359results about "Physiological signal biometric patterns" patented technology

The invention belongs to the technical field of physiological parameter measurement and testing, and discloses a multi-mode optical non-contact health monitoring system and method. The system comprises a sensing measurement module, a data synchronous acquisition module, a micro-motion stabilization and correction module, a three-dimensional trajectory reconstruction module, a multi-modal data processing module and a physiological index calculation module. Optical imaging data are processed preferentially through a U-Net network and then fused with video micro-motion features, and the problem of multi-modal data coupling interference in a traditional method is solved. A mathematical mapping relation between blood flow parameters and physiological indexes is established through a diffusion optical spectrum mapping network, the segmented blood flow parameters are mapped to initial physiological indexes, and the time lag problem of single-modal data is solved through time sequence features extracted through a time sequence feature fusion network. And the weights of the time sequence characteristics and the preliminary physiological indexes are dynamically adjusted through a space-time attention mechanism, so that the robustness in a complex environment is enhanced, and the prediction precision and stability of a non-contact health monitoring system are improved.

A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

The invention discloses a BMI-driven 3D organ obesity visualization system based on multi-modal fusion, and relates to the technical field of medical science, the system comprises a BMI-driven prediction model, the BMI-driven prediction model is used for establishing a quantitative mapping model of a BMI index and three-dimensional organ fat deposition, and the quantitative mapping model is used for establishing a BMI index and three-dimensional organ fat deposition. The BMI driving type prediction model comprises a multi-modal data fusion unit, a model mapping unit and a residual compensation unit. By installing the BMI driving type prediction model, precise nonlinear mapping of the BMI index and organ fat deposition is achieved, the problem that traditional single-mode prediction is large in deviation is solved, and organ fat volume prediction precision and individual difference adaptability are improved.

The invention relates to the field of fatigue driving behavior analysis based on image recognition, in particular to a fatigue driving behavior feature extraction and analysis method based on image recognition, which comprises the following steps of: acquiring an initial state set of a driver in real time through an IMU (Inertial Measurement Unit), an RGB (Red, Green and Blue) camera and an MEMS (Micro Electro Mechanical System) vibration sensor, converting the initial state set into four images such as a head attitude angular velocity oscillogram, extracting image features and inputting the image features into corresponding preset models to obtain fused feature data, dynamically adjusting weights through scene context features, calculating cognitive load indexes and dividing processing modes; according to the method, multi-modal data fusion and dynamic weight adjustment are realized, the cognitive load of the driver can be accurately evaluated and graded intervention can be performed, and the driving safety is improved.

The invention aims to provide an acne precise treatment and drug delivery system based on multi-modal data and feedback control. The system comprises an image acquisition and identification module, which carries out acne focus identification on an obtained skin image based on a deep learning algorithm, and extracts focus feature data; the sensor acquisition module is used for acquiring skin physiological parameters and environmental parameters related to acnes; the multi-modal fusion module carries out fusion processing on the focus characteristic data, the skin physiological parameters and the environmental parameters to generate current acne state parameters; the digital twinborn prediction module constructs a digital twinborn model of the patient according to the current acne state parameters and predicts the development trend of acnes; the individualized treatment decision module formulates an individualized treatment scheme and a corresponding administration dosage according to a prediction result of the digital twinning prediction module; and a set of system capable of realizing multi-source data acquisition, automatic evaluation, prediction analysis and personalized treatment scheme recommendation of the acne of the patient is constructed.

Provided is a blood flow authentication ring that can achieve focus in a captured image regardless of finger size, can prevent reduction of contrast due to external light, or simplifies personal registration. A blood flow authentication ring in an embodiment of the present invention is a blood flow authentication ring having a ring-shaped cover member to be worn on a finger and configured to perform authentication with finger blood flow. The cover member at least includes an irradiation device to irradiate the finger with light and an imaging device to capture an image of light transmitted through the finger. An adjusting member is provided between an inner circumference of the cover member and the finger. The adjusting member has a function of at least one of a size compensation member and a low reflection member.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for medical condition identification. One of the methods includes obtaining visual data representing at least one body part of a subject; obtaining non-visual data that corresponds to one or more biological characteristics of the subject; providing data representing (i) the visual data and (ii) the non-visual data to one or more machine learning models, wherein the one or more machine learning models are trained to predict presence of a medical condition; obtaining an output of the one or more machine learning models that is generated based on the one or more machine learning models processing the visual data and the non-visual data; and determining presence of the medical condition for the subject using the output of the one or more machine learning models.

The invention discloses an intelligent driving school mode virtual driving scene generation method based on an AI model, relates to the technical field of virtual driving scene generation, and is used for solving the problems of feedback lag and poor individualized adaptability of a training scheme caused by difficulty in dynamically adapting to the current stress level and operation habit of a driver. The method comprises the steps of monitoring the instantaneous speed and direction control amplitude of a simulated vehicle in real time, calculating a longitudinal G value and a steering change rate, collecting physiological data of a tested driver to analyze heart rate variability and cognitive load degree, generating driving behavior characteristics and stress characteristics by using an LSTM model, and judging whether to trigger a specific scene or not through a support vector machine based on the characteristics. The method comprises the following steps of: acquiring response time and misoperation frequency, generating a risk response score through fusion function joint quantification, and judging a driving level grade according to a score threshold value, thereby realizing screening and marking of tested drivers, dynamically adapting to actual performance, and improving pertinence and effectiveness of virtual training.

The invention belongs to the technical field of face data dynamic verification, and particularly relates to a centralized dynamic verification and risk defense method based on face data. According to the method, multi-dimensional dynamic indexes such as micro expressions, iris responses and blood flow fluctuations are fused, the detection capacity of attack means such as deep counterfeiting, face masks and picture screenshots is greatly improved, the false recognition rate and the missed recognition rate are reduced, external risks such as equipment integrity, network security and geographical position deviation are fused into comprehensive evaluation, and the method is high in practicability. The face self is concerned, the whole verification environment can be safely controlled, more comprehensive defense is realized, once a threshold alarm is triggered, multi-level defense measures can be immediately started, the defense result is fed back to the model, the discrimination boundary of the generative adversarial network is continuously optimized, and high efficiency and robustness in long-term operation are ensured.

The invention relates to the technical field of living body fingerprint detection, and particularly discloses a living body fingerprint detection method based on multispectral and micro pulse feature fusion, which comprises the steps of signal synchronous generation, time domain alignment processing, feature matrix generation, weight dynamic adjustment, cross validation analysis, abnormal region sampling, living body model verification and living body authentication output. According to the method, the verification result is generated by synchronously collecting the multi-spectral reflection intensity and the skin displacement fluctuation sequence of the fingerprint area, dynamically adjusting the spectral absorption and pulsation track feature weight, performing cross validation to output the local abnormal area and obtain the supplementary feature data, and performing physiological coupling rule matching by using the preset living body judgment model. When three consecutive results do not conform to a living body rule, activating an anti-counterfeiting attack alarm protocol; according to the method, multispectral and micro pulse features are fused, and an environment adaptive weight mechanism is combined, so that the accuracy of living body detection is effectively improved, and the environment adaptability of a detection algorithm is enhanced.

The invention relates to the technical field of skin lesion area reconstruction, in particular to a skin lesion area reconstruction system based on multi-modal image fusion. The system comprises a deep multi-scale feature extraction module, a skin thermal anomaly feature extraction module, a hemodynamic feature extraction module, a depth and boundary feature extraction module, a skin surface feature extraction module, a fluorescence intensity distribution and morphological feature extraction module, a first feature fusion module, a second feature fusion module and a lesion area reconstruction module. According to the method, appearance, heat, blood flow, structure and surface features are extracted from the multi-modal data, then the multi-modal image data are integrated, finally, comprehensive representation and reconstruction of skin lesion appearance, function and structure information are achieved according to the synergistic effect of the multi-modal information, and the accuracy and clinical reference value of lesion area reconstruction are effectively improved.

The invention provides a non-contact heart rate detection method based on mixed multi-level feature fusion, which takes a face video as input and a heart rate value as output to realize direct mapping from the face video to the heart rate. Comprising five steps of data preprocessing and enhancement, multi-scale attention fusion module processing, cascade multi-layer feature fusion processing, model-independent meta-learning (MAML) training strategy, and mapping of multi-dimensional fusion features into one-dimensional BVP signals by using a signal estimator. And the generalization ability, the detail feature capturing ability and the dependency on the marked data of the model are effectively improved.

The present invention relates to a real-time target area tracking and dynamic regulation method and system thereof. The method includes obtaining a phase prediction model based on 4DCT reconstruction data and three-dimensional point cloud body surface contour data; obtaining a two-stage model according to the result output by the phase prediction model and the 4DCT reconstruction data; superimposing the phase prediction model and the two-stage model, and fusing to obtain a target area prediction model; acquiring real-time body surface contour data and respiratory signals; inputting the body surface contour data and the respiratory signals into the target area prediction model, and the target area prediction model predicts the current position of the in-vivo target area; calculating the target pose of the accelerator treatment couch according to the current position of the in-vivo target area and causing the accelerator treatment couch to be converted to the target pose. Through the target area prediction model, real-time input data can be used to obtain the real-time position of the in-vivo target area, which can respond to the dynamic changes of the target area in real time and achieve dynamic regulation. The target area prediction model is modeled based on 4DCT reconstruction data, and the prediction result has high accuracy.

The invention discloses a safety condition monitoring method and device for old people, and relates to the technical field of heart rate monitoring. Acquiring a face video and an infrared video of a target user to obtain a first final space-time diagram and a second final space-time diagram; respectively inputting the first final space-time diagram and the second final space-time diagram into a space-time feature extraction module for feature extraction to obtain RGB features and NIR features, and fusing the RGB features and the NIR features to obtain fused features; the heart rate of the target user is determined according to the fusion features, and state monitoring is conducted. Acquiring a face and an infrared video of a target user, and extracting spatio-temporal features of the face video according to a preset rule to obtain a first spatio-temporal diagram; the infrared video features are extracted and amplified frame by frame to obtain a second space-time diagram, RGB and NIR features are extracted through a space-time feature extraction module and fused into fused features, non-contact heart rate monitoring is achieved, the accuracy and efficiency of heart rate monitoring are improved, the heart rate is determined according to the fused features, and the health condition of a user is monitored and evaluated in real time.

A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

The invention discloses a non-contact heart rate estimation method and system based on multi-modal signal fusion, and relates to the technical field of image processing, and the method comprises the steps: positioning a region of interest in a face video, extracting HSV and Lab color space signals, and fusing the HSV and Lab color space signals; suppressing ambient light interference through background brightness compensation to obtain a color conversion signal and normalizing the color conversion signal; adjusting the bandwidth filtering signal of a band-pass filter by using the historical heart rate to generate an rPPG signal; performing time domain and frequency domain analysis on the rPPG signal, and performing weighted averaging by combining two results to obtain a heart rate estimation value; a historical data smoothing strategy is adopted to correct a heart rate estimation result to ensure accuracy. According to the method, high-precision non-contact heart rate estimation is realized through extraction and fusion of HSV and Lab color space signals, background brightness compensation, signal normalization and processing of human face video signals based on a historical heart rate adjustment band-pass filter.

A system comprises a video capture module to acquire a video, a preprocessing module to enhance video quality and isolate regions of interest within the video, and a biometric data extraction module using remote photoplethysmography to extract heartbeat and SpO2 levels from the video. A machine learning module analyzes the extracted biometric data for liveness and deepfake detection, a verification module to compare the analyzed data against known biometric signatures, and a user interface to display analysis results and alerts.

An active chatbot system with behavioral awareness and on-demand conversation and a method thereof are disclosed. In the active chatbot system, a client-end host continuously senses a client behavior state, and transmits the sensed client behavior state and an on-demand conversation setting to a server-end host to generate a rough question message having a natural language structure, and input the rough question message to a plurality of finite state machines to generate a precise question message. The server-end host transmits the precise question message to an artificial intelligence platform to obtain a corresponding answer message, stores the answer message to an answer list, filters out an answer message matching the on-demand conversation setting as an on-demand conversation message, transmits the on-demand conversation message to the client-end host for output. Therefore, the technical effect of improving human-computer interaction and initiative of chatbot can be achieved.

The invention relates to the technical field of operator safety management, and discloses a non-contact physiological status rapid screening method and system and a medium, and the method comprises the steps: collecting an RGB three-channel space-time image sequence of a facial region of interest; carrying out illumination correction on the RGB three-channel space-time image sequence by adopting an adaptive gamma correction algorithm, carrying out end-to-end noise reduction processing on the illumination correction image sequence by utilizing a one-dimensional convolutional neural network auto-encoder noise reduction algorithm, and separating a remote photoelectric volume pulse wave tracing pulse signal; calculating physiological indexes of heart rate, heart rate variability and oxyhemoglobin saturation based on the remote photoplethysmography pulse signal; and performing grading judgment on the physiological indexes based on the physiological index grading standard, determining a final post suitability judgment grade in combination with the wooden barrel effect model, and generating a grading early warning signal. According to the method, accurate and rapid evaluation of the physiological state of the operator in a complex environment can be realized, the individual health degree can be comprehensively and objectively considered, and decision support is provided for reasonable distribution of posts and efficient configuration of personnel.

The invention relates to the technical field of automatic identification of animal behaviors, in particular to an automatic identification method for pulmonary respiration behaviors of giant salamanders, and the method comprises the steps: obtaining an initial video image of a target region; preprocessing the initial video image to obtain a standard video image; performing feature enhancement on the standard video image to obtain an enhanced feature image; performing multi-scale respiratory behavior feature extraction on the enhanced feature image to obtain depth feature maps of different scales; performing multi-level feature aggregation on the depth feature maps of different scales, and generating an aggregated feature map; and generating an identification result based on the aggregated feature map. According to the method, the problems that the omission ratio of manual monitoring of the breathing behavior in a night low-contrast scene is high and long-term efficient and objective monitoring is difficult to realize due to night walking and cave habits of the giant salamander are solved.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.

The invention discloses a heart rate detection method implemented by using a composite remote photoplethysmography (rPPG), comprising the following steps: continuously capturing an input frame, the input frame having a face image; calculating a plurality of feature points in the face image to obtain a skin color mean value and storing the skin color mean value in a first queue; executing a POS algorithm and a CHROM algorithm based on the skin color mean value of the plurality of input frames in the first queue to generate a first rPPG waveform signal and a second rPPG waveform signal respectively; performing reverse merging processing on the first rPPG waveform signal and the second rPPG waveform signal to generate a merged waveform signal; performing a fast Fourier transform process on the merged waveform signal to generate a merged spectrum; and performing spectrum analysis processing on the combined spectrum to extract heart rate output.

The invention provides a registration method and device in a cardiac surgery and computer equipment, which are used for improving the safety and accuracy of the surgery. The registration method comprises the following steps: acquiring an electrocardiosignal and multi-modal physiological data of a surgical patient, and performing cardiac time phase division on a cardiac cycle based on the electrocardiosignal and the multi-modal physiological data; collecting multi-angle image data of the heart under each cardiac time phase, and synchronously collecting physiological parameter data; performing three-dimensional reconstruction based on the multi-angle image data corresponding to each cardiac time, and mapping the physiological parameter data corresponding to each cardiac time to a corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the method comprises the following steps: acquiring real-time electrocardiosignals and real-time multi-modal physiological data of a surgical patient in a surgical process to identify a current cardiac time phase, selecting a current time phase three-dimensional heart model from a multi-time phase three-dimensional heart model library according to the current cardiac time phase, and performing surgical registration by using the current time phase three-dimensional heart model.

The invention relates to an electronic device which comprises a substrate, a pixel, a second sensing unit, a first driving unit and a second driving unit. Each pixel comprises a display unit and a first sensing unit, and the display unit comprises a self-luminous element. The second sensing unit overlaps at least one of the pixels. In the first mode, the first driving unit outputs a first signal to the display unit. In the second mode, the first driving unit outputs a second signal to the second sensing unit, receives a third signal from the second sensing unit, and outputs a fourth signal to the second driving unit according to the third signal. In the third mode, the second driving unit outputs a fifth signal to the first sensing unit according to the fourth signal, and the first sensing unit returns a sixth signal to the first driving unit.

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.

In embodiments, a method for configuring an intelligent agent to do a task based on spatial-temporal magnetic imaging data of the brain of a worker is disclosed. The method includes generating a brain region parameter indicating an active neocortex region associated with visual processing during performance of the task based on the spatial-temporal magnetic imaging data. The method further includes selecting a convolutional neural network (CNN) component type in response to a match between the brain region parameter and an associated CNN component type. The method includes configuring the intelligent agent based on the selected CNN component and a neocortical processing flow parameter derived using the spatial-temporal magnetic imaging data, wherein the intelligent agent is configured to process image data using the CNN component and provide an output of the CNN to another AI component via a data connection created based on the neocortical processing flow parameter.

The invention relates to the technical field of non-contact physiological signals, and discloses a remote physiological signal detection method based on an adaptive filter, a terminal and a medium. The method comprises the following steps: obtaining a to-be-detected face video, and obtaining a noise rPPG signal through noise processing; performing feature extraction on the face video to obtain a multi-scale space-time diagram; fusing the noise rPPG signal and the multi-scale space-time diagram to obtain fused data; performing denoising processing on the fused data by using a denoising module to obtain denoised data; the de-noising module adopts an adaptive filter de-noising module, the interior of the de-noising module is composed of multiple layers of filter structures, and each layer of filter structure comprises a fixed spectrum shaping filter, a scene adaptive shaping filter and a spectrum period feature extraction filter which are connected in sequence; and outputting a predicted rPPG signal according to the denoised data. According to the method, deep fusion and denoising are carried out on the noise and the multi-scale spatial-temporal characteristic data by using the adaptive filter, and high-precision rPPG signal estimation is realized.

Provided is a cross-period brain fingerprint identification method with paradigm adaptive decoupling and a system thereof. The method includes: extracting a feature representation from original electroencephalogram data by a feature extractor; effectively separating identity-related features and paradigm task-related features from highly coupled electroencephalogram information; and further learning domain invariant features with identity identification ability through domain adversarial training. According to the method, three decouplers are introduced to perform feature decoupling on features extracted by a feature extraction module. At the same time, three classifiers are introduced to pass through a domain label, an identity label and a paradigm task label, and the decouplers are guided to decouple paradigm task features and identity features effectively through adversarial training.

An integrated artificial intelligence camera system provides multi-domain monitoring and enterprise automation from a single hardware endpoint. A camera assembly and auxiliary sensors, including at least one of depth, thermal, audio and environmental sensors, feed an edge compute module executing modular perception, identity, physiological estimation and domain-logic pipelines. The system detects and tracks persons, objects and activities; generates pseudonymous identity tokens for enrolled workers; estimates breathing rate and related wellness indicators from non-contact visual and thermal signals; and transforms these outputs into normalized event records annotated with security, inventory, workforce, payroll, physiological and compliance labels. A unified event model standardizes timestamps, device identifiers, site and zone identifiers, actor identifiers, metrics, confidence scores, policy tags and evidence references, enabling direct integration with payroll, inventory management, security incident and analytics platforms. A policy engine governs feature activation, anonymization and retention per jurisdiction and user role.

The invention relates to the technical field of image processing, and discloses a facial health state assessment method and system based on layered optical modeling. The objective of the invention is to solve the problems of poor face health state evaluation precision and robustness caused by rough optical modeling, mixed reflection components, weak environmental adaptability and insufficient multi-dimensional health index fusion capability in the prior art. The method comprises the following steps: synchronously acquiring a user face video sequence comprising at least two different spectral bands through a multispectral imaging device; constructing a multilayer optical transmission model of the skin tissue, and separating a specular reflection component and a diffuse reflection component from the face video sequence; extracting a physiological time sequence signal related to subcutaneous blood flow activity from the separated diffuse reflection component, and calculating heart rate, heart rate variability and blood oxygen saturation parameters; and fusing the physiological parameters and expression and micro-expression features extracted from the facial video sequence, and outputting quantitative evaluation results of fatigue degree, pressure level and emotional state through a pre-trained deep neural network model. The system comprises a multispectral image acquisition module, a layered optical modeling module, a reflection component separation module, a physiological parameter inversion module and a health state evaluation module. According to the technical scheme, the signal-to-noise ratio and stability of physiological signal extraction in a complex illumination environment can be remarkably improved, the adaptability to individuals with different skin colors and dynamic illumination conditions is enhanced, and more accurate and more robust judgment of the high-order health state is achieved.