320 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 the technical field of diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

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 relates to the technical field of data acquisition control, in particular to a breeding environment temperature control system for a pig farm, and the system comprises a temperature acquisition module which collects the temperature data of each monitoring point in the pig farm in real time, and converts the temperature data into a temperature value sequence. According to the method, temperature data of a plurality of monitoring points in a pig farm are collected in real time, a discrete temperature data sequence is converted into basic environment information such as average temperature and temperature difference, and trend and anomaly analysis is carried out on the basis, so that the opportunity and demand of temperature adjustment are determined, the future temperature change trend of the environment is predicted, and a corresponding regulation and control plan is generated; on the basis of prediction, real-time biological feedback information, such as heart rate and body temperature, of pigs is fused, dynamic evaluation is carried out on the comfort level of individual animals, and an environment temperature regulation and control strategy is further optimized by taking the comfort level of the animals as a benchmark, so that the regulation of the environment temperature is more accurately matched with the growth requirements of the pigs, and personalized and intelligent control of the breeding environment is realized.

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 belongs to the technical field of motion monitoring, and provides an intelligent motion monitoring system and method oriented to water boat sports, a combined displacement sensor, an inertial sensor and a pressure sensing electronic fabric sensor are mounted on a boat body, a pressure sensor is mounted on a paddle, and an unmanned aerial vehicle monitoring system and a heart rate band are combined to monitor the motion of the boat. A multi-modal sensor system is integrated. Under the support of a deep learning algorithm, the invention realizes a multi-modal intelligent motion monitoring system. The system can comprehensively and accurately obtain the kinematics, dynamics and physiology parameters of the athlete in the water boat sports in real time, and does not interfere with the normal sports of the athlete. Through multi-dimensional data operation and coupling analysis, the most real athlete data model is constructed, and daily training of athletes is effectively guided.

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 provides a sound scene regulation and control method and system for a virtual reality scene, and relates to the virtual reality technology, and the method comprises the steps: segmenting a VR audio / video into slices at equal time intervals; packaging the heart rate variability characteristic data and the VR audio and video into attribute data of slices according to a time sequence of the slices; randomly adjusting the audio attribute of the slice, and collecting heart rate data of different experiencers when experiencing the slice with the adjusted audio attribute; extracting heart rate variability characteristic data, inputting the data into an emotion recognition classifier, and recognizing an emotion state of the experiencer as training data; according to the target emotional state and the counted emotional response, calculating Juyden indexes of different audio attributes to determine an optimal threshold interval of the audio attributes; and according to the association degree between the audio attribute of the optimal threshold interval and the emotional state of the experiencer, whether the audio attribute needs to be adjusted is judged, so that sound scene self-adaptive regulation and control are realized, the problem that sound field creation is not matched with visual experience is solved, and the user experience is improved.

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 relates to the technical field of machine learning, in particular to a nursing physical examination data intelligent analysis and management method which comprises the following steps: extracting body temperature, blood pressure and heart rate from a physical examination data set, performing Gaussian kernel mapping processing, and mapping physical examination data into a high-dimensional feature space to obtain high-dimensional feature data; and training a support vector machine model by using the high-dimensional feature data. Through the Gaussian kernel mapping of the high-dimensional feature space and the application of the support vector machine model, the processing capability of the nursing physical examination data is improved, and abnormal and normal data points can be distinguished. And the nonlinear representation of the data is enhanced through mapping, so that the model can process a complex mode more effectively, and outlier and error data are effectively removed. Real-time classification ensures fast processing of data streams, allowing instant updates and responses. And by dynamically marking the severity of the data points and adjusting the processing strategy, the flexibility and individuation of data management are further enhanced.

The invention relates to the technical field of automobile seats, in particular to an automobile seat system and method for intelligently adjusting and relieving the back pushing feeling, and the system comprises a sensor unit which comprises an acceleration sensor, a pressure sensor and a gyroscope which are arranged on a seat and is used for capturing the dynamic change of a vehicle during acceleration and the body posture of a passenger; the heart rate monitoring sensor is arranged on the seat and used for measuring physiological indexes of a passenger and evaluating the physiological reaction of the passenger to the back pushing feeling; the dynamic supporting and adjusting unit comprises a seat adjusting assembly and air bags located at the bottom of the seat and at the backrest part; the controller unit is used for calculating and adjusting the state of the seat adjusting assembly and the inflation amount in the air bag according to data monitored by the sensor unit so as to resist back pushing feeling and relieve discomfort of passengers; the dynamic state of the vehicle is monitored through the acceleration sensor, the acceleration trend is analyzed and predicted in combination with an algorithm, the seat is adjusted in real time through active response, and discomfort brought to passengers due to sudden acceleration or deceleration is reduced to the maximum extent.

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-view fusion millimeter wave radar breath and heartbeat monitoring method. The method comprises the following steps of 1, performing data preprocessing on collected millimeter wave radar original data, and obtaining time domain data, frequency domain data, real number data and imaginary number data from the radar original data; 2, using a backbone network of a channel attention convolution module to process the preprocessed data in parallel, and extracting effective features of each part; and step 3, processing a low-dimensional spatial feature # imgabs0 # or a high-dimensional spatial feature # imgabs1 # through a cross-domain fusion convolution Transform module. And 4, dynamically adjusting the importance of each part of the low-dimensional spatial feature # imgabs2 # and the high-dimensional spatial feature # imgabs3 # by using weight fusion, and generating a final fusion feature. And 5, performing multi-loss joint learning to optimize the prediction model by using the proposed fusion distance alignment harmonic index loss of the dynamic weight, the mean square error and the negative Pearson correlation loss. And 6, predicting the respiratory rate and the heart rate in parallel by the model by sharing the bottom fusion features.

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.

Disclosed are a method and a system for mental state perception and a computer-readable storage medium. The method includes: acquiring image sequences with timestamps and millimeter-wave radar raw data with timestamps; preprocessing the image sequences and the millimeter-wave radar raw data; analyzing the head region image sequences to obtain head vibration signal features; calculating face region image sequences obtained from preprocessing by using a remote photovolumetric pulse wave recording method to obtain a first heart rate; analyzing the original millimeter-wave radar data sequence to obtain a second heart rate and a breathing rate; fusing the first heart rate, the second heart rate and the breathing rate to obtain a fused heart rate and breathing rate; performing feature extraction on facial change information by a Transformer-like network; establishing a non-contact multi-modal mental perception model for prediction to obtain a predicted result of the mental state.

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%.