4951results about "Psychotechnic devices" patented technology

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The invention belongs to the technical field of biological calculation, and discloses an intelligent music regulation and control system and method based on electroencephalogram signal multi-modal feature recognition, electroencephalogram data are obtained through anti-interference collection and adaptive filtering processing, multi-modal feature vectors are constructed based on physiological feature extraction and information fusion, and the multi-modal feature vectors are extracted and subjected to multi-modal feature recognition. And accurate recognition of the emotional state and cognitive load evaluation are realized. A music-emotion mapping network is constructed through music feature analysis and emotion semantic matching, a personalized music regulation and control strategy is formulated, music parameter dynamic adjustment and audio stream reconstruction are realized, and a music output stream is generated. A closed-loop optimization feedback mechanism is formed by adopting electroencephalogram feedback collection and adjustment effect evaluation, user data security grading and privacy protection processing are implemented, a security specification database is constructed, the effect of a music output stream is continuously evaluated, and a comprehensive evaluation report is generated. Immediate emotion regulation is provided for the user, a healthier emotion management mode is established for the user through long-term data accumulation, and psychological toughness is improved.

The invention provides a dynamic calibration method and system for a vehicle-mounted emotion recognition system, and the method comprises the steps: S1, obtaining multi-source data which comprises a facial image, a voice signal and a physiological signal; the obtained multi-source data are preprocessed, and preprocessed multi-source data are obtained; s2, performing feature extraction based on the preprocessed facial image, the voice signal and the physiological signal to obtain a facial expression feature vector, an audio feature vector and a physiological state feature vector; s3, evaluating the current environment credibility based on an environment credibility evaluation function; s4, dynamically distributing the weight of the multi-source data according to the credibility of the current environment and the real-time scene; and S5, constructing a multi-modal fusion vector based on the dynamically distributed weight of the multi-source data, the facial expression feature vector, the audio feature vector and the physiological state feature vector, and performing emotion recognition by using the constructed emotion recognition model based on the multi-modal fusion vector.

The invention discloses a multi-modal signal fusion emotion recognition method based on an attention mechanism. The method comprises the following steps: firstly, preprocessing physiological signal data; constructing a channel attention module for the multi-channel electroencephalogram data; respectively extracting EEG and other physiological signal features by using the EEGNet; a shared-private encoder is introduced to decouple shared features among the multiple modes and private features of each mode; and finally, providing a cross-modal cross attention fusion mechanism to realize multi-modal feature interaction and effective fusion. According to the method, multi-modal signals are combined, the limitation of a single mode is overcome, and a sharing-private feature separation mechanism and a cross-modal cross fusion mechanism are introduced, so that modal collaborative modeling and effective integration are realized, and the accuracy and generalization ability of emotion recognition are improved.

The invention relates to the technical field of psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention belongs to the field of artificial intelligence, and provides a multi-modal depression recognition system based on an MFE-CCANNN model, which comprises a data acquisition unit, a data preprocessing unit and an MFE-CCANNN model unit. The data acquisition unit synchronously acquires multi-mode data such as videos, audios, texts and fNIRS when a subject performs the same interview task. The data preprocessing unit comprises a video preprocessing unit, an audio preprocessing unit, a text preprocessing unit and an fNIRS preprocessing unit. The MFE-CCARNN model unit comprises a video, audio, text and fNIRS neural signal feature extraction module, a multi-modal feature fusion module and a classification module, and depression recognition and classification result output are achieved. The system supports four-level depression degree discrimination, is high in recognition precision, portable in deployment, high in interpretability and the like, and is suitable for psychological health screening and clinical auxiliary evaluation scenes.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The embodiment of the invention provides an emotion recognition method and device based on artificial intelligence, and identity verification is realized through voiceprint features by creatively integrating multi-modal data of facial images, voices and body actions. And a multi-level attention fusion network is designed, and intelligent fusion of modal interior and cross-modal features is realized by using a feature attention layer and a modal attention layer. An emotion change time sequence model is constructed in combination with medical record data, the physiological indexes are fused for evaluation and correction, and dynamic evaluation and accurate monitoring of the emotional state are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal fusion, time sequence modeling, medical monitoring and the like are effectively overcome, and the accuracy and the practical value of emotion recognition in the medical scene are remarkably improved.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a psychological state pre-screening system and method based on multi-modal data. The psychological state pre-screening system comprises a data acquisition module, a data preprocessing module, a psychological state analysis module, a psychological state evaluation and screening module and a feedback and early warning module. The system collects multi-modal data such as facial expressions, voices, texts, physiological signals and the like, and performs psychological state analysis by using a deep learning algorithm. The psychological state score is calculated through a multi-modal data fusion algorithm, and feedback or early warning is provided, so that the user or related mechanisms can perform further intervention. According to the method, the accuracy and the real-time performance of psychological state screening can be improved, and the problem of misjudgment of a single data source is avoided. The system can be integrated to a smart phone, a smart bracelet, a computer and other equipment, realizes low-cost and non-perceptual mental health monitoring, is suitable for personal health management, enterprise employee mental monitoring, psychological counseling auxiliary diagnosis and school psychological screening, and is beneficial to early discovery and intervention of mental health problems.

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.

The embodiment of the invention provides an elderly emotion recognition method and device based on multi-modal perception, and the method and device achieve the optimization and enhancement of the signal quality through innovatively constructing a multi-modal data preprocessing mechanism and integrating the facial expression, voice and posture features. And designing a personalized feature mapping model based on historical emotion expression data, and establishing an adaptive feature fusion strategy for intelligent matching in combination with a cross-modal attention network. A hierarchical time sequence classification mechanism is introduced, dynamic modeling of the emotional development trend is realized through a long-short term memory network, and accurate prediction of the emotional state is supported. According to the method, the defects of the traditional technology in the aspects of multi-modal processing, personalized modeling, time sequence analysis and the like are effectively overcome, and the accuracy and reliability of sentiment recognition of the old people are remarkably improved.

The invention discloses a Multi-agent-based psychological counseling platform and method. The counseling platform comprises an evaluation agent used for dynamically extracting psychological features based on dialogue behaviors of a visitor agent and updating results to a traits library module; the planning agent is used for constructing a personalized psychological counseling scheme based on the traits library module and dynamically adjusting a counseling process and a strategy according to counseling feedback; the consultant agent is used for carrying out dialogue interaction with the visitor agent according to the personalized consultation scheme and implementing emotion pacification and psychological intervention; the visitor agent is used for expressing psychological troubles, feeding back psychological state changes and promoting the consultation process; the historical dialogue long and short-term memory module is used for storing multiple rounds of psychological counseling interaction contents and comprises historical dialogue short-term memory and historical dialogue long-term memory; and the dynamic probability memory retrieval module is used for generating probability distribution based on the current consultation context and dynamically calling memory nodes from the historical dialogue long and short term memory module or the traits library module.

The invention discloses an attention deficit hyperactivity disorder recognition system and recognition method, and belongs to the technical field of attention deficit hyperactivity disorder. The data processing module is used for carrying out preprocessing and feature extraction on the acquired electroencephalogram data; a multi-source feature fusion mechanism is firstly used for the extracted original feature data, and then a data enhancement strategy is applied; a multi-source fusion feedback regulation network model is constructed, wherein the model is of a CNN-GRU parallel modeling structure; the training module is used for inputting the enhanced data into a model for training, key hyper-parameters are dynamically adjusted by a performance feedback adjusting mechanism in the training process, and the performance feedback adjusting mechanism is used for dynamically adjusting key training parameters according to the performance of the verification set; and the classification module is used for classifying to-be-detected samples through the trained multi-source fusion feedback regulation network model and outputting a final recognition result. The ADHD electroencephalogram recognition method effectively improves the accuracy, robustness and generalization performance of ADHD electroencephalogram recognition.

The invention relates to a fatigue detection method of a hybrid convolutional neural network based on multi-modal physiological signal fusion, and belongs to the technical field of fatigue detection and signal processing in artificial intelligence. Comprising the steps of single-modal feature extraction, modal independent encoder construction, multi-modal fusion module construction and fatigue detection and classification. The method has the advantages that a multi-modal hybrid convolutional neural network fusing space, time and frequency characteristics is adopted to jointly model electroencephalogram and electro-oculogram signals, so that more comprehensive and accurate fatigue detection is realized; a modal specific encoder is designed for the electroencephalogram signals and the electro-oculogram signals to jointly capture time, space and frequency characteristics and frequency domain characteristics, and the limitation that a traditional method neglects cross-dimension dependence is solved; cross-modal fusion is carried out by fusing the attention module and the transformer encoder, the complementary advantages of the two modals are effectively utilized to improve the feature distinguishing capability, redundant information between the modals is reduced, and the distinguishing capability of the model for different fatigue states is enhanced.

The invention provides a psychological analysis method and system based on multi-modal features, and relates to the technical field of psychological assessment. The method comprises the following steps: synchronously obtaining voice data, video data, text data and physiological signal data of a user; respectively denoising the data to obtain each piece of denoised modal data; performing feature extraction on each modal data to obtain each modal feature; mapping each modal feature vector to a unified semantic space through an attention weighting mechanism to generate a fusion feature matrix; a trained psychological analysis model is adopted to perform multi-task learning on the fusion feature matrix, and emotion classification, pressure indexes and psychological health risk scores are synchronously output; and updating model parameters of the psychoanalysis model through an online incremental learning mechanism based on the real-time feedback data of the user and the newly added sample. Through accurate data denoising, efficient feature extraction and flexible model optimization, comprehensive and accurate analysis of the psychological state of the user is realized.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention provides a driving fatigue monitoring auxiliary system based on Beidou satellite positioning and a multi-modal data fusion technology, which relates to the field of electric digital data processing and comprises a multi-modal information sensing and acquisition module, a data preprocessing and quality assurance module, a fatigue state recognition and evaluation module and an intelligent early warning and adaptive optimization module. The multi-modal information perception and acquisition module is responsible for acquiring driver states, driving behaviors and environment information in real time, and the data preprocessing and quality assurance module is responsible for performing space-time alignment, quality evaluation and feature standardization on multi-source data. The fatigue state recognition and evaluation module is responsible for fusing multi-dimensional features and judging fatigue levels and risk trends, and the intelligent early warning and self-adaptive optimization module is responsible for implementing hierarchical intervention and continuously optimizing system performance; according to the system, the accurate space-time reference provided by Beidou satellite positioning is utilized, effective fusion of multi-source heterogeneous data is realized, and the accuracy, the real-time performance and the individuation level of fatigue monitoring are remarkably improved.

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.

The invention relates to the technical field of medical rehabilitation, and discloses a vestibular function and cognitive function rehabilitation training system and method based on virtual reality, and the rehabilitation training system comprises a hardware platform, a software platform and a server. The hardware platform comprises VR interaction equipment, a head motion tracking sensor and calculation and display equipment; the software platform is integrated with a user management module, a visual stimulation module, a vestibular rehabilitation training module, a cognitive evaluation and training module, a dynamic visual acuity evaluation module and a data management and analysis module. By combining black and white chess grid visual stimulation, standardized vestibular rehabilitation tasks, spatial cognitive testing and DVA objective monitoring, synchronous evaluation and personalized collaborative intervention of vestibular functions and cognitive functions of dizzy patients are realized. According to the method, the problems of boring traditional rehabilitation means, subjective evaluation and lack of quantitative feedback are solved, and the scientificity, interestingness and curative effect testability of rehabilitation training are remarkably improved.

The invention discloses a dangerous behavior recognition method and device based on multi-source physiological signal fusion, and aims to generate a physiological synchronization value reflecting a heart-brain coupling state by monitoring electroencephalogram and electrocardiosignals and calculating a phase locking value. Strong coupling signal pairs are screened based on physiological synchronization values, abnormal delay propagation paths and cascade mutation points are extracted, and dangerous trigger nodes and high-risk accumulation areas are identified through energy density analysis; the abnormal gradient field is subjected to rotation modulation innovatively through electroencephalogram signals, and a dynamic gradient field is generated to determine a dangerous behavior boundary; establishing an electrocardio resonance matching mechanism to generate a self-adaptive monitoring sequence and a grading early warning strategy; dynamically optimizing resource configuration and constructing a danger convergence field by adopting an adaptive acquisition scheme triggered by rhythm variability; and finally, early warning information is converted into a specific dangerous behavior category based on an accurate identification window, and an omnibearing intelligent monitoring solution is provided for high-risk operation safety management.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

A method to prevent intoxicated operation is described. The method includes providing instructions to a vehicle user to position a vehicle user body portion posture in a predefined alignment. The method further includes obtaining the vehicle user body portion posture from a first vehicle detector, and determining whether the vehicle user body portion posture is in the predefined alignment. The method includes activating a plurality of vehicle visual indicators to illuminate in a predefined manner when the vehicle user body portion posture is in the predefined alignment. The method further includes providing instructions to the vehicle user to move vehicle user eyes to track the plurality of vehicle visual indicators, and obtaining a vehicle user eye movement from a second vehicle detector. The method further includes determining whether the vehicle user eye movement meets a predetermined condition, and actuating a control action accordingly.