256 results about "Cardiac rate" patented technology

The invention relates to the technical field of fire fighting systems, in particular to an intelligent cockpit system for fire fighting, which comprises a perception analysis layer, a decision processing layer, a command execution layer and a reinforcement learning closed-loop architecture for mixed reward shaping, and integrates video streams, audio communication and firefighter physiological data containing heart rate variability through a deep multi-modal fusion module. Generating a global fire scene situation of physical constraint verification; through a risk sensitive type three-dimensional fire scene deduction module, a fire extinguishing strategy considering efficiency and safety is generated; through an immersive augmented reality visual interface, in combination with a synchronous positioning and mapping technology, precise navigation in a complex environment and superposed display of situation information containing risk levels are realized; a Q learning algorithm continuous optimization strategy including domain knowledge intermediate process rewards is adopted, closed-loop optimization of perception-analysis-decision-execution-feedback is achieved, and therefore the overall efficiency of fire rescue operation in modern complex disaster scenes is remarkably improved.

The invention relates to a millimeter wave radar vital sign modeling method based on time-frequency characteristic decoupling. According to the method, a millimeter wave radar array is arranged, reflection echo signals are collected, and pure initial signal data are obtained; performing time-frequency transformation on the initial signal data based on a multi-scale sliding window to generate a time-frequency energy distribution map; according to the energy concentration degree and stability difference of different frequency components in the time-frequency spectrum, frequency components which are high in energy concentration degree and have continuous, stable and periodic changes on a time axis are extracted and serve as effective signal components corresponding to human respiration and heartbeat characteristics; extracting a corresponding frequency change trajectory according to the effective signal components, establishing a vital sign signal trajectory model based on a time-frequency characteristic change trend, and determining an optimal vital sign trajectory path according to the stability and continuity of the trajectory on a time axis; and establishing a vital sign monitoring model by using the optimal vital sign track path to realize high-precision monitoring of human respiration and heart rate.

The invention belongs to the technical field of intelligent education, and particularly relates to a classroom attention detection method and system based on multi-modal data fusion. Aiming at the problems of high equipment cost, low multi-source data fusion efficiency, insufficient privacy protection and the like in the prior art, the invention provides the following solutions: collecting face, eye movement, posture, voice signals and heart rate variability data of a student through a sensor; multi-modal data synchronization is realized by adopting a time sequence alignment algorithm; respectively extracting a visual attention feature, a voiceprint matching feature and a physiological wake-up feature by using a lightweight deep learning model; constructing a multi-modal data fusion network, and dynamically adjusting a feature weight in combination with a classroom scene; attention anomaly detection is realized by adopting a hybrid model, and real-time early warning is output through edge computing equipment. The method has the beneficial effects that the hardware cost is greatly reduced while the detection precision is ensured, and the privacy of students is effectively protected; a dynamic weight distribution mechanism improves the adaptability of different teaching scenes.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention discloses a Parkinson's disease assessment method and system based on face video multi-modal physiological feature fusion, and relates to multiple technical fields of computer vision, physiological signal processing and the like, and the method comprises the following steps: S1, based on a continuous face video stream, extracting rPPG signals; then heart rate variability key parameters are calculated, and heart rate variability characteristics are obtained; s2, analyzing the dynamic change of an eye fixation point in the face video based on IPAST, extracting key eye movement behavior parameters, and obtaining eye movement behavior characteristics through a convolution gating loop unit; and S3, inputting the heart rate variability characteristics and the eye movement behavior characteristics into a multi-modal fusion network structure, and outputting a continuous risk score or an illness state label for assisting a doctor in early Parkinson risk assessment. According to the method, multiple physiological signals acquired through videos are deeply integrated, a multi-modal feature collaborative analysis framework is constructed, the subjective limitation of traditional scale evaluation is broken through, and the one-sidedness defect of single biomarker detection is overcome.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

The invention relates to the cross technical field of biomedical engineering and psychotherapy, in particular to an automatic control system for classified exposure therapy of phobia based on naked eye 3D. The system comprises an electroencephalogram signal feature extraction module, a heart rate variability feature extraction module, a skin electric response feature extraction module, an eye movement feature extraction module, a physiological load state feature extraction module, an exposure therapy process feature extraction module, a first feature fusion module, a second feature fusion module and a stimulation parameter adaptive control module. The method combines multi-mode biological signal real-time analysis and naked eye 3D stimulation parameter self-adaptive adjustment, and is suitable for clinical psychological treatment mechanisms and psychological health intervention scenes.

The invention relates to the field of traditional Chinese medicine pulse condition collection, and provides a traditional Chinese medicine six-channel identification cognition method and system based on heart rate variability, and the method comprises the steps: obtaining heart rate variability data of a detected object, and extracting parameters such as time frequency from the heart rate variability data; constructing a hierarchical feature extraction network, inputting parameters such as time frequency into the hierarchical feature extraction network, and distributing weights for the parameters such as time frequency based on a qi-blood-body fluid theory to obtain a fusion feature vector; inputting the fused feature vector into a particle swarm optimization algorithm, and performing feature selection by adopting a six-channel transmission constraint function and a syndrome affinity particle update strategy to obtain an optimized feature combination; the optimized feature combination is converted through the semantic mapping relation between the heart rate variability parameters and the pulse condition descriptors, and pulse condition feature parameters are obtained; and performing syndrome classification calculation based on the pulse condition characteristic parameters, and outputting six-channel syndrome types. According to the invention, automatic identification conversion from physiological signals to traditional Chinese medicine syndromes is realized, and the precision and reliability of six-channel syndrome identification are improved.

The invention provides a multi-modal fusion AR glasses intelligent control method and system, and relates to the technical field of artificial intelligence and AR glasses application, and the method comprises the steps: collecting an electroencephalogram signal, an eye movement signal, a heart rate signal and a skin electrical signal of a user, and building a user physiological feature database; the interaction intention of the user is recognized by comparing with the current real-time physiological feature data of the user; an interaction control instruction and content presentation parameters are generated in combination with the sight focus position to adjust spatial distribution of AR glasses display content; according to the visual fatigue degree and the real-time physiological feature data of the user, visual comfort parameters and physiological state parameters are generated; and adjusting optical display parameters of the AR glasses according to a weighting result. By implementing the method, the user can realize natural and stable interaction control of the AR glasses even in a special working environment in which both hands cannot operate and voice interaction is limited, and the practicability of the system in a complex scene is improved.

An apparatus for vital sign extraction. The apparatus may receive a vital signal of a subject from a sensing device. The apparatus may also perform a preprocessing procedure on the vital signal via bandpass filtering and normalization to obtain a preprocessed signal. The apparatus may also perform a time-frequency analysis of the preprocessed signal, and estimate a heart rate of the subject from a dominant component of the preprocessed signal by finding location of a maximum spectral energy of the time-frequency analysis. In addition, the apparatus may identify guard components in the preprocessed signal in view of the dominant component, and derive a respiratory rate of the subject from a length of an interval between the dominant component and each of the guard components.

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

The invention discloses a sleep staging detection method and system based on a smart watch, and the method comprises the steps: S1, obtaining the heart rate variability, triaxial accelerometer data and body movement intensity index signals of a wearer through a sensor of the smart watch, and carrying out the multi-source signal fusion and time synchronization; s2, carrying out noise reduction processing on the synchronized multi-source signal, and constructing a feature vector; s3, calculating a time sequence self-correlation feature of the feature vector; s4, based on a bidirectional long and short time memory network structure, performing time sequence modeling on the input feature vector and the time sequence self-correlation feature to obtain fused bidirectional feature representation; and S5, performing multi-classification processing on the fused bidirectional feature representation based on a Softmax classifier to realize accurate classification of sleep stages. According to the method, the heart rate variability signal and the three-axis acceleration signal can be fused, and the sleep state of the human body can be accurately recognized and classified in combination with the bidirectional long-short-term memory network and time sequence self-correlation feature analysis.

The invention relates to the technical field of electric digital data processing, in particular to an intelligent early warning method and system for joint rehabilitation medical equipment, and the method comprises the steps: obtaining the heart rate and joint motion resistance of a user at a plurality of historical moments and in the rehabilitation training process at the current moment; fitting based on the joint movement resistance of the user at multiple historical moments to obtain a resistance safety baseline, and determining a resistance threshold value according to a mean value and a standard deviation of the resistance safety baseline within a set time; if the heart rate of the user at the current moment exceeds a set first threshold value or the joint movement resistance of the user at the current moment exceeds a resistance threshold value for a preset duration, triggering an alarm and stopping rehabilitation training. According to the invention, the problem that self-adaptive switching and intensity optimization adjustment of training modes cannot be realized is solved.

The invention discloses a millimeter wave radar health monitoring method and system based on wearable equipment, and relates to the technical field of non-contact physiological signal monitoring, the specific steps are as follows: low-frequency life signals of a monitored object are collected based on frequency modulation continuous waves emitted by a radar system, and the low-frequency life signals are time sequence signals of multiple channels; performing wavelet packet decomposition and adaptive filtering on the low-frequency life signal to obtain a filtered signal; and inputting the filtered signal into a trained feature extraction and separation model, carrying out feature extraction and evaluation, and outputting a heartbeat signal and a respiration signal. According to the method, a strong anti-interference preprocessing process is constructed by fusing wavelet packet dynamic decomposition and a sub-band energy entropy threshold method and combining an NLMS adaptive filtering technology, motion artifacts and environmental noise can be effectively stripped, the signal-to-noise ratio of original millimeter wave radar signals is increased, and the anti-interference performance of the millimeter wave radar signals is improved. And a solid foundation is laid for subsequent accurate extraction of weak vital sign signals (respiration and heart rate).

The invention discloses a multi-radar vital sign monitoring system applied to a fixed crowd place. The multi-radar vital sign monitoring system comprises a horizontal layer 24 GHz radar network, a vertical layer 60 GHz radar array and an auxiliary sensor module. Through three-dimensional collaborative deployment of heterogeneous radars, an improved variational mode decomposition algorithm and a joint probability data association tracking technology are combined to realize separation of breathing, heart rate and behavior characteristics of multiple targets in a dense scene. The system adopts a space-time diagram convolutional network to extract individual gait periodic features, establishes a unique biological feature coding file through dynamic gesture track matching, and completes non-contact identity binding. A multi-band radar chip and a dual-mode communication interface are integrated in hardware design, and a lightweight AI model is deployed in software architecture to realize localized real-time analysis. The embodiment covers nursing home tumble monitoring, hospital postoperative monitoring and kindergarten safety management and control scenes, the radar distance and the beam direction can be dynamically adjusted, and the problems of privacy disclosure and target confusion existing in a traditional monitoring system are solved.

The invention discloses a method for detecting heart rate, respiration and oxyhemoglobin saturation based on micro-vibration images, and belongs to the technical field of physiological detection and image processing. A camera collects facial micro-vibration videos of a testee in real time, uses an ROI algorithm to lock a facial key area, extracts RGB micro-vibration change signals to replace red light and near-red light signals, and determines whether the micro-vibration videos are abnormal or not; performing wavelet transform five-layer decomposition, three rounds of peak valley screening and abnormal value elimination on the RGB microvibration time sequence signal, performing fine tuning training by using a yov11 model, and calculating to obtain heart rate, respiration and blood oxygen physiological indexes. According to the invention, non-contact detection is realized through video acquisition and signal processing of micro-vibration of a face area, and the comfort level and the use convenience of a testee are improved; interference caused by motion artifacts, baseline drift and expression changes is eliminated through multi-scale wavelet denoising; the three-wheel peak-valley screening algorithm deeply excavates the features of the face micro-vibration signals, and the recognition accuracy of the signal peak-valley pairs is improved; and an abnormity elimination and self-feedback parameter adjustment mechanism improves the calculation precision.

The embodiment of the invention provides an information adjustment method, system and device for a virtual reality scene, and relates to the technical field of virtual reality, the method is applied to a management device in a virtual reality system, and the system further comprises a physiological monitoring device and a virtual reality device. The method comprises the steps of obtaining parameter information of heart rate variability associated parameters when a target object is in a current virtual reality scene, and obtaining current first information; on the basis of the parameter information corresponding relation, determining parameter information which specifies presentation parameters and corresponds to the current first information, and obtaining second information; and sending the second information to the virtual reality equipment, so that the virtual reality equipment adjusts parameter information of a specified presentation parameter of the current virtual reality scene into the second information. Visibly, the information of the virtual reality scene is adjusted, so that the target object can more quickly adapt to the stimulation source in the virtual reality scene, and the possibility that the target object generates the conflict psychology is reduced.

The invention provides a pet behavior data acquisition and emotion monitoring method and an integrated system. The method comprises the following steps: firstly, collecting heart rate, body temperature, environment temperature and humidity and videos, unifying a time base by matching short-distance wireless timestamp broadcasting with local time service, performing face mask, foreground separation and skeleton key point extraction on the videos by an end side, and only uploading structural features including key point coordinates and foreground area ratios; time lag is obtained through normalized cross-correlation of the skeleton key point speed sequence and the heart rate sequence, phase alignment is completed, and a first feature sequence is obtained; frequency domain analysis is conducted on the tail trajectory, the auricle pitch angle and the first feature are connected in series to form a second feature sequence, the second feature sequence is input into a cross-modal attention sequential network, and continuous coordinates and discrete categories such as relaxation, excitation and anxiety are output in a combined mode; a deviation degree is calculated based on an individualized rhythm baseline, and a graded alarm is output when the threshold is exceeded and the standard is continuously reached, so that pet emotion monitoring which is privacy-friendly, reliable in alignment and explainable is realized.

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 field of electroencephalogram data acquisition and processing, in particular to an electric power dispatcher cognitive load evaluation method based on brain-computer interaction and physiological signals, which comprises the following steps of: acquiring electroencephalogram signals of a dispatcher by adopting an anti-electromagnetic interference electroencephalogram acquisition device, synchronously acquiring heart rate, heart rate variability and eye movement signals through wearable equipment, and calculating the cognitive load of the dispatcher according to the heart rate, the heart rate variability and the eye movement signals; time synchronization of all signals is ensured; carrying out denoising processing on the electroencephalogram signals, and extracting frequency domain features; filtering and normalizing the physiological signals, and extracting features; the attention mechanism is adopted to dynamically distribute the weights of the electroencephalogram signals and the physiological signals, fused features are input into a model constructed by a long short-term memory network and a multi-layer perception mechanism, and low, medium, high and overload four-level cognitive load results are output; the cognitive load state is output in real time, and when a'high 'or'overload' level is detected, sound-light alarm and mobile phone APP push early warning are triggered; and generating personalized decision suggestions according to the power grid SCADA system data.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

The invention discloses an immersive cinema dynamic content adaptation control method based on multi-modal fusion, and the method comprises the steps: collecting at least one non-contact physiological signal of a target audience seat area in real time, the physiological signal comprising a respiratory wave signal collected by a millimeter wave radar or a photoplethysmography signal collected by a near-infrared photoelectric sensor; performing time-frequency analysis on the physiological signal, and extracting a physiological wake-up characterization value in a current time window; the physiological wake-up characterization value comprises a heart rate variability change rate and a heart rate deviation value; constructing a dynamic damping adjustment model, and calculating a virtual damping coefficient of the seat movement system at the current moment based on the physiological wake-up characterization value; and the virtual damping coefficient is converted into an execution instruction of a seat motion controller, and damping force output of the motion platform is adjusted in real time. The technical problem that the pre-programmed motion feedback is disjointed with the real-time physiological and psychological state of the audience can be solved, the experience adaptability and comfort of the cinema seat are improved, and the system safety is improved.

The invention relates to the technical field of internet-of-things scheduling, in particular to an internet-of-things-based automobile service scheduling method and system, and the method comprises the steps: collecting the heart rate and three-axis acceleration sequence of an automobile maintenance technician through a bracelet sensor, analyzing the heart rate variability, synthesizing the acceleration into an integral, comparing the integral with a fatigue threshold value, and mapping a risk level; and carrying out statistics on the grid signal density and combining with the urgency degree weight, calculating the distance between the vehicle and the task and carrying out physiological correction, evaluating matching score sorting and preferential selection, and generating an automobile service target task scheduling instruction set. Through fusion analysis of heart rate and acceleration data of automobile maintenance technicians, quantitative identification and dynamic monitoring of fatigue degree are realized, task urgency degree weighting is combined, comprehensive perception of task density and urgency degree is enhanced, states of the automobile maintenance technicians and task positions are associated, matching priorities are optimized by means of an input-output ratio, and the fatigue degree of the automobile maintenance technicians is improved. The scheduling accuracy and the response efficiency are improved, the problems of uneven personnel load and resource mismatching are relieved, and the scheduling intelligence and the service stability are enhanced.

The invention discloses a remote photoplethysmography (rPPG) signal extraction method based on a 3D convolutional neural network (3D CNN), and belongs to the technical field of image processing and physiological signal detection. In order to solve the problem that space-time information utilization is insufficient in a complex scene (illumination change and motion interference) in a traditional method, space-time feature fusion is carried out through 3D CNN, and feature expression is enhanced in combination with an encoder-decoder structure and an attention mechanism. The method comprises the following steps: extracting continuous time difference features of a face video, generating space-time fusion features through a 3D CNN, enhancing the space-time fusion features through a codec, and outputting rPPG signals. A self-supervised training method is innovatively proposed, and the generalization ability of the model is improved by adopting data enhancement, time period alignment and positive and negative sample comparison. Experiments show that according to the method, the heart rate estimation error is reduced to be within 1 BPM, the signal-to-noise ratio is increased to 1.8 dB, and the motion robustness is superior to that of a traditional algorithm. The method is suitable for non-contact heart rate monitoring, respiratory rate detection and other scenes, has the characteristics of high precision and strong anti-interference, and provides a reliable technical scheme for intelligent health monitoring.

The invention belongs to the technical field of life information monitoring, and particularly relates to an FMCW radar-based non-contact breathing and heartbeat combined detection method, which comprises the following steps of: identifying the chest of a target to be detected by utilizing a target detection algorithm to obtain a target area; controlling a steering engine to enable the FMCW radar to move to a corresponding target area, transmitting a linear frequency modulation signal, receiving a reflection signal, and performing frequency mixing to obtain an intermediate frequency signal; performing distance fast Fourier transform on the sampling data of the intermediate frequency signal to obtain a distance feature, and determining a distance unit corresponding to the target according to an extreme value of the distance feature; a phase value of a distance unit where a target is located is extracted to perform phase unwrapping and phase difference operation, then a phase difference signal is subjected to signal decomposition by using a variational mode decomposition (VMD) algorithm, and a respiration and heartbeat signal is processed by using an independent component analysis (ICA) algorithm to obtain a respiration signal and a heartbeat signal; and S4, estimating the respiratory frequency and the heart rate through the frequency spectrums of the respiratory signal and the heartbeat signal. According to the invention, the signal processing efficiency and accuracy are improved.

The invention discloses an rPPG physiological signal reconstruction recognition system based on generative adversarial optimization, and relates to the technical field of data processing. The system comprises a signal preprocessing module used for extracting an initial rPPG signal from an input video sequence; and the signal reconstruction module comprises a generator and is used for receiving the initial rPPG signal and outputting a reconstructed rPPG signal. According to the method, by introducing a multi-dimensional discriminator set and physiological prior loss collaborative optimization mechanism, the precision and robustness of rPPG signal reconstruction are remarkably improved. The system can restrain signal quality from multiple angles of time domain, frequency domain and time-frequency domain, and restrain irrational fluctuation in combination with physiological laws, thereby effectively overcoming motion artifacts and illumination interference. Meanwhile, the dynamic region-of-interest selection module adaptively focuses an optimal signal region through a learnable attention mechanism, the input quality is improved from the source, and finally high-reliability estimation of the physiological parameters such as the heart rate and the respiration rate in a complex scene is achieved.

The invention provides a behavior early warning method and system based on a child watch, and relates to the technical field of child watchs.According to the behavior early warning method and system based on the child watch, acceleration, angular velocity, heart rate and ambient light intensity data and real-time geographic coordinates are synchronously collected through a multi-modal sensor to generate a space-time calibration data stream, and multi-dimensional monitoring of child behaviors is achieved; through dynamic matching with a classification type safety behavior pattern library, the scene safety coefficient is calculated in combination with environment light intensity and an electronic fence database, the problem of false alarm and missing alarm caused by dependence on a single sensor is solved, and the method is based on a non-uniform time window segmentation and feature confidence evaluation technology. The system can dynamically adjust and analyze the window length and the overlapping rate according to the environmental risk level, considers the real-time performance and the data integrity, independently calculates the comprehensive risk index of each risk type by fusing the terrain complexity, the traffic risk coefficient and the typed environmental compensation factor in combination with the space-time fusion network, and improves the safety of the system. And efficient concurrent processing of diversified risks is realized.

Methods, systems, and apparatus for a vehicle preconditioning system. The vehicle preconditioning system includes a vehicle and an associated wireless key for the vehicle. The wireless key is configured to measure biometric data (e.g., heart rate, temperature, and the like) of a user of the wireless key to determine a physiological state of the user. The wireless key can transmit a signal including the biometric data of the user to the vehicle. The vehicle can receive the signal and precondition the vehicle based upon the signal. Preconditioning can include adjusting climate control settings, lighting settings, and / or audio settings, among other settings. The wireless key can transmit location data of the wireless key, which can be used to precondition the vehicle (e.g., to detect the wireless key is approaching or to determine a current activity of the user).