184 results about "Cardiac rate" patented technology

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 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 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 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 belongs to the technical field of computer vision and behavior recognition, and particularly discloses and provides a multi-dimensional drowning risk trajectory backtracking visualization system, which is characterized in that through time-space correlation analysis of swimmer stroke frequency variation coefficient, heart rate fluctuation trend and water flow data, traditional single data source monitoring limitation is broken through, and early warning mode upgrading is realized; based on spatial relationship detection of dynamic visual range rendering and risk key nodes, responsibility boundaries lacking environmental objective limitation and human intervention are distinguished, and wave height and illumination intensity are introduced to correct transparency of a mask layer in real time, so that the view of a lifeguard is highly consistent with an actual shielding state; a risk weight distributor and a three-dimensional space-time path are utilized to convert chain reactions of action abnormity, water flow environment sudden change and view shielding into a space-time evidence chain capable of being quantitatively traced, multiple data sources are integrated to generate a visual report, and the proof effect of drowning accident liability traceability and the accurate optimization effect of safety design are achieved.

The invention discloses an attention reminding algorithm for intelligent driving based on multiple modes, and relates to the technical field of intelligent driving and active safety, and the algorithm comprises the steps: firstly, collecting data in real time through a multi-mode sensor group, carrying out the alignment, and then calculating the quantization features, such as the fixation deviation degree, the steering wheel disturbance entropy and the heart rate variability; then, mapping scores by adopting an S-type function, analyzing a historical sequence by utilizing a long short-term memory network to generate a dynamic weight, and performing adjustment and weighted calculation on a single-mode score in combination with an environment complexity weight to obtain a comprehensive attention score; and finally, according to the continuous driving duration, calculating a linear decreasing dynamic reminding threshold value, executing dual logic judgment in combination with the environmental risk level, and triggering graded feedback through sound and light or vibration. According to the method, the problems of poor adaptability and high false alarm rate of traditional single-mode monitoring in a dynamic environment are effectively solved, the robustness of the system is improved, and adaptive evaluation and accurate early warning of driver distraction behaviors in a complex scene are realized.

The invention discloses a VR simulation experience operation and maintenance system for a theme park, and relates to the technical field of virtual simulation, in particular to a theme park simulation system composed of VR head display equipment, a motion capture sensor, a physiological monitoring module, a central processing unit and a scene database. Through identity verification of multi-modal biological feature fusion, it is ensured that only authorized tourists can participate, meanwhile, the difficulty is dynamically adjusted according to the performance of the tourists, each tourist can obtain personalized experience suitable for the level and interest of the tourist, the physiological data of the tourists are monitored in real time, and if the heart rate is abnormal, gradual load reduction measures are adopted in time, and the user experience is improved. Comprising the steps of reducing rendering complexity, starting an optical filter and a tactile feedback prompt, forcibly exiting when necessary, ensuring physical and psychological safety of tourists to the greatest extent, improving data reading and processing speed through a hierarchical storage architecture of a scene database and a heterogeneous computing architecture of a central processing unit, optimizing system resource utilization, and improving system performance. And smooth operation in a complex scene can be ensured.

1. The name of the design product: the nursing machine. 2. The use of the design product: for the nursing of the elderly at home through the functions of fall detection, heart rate and respiration detection, and automatic opening of telephone communication, video chat, etc. when abnormal. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: Design 1 perspective view 1. 5. Design 1 is designated as the basic design.

The present application belongs to the technical field of health monitoring and medical equipment, and provides a syncope risk assessment method for children and adolescents based on a photoelectric plethysmogram signal, comprising: constructing a personal heart rate variability coefficient fitting baseline and a personal photoelectric plethysmogram amplitude variability coefficient fitting baseline, collecting a photoelectric plethysmogram signal under a real-time state of a user, calculating and obtaining a heart rate variability coefficient and a photoelectric plethysmogram amplitude variability coefficient under the real-time state of the user according to the photoelectric plethysmogram signal under the current state of the user, and calculating and obtaining a syncope risk score of the user at present through a comprehensive evaluation model constructed based on fuzzy logic fusion.The syncope risk assessment method for children and adolescents based on the photoelectric plethysmogram signal can accurately capture changes in the physiological state of a syncope precursor of a user, realize early warning of syncope occurrence, reserve sufficient time for subsequent intervention, and thus effectively prevent or delay the occurrence of a syncope phenomenon of the user.

A method and system are provided for estimating the respiratory rate of a subject using a wearable real-time heart rate monitor. The method includes receiving heart rate data indicating the subject from the real-time heart rate monitor; determining a heartbeat annotation of the heart rate based on the received data; filtering the heartbeat annotation to remove outliers from the heart rate; determining an IBI sequence based on the filtered heartbeat annotation; determining the power spectral density of the determined IBI sequence; inputting the power spectral density, a lower limit estimate of the respiratory rate, and an upper limit estimate of the respiratory rate into a respiratory rate estimation algorithm; using the respiratory rate estimation algorithm to determine the peak energy and maximum frequency energy of the power spectral density; and outputting an estimated respiratory rate by fusing the peak energy and maximum frequency energy using the respiratory rate estimation algorithm.

A radar system for vital signs monitoring such as at least heart rate or breathing and a method for mitigating random body movement and external interference effects in vital signs monitoring such as at least heart rate or breathing by employing a radar system that is configured for providing in-phase and quadrature signals from reflected and received radar waves are provided. The problem of random body movement effects and external interference affecting vital signs monitoring is at least mitigated by decomposing and filtering a reconstructed displacement signal using a time and frequency analysis technique such as Short-Time Fourier Transform (STFT), followed by a Non-negative Matrix Factorization (NMF) operation. This decomposition allows the identification of time and frequency basis components containing the random body movement interference. Hence, the filtered signal can be reconstructed after removing the random body movement components, thus enabling reliable and robust vital-signs parameter estimation.

A physiological information display apparatus includes a heart rate acquisition unit configured to acquire a heart rate of a subject, a respiration rate acquisition unit configured to acquire a respiration rate of the subject, a hemodynamics calculation unit configured to calculate a hemodynamic parameter of the subject, and a respiratory variation of the hemodynamic parameter, based on a plurality of the hemodynamic parameters calculated in a predetermined period, and a display controller configured to output the respiratory variation to a display. The hemodynamics calculation unit is configured to calculate a heart rate in one respiration. The display controller is configured to output a setting screen for setting a length of the predetermined period. The hemodynamics calculation unit is configured to acquire setting information, set the length of the predetermined period based on the acquired setting information; and calculate the respiratory variation in the set predetermined period.

1. The name of the design product: smart watch (AIMB-Z28). 2. The use of the design product: smart watch with downloadable software for displaying time, while having heart rate monitoring, real-time body temperature monitoring, step counting, Bluetooth information pushing, sitting reminder, sleep monitoring, Bluetooth call, Bluetooth music playing, earphone call, bedside lamp, off-screen display pointer, payment, etc. System functions. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention discloses a sensory-cognitive progressive VR psychological counseling and relaxation system for the elderly, comprising a sensory activation experience module and a cognitive training and adjustment module. The sensory activation module, through guided virtual scenes and interactive tasks, enables elderly users to achieve emotional relief and attention recovery through low-intensity operation. The cognitive training module, through physiological monitoring devices, acquires real-time data such as heart rate and eye movement, dynamically adjusts task difficulty, and guides users in active cognitive training such as breathing regulation and attention allocation. The system allows users to select modules according to their own condition, achieving a comprehensive goal of emotional optimization, cognitive maintenance, and improved self-efficacy through age-appropriate interaction. This invention, by constructing a progressive adjustment path of "sensory activation - cognitive training," achieves progressive psychological counseling from emotional arousal to cognitive intervention.

The invention relates to an intelligent low-altitude economic scene passenger emotion pacifying method and system based on a large model. The method comprises the following steps: constructing a multi-modal emotional state data set of low-altitude travel passengers; wherein the multi-mode emotional state data set comprises passenger face video sequence data, in-cabin voice and audio data, heart rate variability time sequence data, respiratory frequency time sequence data and external situation context data; performing emotional depth feature extraction based on multi-source space-time signals on the multi-modal emotional state data set to obtain multi-source emotional features; emotion recognition is carried out based on the multi-source emotion features; based on the emotion recognition result, constructing a pacifying strategy generation model; and utilizing a pacifying strategy generation model to intelligently pacify the emotion of the passenger, and evaluating and feeding back the pacifying effect in real time. According to the emotion and specific inducements recognized in real time, pacifying measure suggestions which conform to safety specifications, fit scenes and are natural in language can be generated, and timeliness and specialty of passenger cabin service are improved.

The application relates to a human-vehicle intelligent security method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring heart rate variation information of a driver, each facial image of the driver and multi-angle photographed vehicle images, and identifying facial expression information corresponding to each facial image; identifying psychological state information of the driver based on the heart rate variation information of the driver, and identifying emotion change distribution information of the driver based on each facial expression information; identifying vehicle abnormal information of the vehicle based on each vehicle image, and identifying intention information of the driver through an abnormal emotion analysis strategy based on the psychological state information of the driver and the emotion change distribution information of the driver; and determining a target detection result of the vehicle based on the intention information of the driver and the vehicle abnormal information of the vehicle. The method can improve the comprehensiveness of human-vehicle security.