453results 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.

An embodiment related to a system, comprising: a processor; a sensor; an image sensor; a display; and a communication module; wherein the processor is configured to measure a physiological characteristic of a passenger of a vehicle; capture an image of the passenger of a vehicle; process the physiological characteristic of the passenger and determine whether the physiological characteristic is outside a predefined threshold; process the image, via the processor comprising an image processing algorithm and artificial intelligence, for extracting a feature; detect the health emergency and a severity of the health emergency based on the feature extracted; generate an alert signal; display the alert signal and at least one of a detail of the health emergency of the passenger; and an action based on the health emergency and a severity of the health emergency. In an embodiment, the system is configured to be a component of the vehicle.

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.

The invention provides a fatigue driving monitoring method and system and a storage medium, and the method comprises the steps: obtaining a DMS video stream, carrying out the feature extraction of the DMS video stream, and obtaining a face ROI region; inputting the face ROI region into a regression model to extract skin brightness change characteristics of the driver, and obtaining multiple vital signs of the driver based on the skin brightness change characteristics; the vital signs at least comprise heart rate, respiratory rate, oxyhemoglobin saturation, blood pressure and heart rate variability; the vital signs are judged according to a preset time period and multiple vital sign threshold ranges, and when at least two of the multiple vital signs exceed the corresponding vital sign threshold ranges, it is judged that the driver is in the fatigue driving state. According to the method provided by the invention, multiple vital signs are detected by depending on one independent regression model, and the accuracy and timeliness of fatigue driving detection are improved.

The invention relates to a real-time target region tracking and dynamic regulation and control method and system. The method comprises the steps that a phase prediction model is obtained based on 4DCT reconstruction data and three-dimensional point cloud body surface contour data; obtaining a two-stage model according to a result output by the phase prediction model and 4DCT reconstruction data; superposing the phase prediction model and the two-stage model, and fusing to obtain a target region prediction model; acquiring real-time body surface contour data and breathing signals; inputting the body surface contour data and the breathing signal into a target region prediction model, wherein the target region prediction model predicts the current position of an in-vivo target region; and calculating the target pose of the accelerator treatment bed according to the current position of the in-vivo target region, and converting the accelerator treatment bed to the target pose. Through the target region prediction model, the real-time in-vivo target region position can be obtained by inputting data in real time, the dynamic change of the target region can be responded in real time, and dynamic regulation and control are realized. And the target region prediction model is modeled based on 4DCT reconstruction data, so that the prediction result is high in accuracy.

In a device and a method for obtaining a heart rate, the device for obtaining a heart rate may include: a processor; and a memory device, and obtain a first band image and a second band image in different bands, measure a first remote heart rate signal and a second remote heart rate signal for the first band image and the second band image, respectively, compute a first quality score and a second quality score for the first band image and the second band image, respectively, select a first effective heart rate section based on the first remote heart rate signal and the first quality score, select a second effective heart rate section based on the second remote heart rate signal and the second quality score, and determine a complementary heart rate based on the first effective heart rate section and the second effective heart rate section.

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.

The embodiment of the invention relates to the technical field of pulse diagnosis, and discloses a pulse analysis method based on a pulse diagnosis instrument, which comprises the following steps: processing a pressure change signal within a set time to obtain an original pulse waveform signal associated with a user; performing high-frequency denoising processing on the first pulse frequency signal according to a set noise threshold value; acquiring all wavelet coefficients associated with the high-frequency detail signal, traversing all the wavelet coefficients, determining that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of the wavelet coefficient adjacent to the wavelet coefficient, and taking the wavelet coefficient as a signal abrupt change point; generating a corresponding pulse image of the user based on the pulse condition data of the user within a set time range; and inputting the obtained pulse condition graph of the user into a pre-constructed standard pulse condition model to determine a pulse condition type result of the user, and outputting the pulse condition type result of the user. According to the scheme provided by the embodiment of the invention, the user can conveniently, quickly and accurately know the pulse condition and the physique state of the user, and the use convenience is improved.

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 discloses an information processing method, device and system, and belongs to the field of computers. The method is applied to a brain-computer system, the brain-computer system comprises a control module, and the method comprises the steps that the control module receives a first signal sent by a signal acquisition module, and the first signal is obtained by converting a first neural signal of a brain by the signal acquisition module based on a first configuration parameter; and when the state of the first signal is abnormal, the control module sends first information, and the first information indicates the signal acquisition module to adjust the first configuration parameter to a second configuration parameter. It is ensured that the feedback module can be controlled.

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 current document is directed to computationally efficient, robust, and accurate methods and systems that determine the heartbeat rate of human subjects, patients, and / or participants monitored by a video-recording-capable device or system by analyzing video recordings of human subjects, patients, and / or participants, either in real time or following video recording. The currently disclosed methods and systems employ face-recognition methods to identify adaptive regions of interest corresponding to facial-skin areas. Spatial averages of the intensities of light components with different frequencies reflected from the identified adaptive regions of interest are extracted from the video recording as intensity signals. The intensity signals are then analyzed to extract detrended, band-pass-filtered oscillating light-intensity signals that are processed by spectral and peak-finding methods that each produces a determined heartbeat rate for each of multiple time intervals.

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 provides an emotion recognition method and device. The method comprises the following steps: acquiring face video data and environment data; extracting image data, voice data and text data according to the collected face video data; extracting image features and physiological signal features from the extracted image data; extracting audio features from the extracted voice data; extracting text features from the extracted text data; environment features are extracted from the collected environment data; splicing and fusing the extracted image features, physiological signal features, audio features, text features and environment features by using an attention mechanism to generate multi-modal fusion features; and inputting the generated multi-modal fusion features into a pre-trained emotion detection model for emotion recognition, and outputting emotion contents to which the multi-modal fusion features belong. The emotion recognition method can solve the problem that an existing emotion recognition method only uses a single-mode picture and ignores other mode data.

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.

The invention relates to the technical field of medical imaging, and provides an automatic measurement method, system and equipment for the cardiac muscle full segment thickness of an echocardiogram, and the method comprises the steps: determining a sector rectangular frame in an ultrasonic video stream through employing a deep learning target detection model, so as to determine a cutting region; cutting the ultrasonic video stream based on the cutting area, and scaling the cut ultrasonic cardiac image to a target size in an equal proportion; performing feature extraction on the echocardiography image through a feature extraction module and generating a feature map; cardiac muscle segmentation is completed through the cardiac muscle segmentation module, and mitral valve endpoint recognition is completed through the mitral valve endpoint recognition module; based on the data obtained by the myocardial segmentation module and the mitral valve endpoint identification module, a post-processing module is adopted to calculate and visually output a myocardial full segment thickness result at the end of diastole or end of systole; the cardiac muscle full segment thickness can be effectively measured, the accuracy and repeatability of cardiac function parameter measurement can be improved, and the medical examination process is optimized.

The invention provides a non-contact heart sound monitoring method and system, a terminal and a medium, and particularly relates to the technical field of biomedical engineering.The non-contact heart sound monitoring method comprises the steps that lasers of different wavebands are used for conducting multi-point synchronous monitoring on the heart, and a defocus speckle image is obtained; performing signal extraction on the out-of-focus speckle image to obtain heart movement signals corresponding to multiple wavebands; and filtering the heart movement signals corresponding to the multiple wavebands to obtain heart sound signals. According to the scheme, the characteristic of out-of-focus independent interference imaging of the multi-band laser is utilized, multi-point synchronous monitoring is carried out on micro movement of the heart position in the thoracic cavity through the laser of different bands, the heart sound can be monitored in a non-contact mode, the requirement for measuring environment noise can be lowered, meanwhile, the detection precision is guaranteed, and the detection efficiency is improved. And the comfort of the target living body can be improved.

The invention discloses a non-contact vital sign monitoring method and device, a medium and equipment. The method comprises the following steps: acquiring continuous video images of a monitor based on a multispectral imager; extracting and processing the pulse wave signal; acquiring a heart rate value; acquiring a respiratory rate; acquiring a body temperature value; based on a pain expression recognition monitoring technology, a recognition rate is obtained. The problem that heart rate estimation is affected by motion artifacts is solved, and then accurate heart rate estimation is achieved; according to the invention, the problem of high noise of the existing thermal imaging equipment can be effectively solved; according to the invention, the problem of partial loss of the energy signal value received by the sensor due to attenuation of target radiation to a certain degree is solved, and the problem that a real-time correction method based on a standard source is difficult to adapt to actual measurement due to limitation of a carrying system is effectively solved. The accuracy and details of pain assessment are improved, the workload of medical staff is reduced, the medical efficiency is improved, and resources are saved.

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.

One exemplary implementation provides an improved user experience on a device by using physiological data to initiate a user interaction for the user experience based on an identified interest or intention of a user. For example, a sensor may obtain physiological data (e.g., pupil diameter) of a user during a user experience in which content is displayed on a display. The physiological data varies over time during the user experience and a pattern is detected. The detected pattern is used to identify an interest of the user in the content or an intention of the user regarding the content. The user interaction is then initiated based on the identified interest or the identified intention.

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.