shot image is improved, and the accuracy of defect detection is further improved; meanwhile, the two ends of the to-be-detected film are clamped and fixed through the fixed clamping assembly and the movable clamping assembly, the probability that a tearing opening is expanded to a non-detection part of the film after the film is tensioned and torn is reduced, and the output of plastic film waste during detection is reduced; in addition, through the cutting assembly and the secondary feeding mechanism, cutting of the unqualified part of the film and secondary feeding of the part to be detected are achieved.

The invention relates to the technical field of road defect detection, in particular to a road pit detection method and system, and the method comprises the following steps: S1, obtaining an original image flow of a road surface through a vehicle-mounted multispectral camera, and carrying out the motion artifact elimination to generate a vibration compensation image; s2, generating a shadow suppression image through asymmetric gamma correction; s3, adopting a dual-threshold connected domain analysis method to extract candidate pothole regions; s4, when the contrast difference value exceeds a texture mutation threshold value, determining that the area is a surface damaged area; s5, extracting a continuous pixel cluster statistical area proportion, and when the proportion is greater than 60%, determining that the feature is an effective pothole feature; and S6, calculating the minimum enclosing ellipse eccentricity rate and the ellipse area ratio, and generating a final pothole detection report. According to the method, through a multi-source information fusion and multi-stage feature extraction method, high-precision identification and standardized output of the pothole area in a complex road environment are realized, and the detection accuracy and the application reliability are improved.

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion can be used for motion-artifact filtering. With motion-artifact filtering, noise caused by motion of a user can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion can expand a functionality of a hearable to include activity detection and / or activity classification. While activity detection and / or activity classification can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable and / or a computing device.

A system and method are provided for creating magnetic resonance (MR) images with reduced motion artifacts from the MR data from which the images are produced. The method includes selecting a candidate image from a plurality of candidate images reconstructed from the MR data. The method also includes registering the candidate image to a reference image, comparing the candidate image to a consistency map, and, based on comparing the candidate image using the consistency map, selecting a blending algorithm. The method also includes generating a blended image using the blending algorithm and the candidate image and repeating these steps for each candidate image. The method also includes performing a Fourier aggregation to generate a combined image and displaying the combined image with reduced motion artifacts compared to the plurality of candidate images.

The invention relates to the technical field of intelligent wearable devices, and discloses a multi-modal positioning and health monitoring combined method and device, and the method comprises the steps: obtaining first multi-modal sensor data of an intelligent wearable device, and carrying out the two-domain signal representation separation, and obtaining a physiological signal representation vector and a motion artifact representation vector; performing comparison loss optimization of motion posture modulation to obtain a first physiological signal representation and a first motion artifact representation; performing feature enhancement to obtain a second physiological signal representation and a second motion artifact representation; performing position-sensitive contrast characterization joint optimization and layered contrast characteristic distillation to obtain a lightweight model, processing data of the second multi-mode sensor through the lightweight model, and outputting a pure physiological signal and a target positioning result. The problem that the performance of a traditional attention mechanism is reduced at a motion conversion point is solved; and the adaptive capacity to a complex motion scene is obviously enhanced.

The invention belongs to the technical field of image processing, and particularly relates to a motion artifact adaptive correction method based on multi-modal MRI fusion, which comprises the following steps: acquiring sMRI and fMRI of a patient, preprocessing the sMRI and fMRI, creating a cross-modal feature fusion model, and inputting the preprocessed sMRI and fMRI into the cross-modal feature fusion model for double-branch feature extraction; the cross-modal feature fusion model carries out cross-modal feature interaction fusion on the extracted double-branch features and carries out multi-scale feature reconstruction; detecting image artifacts based on the reconstructed multi-scale features, and performing dynamic space-time attention correction on the detected image artifacts; and establishing a three-level loss function system to carry out multi-task joint optimization. By integrating sMRI and fMRI information and based on image processing and deep learning, the defect that various complex head movement conditions cannot be completely handled in the prior art is overcome, and the technical problems of information loss between image modalities and insufficient feature utilization are solved.

The invention discloses an electroencephalogram signal artifact removal method and an acquisition system. The method comprises the following steps: synchronously acquiring sensor data of a testee and each lead electroencephalogram signal by utilizing a unified trigger signal; performing timestamp alignment verification on the two types of synchronous data; a motion state quantitative index is determined, and if the motion state quantitative index does not exceed a preset motion threshold value, the collected electroencephalogram signals are output; if yes, an exception processing flow is triggered and comprises the steps that self-adaptive filtering and nonlinear compensation are sequentially executed on the electroencephalogram signals, static electroencephalogram data of the testee serve as reference in the self-adaptive filtering process, an NLMS filter with the step length dynamically adjusted is adopted, a motion artifact linear part and base noise are restrained, a self-adaptive filter is constructed based on the second-order Volterra series in the nonlinear compensation process, and the motion artifact linear part and the base noise are restrained; a kernel coefficient is optimized through an RLS algorithm, and nonlinear distortion is processed; and outputting the electroencephalogram signal after the exception processing. The method is suitable for non-linear and non-stationary motion artifact signals, signal synchronism and quality can be improved, and processing efficiency is considered.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention relates to an imaging type non-contact gating signal obtaining method for a magnetic resonance system, and belongs to the field of physiological signal detection.The method comprises the steps that pulse and respiration signals of a magnetic resonance detection patient are collected through a camera, and face information of the patient is obtained through a reflector in a magnetic resonance instrument cavity; a living body skin detection technology is utilized to efficiently select a region of interest of the face of a patient, and an IPPG technology is combined to process a video image to obtain a curve of image gray along a time sequence; a motion detection algorithm is used for tracking micro motion of the chest, and stable and accurate breathing signals are extracted by combining frequency selection. By analyzing and quantifying pulse wave signals and delay of respiration signals and electrocardiosignals, the purpose of replacing electrocardiosignal R-peak gating triggering with pulse wave and respiration signal feature triggering is achieved. The invention provides an imaging type non-contact gating technology to solve the problems that traditional ECG and other contact gating technologies are easily affected by a strong static magnetic field, an alternating gradient magnetic field and a radio-frequency pulse electromagnetic field in a magnetic resonance imaging system, and motion artifacts can be generated on motion of fluid such as human blood, breathing and cardiac contraction. Therefore, the quality of a magnetic resonance imaging image is improved.

The invention relates to the technical field of biomedical signal processing, and discloses a multi-wavelength PPG signal motion artifact elimination algorithm and system. The method comprises the following steps: acquiring a multi-wavelength PPG signal by using acquisition equipment; inputting the acquired noise-containing multi-wavelength PPG signal into a filter; the filter processes an input signal and then generates a denoised PPG signal; extracting a period of the denoised signal in real time by using an autocorrelation function; inputting the denoised PPG signal and a period extracted in real time into a BSS algorithm to find an optimal noise reference signal; taking the denoised PPG signal and the optimal noise signal as a first input and a second input of adaptive filtering to eliminate motion artifacts; the motion artifacts are eliminated by using the algorithm, and finally the PPG signals with the motion artifacts eliminated are output; the method overcomes the problems of dependence on additional hardware, low correlation of noise reference signals, complex algorithm and the like in the prior art.

The invention discloses a space-time speckle projection three-dimensional imaging method based on multi-frame optical flow alignment. Firstly, a projector based on DLP is used for projecting a space-time speckle pattern to a measured scene, and a binocular camera synchronously collects a three-dimensional space-time speckle image. The calibration parameters of the binocular camera are used to carry out stereo correction on an acquired original speckle image, and a parallax image is generated frame by frame in combination with a coarse-to-fine single-frame speckle matching strategy. And estimating a two-dimensional inter-frame displacement field between continuous disparity maps by using an optical flow method by taking an intermediate frame disparity map as a reference, compensating motion artifacts in a space-time speckle image, and ensuring strict space-time registration of a dynamic target. And based on the speckle image after motion correction, a speckle matching strategy is expanded to a time-space domain, and high-precision multi-frame three-dimensional measurement of a complex dynamic scene is realized. The method is suitable for performing rapid and high-precision three-dimensional modeling on a moving target in an unstructured environment, and can perform accurate three-dimensional measurement on a high-speed dynamic target undergoing any translation or rotation motion.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention discloses a patient state intelligent monitoring system and method, and relates to the technical field of medical data processing. Comprising the steps that physiological data and microenvironment parameters of a patient are collected through a multi-mode sensor, and an initial data set is formed; performing environmental error correction on the physiological data of the patient by using a dynamic compensation algorithm to generate a preliminary correction data set; calculating motion state characteristic parameters of the patient, and judging whether significant motion artifacts exist or not; for the detected artifact interference signal, correcting the physiological data in the preliminary correction data set by adopting a dynamic artifact removing algorithm to obtain a correction data set; and determining the priority of each physiological data, and combining the correction data set to form real-time output. The method can overcome monitoring data distortion caused by environmental noise, motion artifacts and other problems, improves the precision and reliability of a monitoring result, and has dynamic adaptability and convenience.

The invention discloses an electroencephalogram signal processing method and system based on motion artifact prediction. The method comprises the following steps: acquiring experimental electroencephalogram data when a testee executes a motion imagination task; discrete wavelet transform is carried out on experimental electroencephalogram data, and signals are decomposed into low-frequency components and high-frequency components through a low-pass filter and a high-pass filter; inputting the low-frequency component into a pre-constructed and trained ARIMA model, and predicting to obtain a linear artifact; inputting the high-frequency component into a pre-constructed and trained XGBoost regression model, and predicting to obtain a nonlinear artifact; combining the linear artifacts and the nonlinear artifacts to generate a complete artifact prediction signal; real electroencephalogram signals are separated through difference value calculation of the experimental electroencephalogram data and the artifact prediction signals. According to the method, the time sequence change of the motion artifacts is predicted by utilizing the ARIMA model, and the nonlinear artifact features are captured in combination with the XGBoost regression model, so that the motion artifacts can be effectively removed, and purer electroencephalogram signals can be recovered.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention provides a multi-birth fetus brain age prediction method and system, and relates to the technical field of image data processing. Performing image segmentation and motion artifact correction processing according to the original image data to obtain a separated image; reconstructing mutual shielding areas in the separated image to generate a reconstructed image; performing feature extraction processing according to the reconstructed image to obtain brain development feature data of each fetus, and performing difference analysis of relative development levels based on the brain development feature data to generate development difference data; and performing fusion processing according to the brain development characteristic data and the development difference data, evaluating the actual brain age of each fetus, and outputting a brain age prediction result. Through image segmentation, motion correction and signal interference compensation technologies, the problems of image blurring and structure overlapping caused by mutual shielding, fetal movement and signal interference of the multiple-birth fetuses in the maternal body are solved, and the accuracy of predicting the brain age of the multiple-birth fetuses is remarkably improved.

The invention relates to the field of real-time wireless monitoring, in particular to a multi-parameter real-time wireless monitoring integrated device and method, and the device comprises a multi-sensor data collection module, a multi-modal fusion processing module, an anomaly detection analysis module and a multi-stage alarm module. The multi-sensor data acquisition module eliminates motion artifacts by using a generative adversarial network and acquires various physiological signals; the multi-modal fusion processing module realizes space-time alignment and correlation modeling by means of linear interpolation and a graph convolution network to generate a joint feature vector; the anomaly detection and analysis module presets a causal relationship based on a causal graph, optimizes a model through a weight matrix, and judges a root cause through anti-fact reasoning; the multi-level alarm module distinguishes alarm levels according to the risk scoring model and processes the alarm levels; the device and the method solve the problems of low precision, poor collaboration and the like of a traditional monitoring technology, realize multi-parameter accurate monitoring, intelligent analysis and efficient alarm, and can be widely applied to scenes such as remote medical monitoring and the like.

The invention relates to a mechanism and data combined dynamic PET-CT pharmacokinetic fusion modeling method, and belongs to the technical field of pharmacokinetic modeling, and the method comprises the steps: obtaining original image data of a historical case, and constructing a training data set according to the original image data; constructing a dynamic pharmacokinetic model based on a mechanism model and a data-driven network, and completing the training of the dynamic pharmacokinetic model according to the training data set; collecting actual image data of the current subject in a period of time; and inputting the actual image data into a trained dynamic pharmacokinetic model to obtain a prediction result. According to the method, the mechanism model and the data driving network are fused, the complete pharmacokinetic process can be accurately predicted only through image data in a short time in the early stage, the problems that a traditional model depends on long-time scanning and is easily interfered by motion artifacts and the like are solved, the parameter analysis efficiency and accuracy are greatly improved, and the method is suitable for clinical rapid diagnosis.

The invention discloses a self-adaptive adjustment method and system for operating parameters of color ultrasound equipment, and relates to the technical field of medical imagines.The method comprises the steps that in the cardiac ultrasound examination process, image feature state data information is associated with echo signal state data information, and a motion artifact evaluation coefficient Xwy is obtained; after comparison, an artifact image frame set is marked, and a gain adjustment analysis instruction is sent out; the method comprises the following steps: acquiring vascular rigidity state data information according to interference of vascular instantaneous relaxation on echo signals, and associating the vascular rigidity state data information with a corresponding motion artifact evaluation coefficient Xwy to obtain an echo signal gain degree index Zzy; through comparative analysis, whether each frame of echo signal corresponding to the current artifact image frame set needs gain compensation is judged so as to generate and execute a gain adjustment instruction of a corresponding grade, and the process optimizes the gain adjustment of the echo signal, improves the image quality, ensures the definition of ultrasonic imaging, and improves the accuracy of ultrasonic imaging. Therefore, the accuracy and the diagnosis reliability of cardiac ultrasonic examination are improved.

An OCTA image motion artifact and noise parallel removal method based on self-supervised learning comprises the steps of constructing an OCTA image data set, learning a mapping relation between a low-quality image and a high-quality image by using a deep learning method, and further constructing an OCTA image motion artifact and noise parallel removal model. And inputting a low-quality image into the model to realize OCTA image motion artifact removal and noise reduction. According to the method, the OCTA image data set is constructed through an image processing and calculation method, the complexity of data set construction is reduced, and the diversity and representativeness of the data set are enhanced. Model construction is a self-supervision mode, and the processing capacity of the model on complex image features is enhanced by combining a window, a channel attention mechanism and a multi-scale noise reduction module. A composite loss function is constructed by using wavelet transform and a mean square error loss function, and the recognition and removal capability of the model on artifacts and noise is optimized.

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion (202) can be used for motion-artifact filtering (206). With motion-artifact filtering (206), noise caused by motion of a user (106) can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user (106) engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion (202) can expand a functionality of a hearable (102) to include activity detection (208) and / or activity classification (210). While activity detection (208) and / or activity classification (210) can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable (102) and / or a computing device.

The invention discloses a strip steel outlet detection method and related equipment, and relates to the technical field of industrial automatic detection, and the method comprises the steps: obtaining image data of a target detection point in a strip steel outlet area; target detection is conducted on the image data based on a strip steel state detection model, the abnormal state of the strip steel is determined, and the abnormal state comprises the steel stacking state, the steel clamping state or the strip steel arching state; performing motion artifact compensation verification on the abnormal state based on the roller way vibration amplitude data to obtain a verification result; and generating a control instruction based on the verification result and the abnormal state to adjust the operation action of the production line. According to the method, manual monitoring is replaced by automatic detection, the omission ratio and the misjudgment rate are reduced, response and interlocking control of the abnormal state are achieved, non-planned shutdown of a production line is effectively avoided, and continuity and high efficiency of the production process are guaranteed.

The invention discloses a motion artifact elimination method and system based on PPG signals, and relates to the technical field of electronic digital data processing, in particular to a high-accuracy and high-robustness artifact elimination method oriented to the PPG signals in wearable equipment. Firstly, artifact detection is carried out through a multi-base learner fusion model based on meta-learning, and efficient identification of different individual motion artifacts is realized; afterwards, for the detected artifact signals, the system introduces an AVAE model to carry out artifact removal, through multi-loss function joint optimization, high-quality PPG signals with time sequence details reserved are recovered, an effective solution is provided for high-quality perception and robustness health monitoring of the PPG signals in the wearable device in a complex dynamic environment, and the method has the advantages of being high in accuracy and high in robustness. Good popularization and application prospects are realized.

Disclosed is a super-resolution flow field reconstruction method, including: superimposing a plurality of frames of contrast-enhanced ultrasound images that are in one time interval and each of which includes images of a plurality of microbubbles, to obtain a superimposed image including a plurality of microbubble trajectories of the plurality of microbubbles; performing straight line fitting on the plurality of microbubble trajectories, to obtain a plurality of microbubble trajectory straight lines of the plurality of microbubbles, respectively; determining directions and velocities of instantaneous movements of the plurality of microbubbles in the time interval; and reconstructing a super-resolution flow field. This method avoids localization and tracking process of the plurality of moving microbubbles, overcomes the limitations of current ultrasound super-resolution imaging strategies under the impact of motion artifacts and low signal-to-noise-ratio, and improving precision and efficiency of super-resolution flow field reconstruction.

The invention discloses a patch position correction method and system, relates to the technical field of biomedical engineering and digital health, and adopts a hardware-level time synchronization and motion-artifact coupling modeling technology to effectively solve the problems of time delay drift and motion interference of signal acquisition in a dynamic environment and remarkably improve the basic quality of electrocardiosignals. Secondly, by establishing a body surface reference coordinate system and a pose compensation parameter generation algorithm, the micro-displacement state of the patch can be accurately recognized, and visual position adjustment guidance is generated, so that the patch is always kept at the optimal measurement position, and the phenomenon of signal attenuation caused by poor contact is fundamentally reduced; a signal quality multi-dimensional evaluation system adapts to quality discrimination requirements in different motion states through an intelligent weighted fusion mechanism, and combines an adaptive filtering and deep learning enhancement technology, so that the morphological integrity of a QRS waveform is kept, various interference components are effectively inhibited, and the system can still keep excellent electrocardiosignals in a strenuous motion state.