672results about "Physiological signal biometric patterns" patented technology

A system for generating highly personalized image content with generative artificial intelligence (AI) that incorporates brainwave data collected from a Brain-Computer Interface (BCI) device worn by the user while they are immersed in their experiences. The system can identify a mood (i.e., cognitive, mental, and emotional states) felt by the user during that time. The system develops a prompt that describes the target scene and modulates the weights assigned to each parameter in the prompt based on the mood identified. The generative AI engine can then to apply the mood to the target scene by adjusting image attributes such as hue, saturation, and lighting. The outputted image can offer a visual representation not only of the objects that were in the user's environment, but also the user's general perception and experience of the occasion. The process is passive and requires no interaction from the user to define their emotional affect.

The invention relates to an emotion recognition method based on a millimeter-wave radar and a robot. The method comprises the following steps: receiving a transmitting signal of the millimeter-wave radar and a corresponding echo signal, converting the transmitting signal and the corresponding echo signal into a point cloud format, and removing static reflection to obtain dynamic point cloud data and a timestamp of a surrounding environment; grouping the dynamic point cloud data into clusters; obtaining a centroid estimation value of the target, and tracking the centroid estimation value; sending the millimeter wave signal which horizontally moves at a constant speed to a target person, and receiving echo signals which are reflected by the target person and are uniformly spaced; point spread function optimization is carried out, and SAR image reconstruction is carried out by using a fast frequency domain near-field imaging method; calculating to obtain phase change of the human body signal, and separating to obtain respiration and heartbeat signals of the target person; and extracting emotion related features to obtain the current emotion of the target person. Compared with the prior art, the specific position of the human body can be accurately recognized, and non-contact privacy protection emotion recognition can be remotely achieved.

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 system (1000) and method are based on the generation of at least one individual biometric identifier – IBI - from at least user representative synthetic data {Ns} (S70), said user representative synthetic data being generated from neural data {N} of said user (10) received by a pre- trained generative model. Said at least one IBI enables to authenticate said user (10) when compared to at least one user's signature stored in a memory (40). Said at least one IBI can replace both identifier and password to authenticate and login said user (10) without containing any personal biometric data, guaranteeing the anonymity of the login data that can be exchanged remotely.

The invention discloses a driving scene human factor safety monitoring experiment platform and method, and belongs to the technical field of safe driving, the driving scene human factor safety monitoring experiment platform is composed of a plurality of acquisition devices, an experiment machine, a server and a display, the plurality of acquisition devices are used for acquiring monitoring data in an experiment process of a user, and the monitoring data correspond to current simulation driving scene information; according to the method, the current behavior mode and the physiological signal of the driver are comprehensively monitored and correspond to the current simulated complex driving scene information, and the problems that the human factor safety monitoring is poor in universality, and the human factor safety monitoring experience is poor are solved. And the physiological signal of the driver cannot be comprehensively monitored, so that the human factor safety monitoring experiment result is more accurate and reliable.

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.

Embodiments of the present invention provide a physiological state detection method and apparatus, an electronic device, a storage medium, and a program. The method comprises: acquiring a video stream collected by a photographing device; extracting multiple frames of face images of a target object from multiple frames of images in the video stream; determining at least one smooth area in the face images; performing weighting processing or area screening on the at least one smooth area on the basis of area attribute information, so as to obtain information to be extracted that is related to a physiological state; and performing physiological state information extraction on the information to be extracted, so as to obtain a physiological state detection result of the target object. According to the embodiments of the present invention, the physiological state detection is implemented on the basis of an imaging processing mode, real-time measurement can be performed anytime and anywhere, the practicability is better, and the accuracy of measurement can be improved by means of the constraint of preset conditions.

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.

A sound masking apparatus and method are provided. The sound masking apparatus includes a sensing device that measures image information and biometric information of a passenger in a vehicle. The apparatus also includes a controller connected with the sensing device. The controller determines a sleep state of the passenger based on the biometric information and determines whether the passenger is disturbed in sleep based on the image information in the sleep state of the passenger. The controller measures noise in the vehicle in response to determining that the passenger is disturbed in sleep and determines whether there is a need for sound masking based on the measured noise. The controller performs the sound masking based on the sleep state in response to determining that there is the need for the sound masking.

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.

The invention discloses a pulse detection method, system and device based on micro motion amplification parameter optimization. The method comprises the following steps: acquiring an image pulse signal through a binocular vision pulse image acquisition system; a phase motion amplification algorithm in a micro motion amplification technology is applied to amplify the out-of-plane displacement of the contact film and enhance the signal intensity of micro vibration on the contact film; space coordinate calculation and V4 algorithm interpolation curved surface fitting are carried out through the feature points to reconstruct a three-dimensional pulse signal; pulse condition recognition and classification are carried out on the reconstructed pulse signals; through the phase amplification technology and the three-dimensional pulse signal reconstruction method, the out-of-plane displacement of the contact film is enhanced in the three-dimensional pulse signal detection process, the specified frequency of the pulse signal is accurately amplified, and the three-dimensional pulse signal is accurately reconstructed.

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.

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 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 discloses a physiological signal processing method and device and a medium, and is applied to the technical field of physiological signal processing. Processing the surface length sequence video data to obtain spatial-temporal characteristics of the surface length sequence video data; according to the spatial-temporal features, initial fast features and initial slow features are obtained, and the initial fast features and the initial slow features are features representing different time dimensions; according to the initial fast features, a time sequence difference module is combined to obtain deep fast features, and the time sequence difference module is used for carrying out feature extraction based on a preset state space model; according to the initial fast feature and the initial slow feature, combining a time sequence difference module to obtain a deep slow feature; and obtaining a physiological signal according to the deep-layer fast feature and the deep-layer slow feature. According to the application, the accuracy and reliability of the physiological signal can be effectively improved.

The invention relates to a method for detecting a living being, characterised in that it comprises: - acquiring a plurality of images within a time window, each image comprising a series of data with which a position, a power and a speed are associated; - constructing a first database of instantaneous data from the plurality of images, the instantaneous data being based on the position, the power and the speed of each data item of each image acquired in the time window; - constructing a second database of time data from the plurality of images, the time data being calculated from a periodic signal of each image recorded over a moving time window; - applying the first database and the second database as input to a machine learning model; - executing the machine learning model; - establishing a classification by the machine learning model as regards the presence / absence of a living being; and - depending on the classification, transmitting corresponding information to a management unit.

The invention provides a detection method and device for assisting in determining blood pressure and cardiovascular health levels. The detection method and device can be applied to the technical field of coherent optical imaging detection. The method comprises the following steps: continuously collecting diffused coherent light emitted from a tissue at a preset position within a preset duration to obtain a plurality of diffused coherent images; determining a dynamic coherent optical signal corresponding to each pixel according to the plurality of diffusion coherent images, and determining a tissue static signal and a tissue dynamic signal according to the dynamic coherent optical signal and the reference coherent optical signal; calculating information entropy of the tissue dynamic signal to obtain a flow velocity pulse signal; calculating the average intensity of the static signals to obtain volume pulse signals; respectively performing Fourier transform on the flow rate pulse signal and the volume pulse signal to obtain a flow rate pulse frequency spectrum and a volume pulse frequency spectrum; performing cross-correlation operation on the flow rate pulse spectrum and the volume pulse spectrum to obtain multi-dimensional fusion features, and determining the blood pressure and cardiovascular health level according to the multi-dimensional fusion features.

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 discloses a non-contact type user heart rate monitoring method and device, electronic equipment and a storage medium, which are used for solving the problem of poor heart rate monitoring accuracy existing in a related non-contact type heart rate monitoring technology. The method comprises the following steps: acquiring a plurality of face images of a user; determining a target region of interest in the plurality of face images based on a preset target prompt word and a pre-trained multi-modal language model; wherein the target cue word is used for enabling the pre-trained multi-modal language model to screen an area in the face image, so as to output an area of interest in the face image; and determining heart rate data of the user based on the target region of interest in the plurality of face images.