403 results about "Mood state" patented technology

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

The invention relates to the technical field of child cognitive training, and discloses an ADHD child cognitive training system. The system comprises a multi-modal sensing module, an attention evaluation module, a dynamic task generation module, a behavior feedback module and a self-adaptive adjustment module. The multi-modal perception module collects multi-dimensional behavior data, the attention evaluation module extracts attention fluctuation features, the dynamic task generation module generates a cognitive training task sequence, the behavior feedback module performs emotion state classification processing to generate feedback signals, and the self-adaptive adjustment module generates personalized training difficulty steps. According to the system and the method, through comprehensive data acquisition, precise attention assessment, dynamic task generation, real-time emotion feedback and difficulty adaptive adjustment, personalized and precise cognitive training of ADHD children is realized, the training effect is effectively improved, and the problems of lack of personalization, insufficient state monitoring, unscientific difficulty adjustment and the like of an existing training method are solved.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

The invention provides a depression virtual reality auxiliary diagnosis and treatment method fused with multi-modal data, and the method comprises the steps: obtaining the multi-modal data, the multi-modal data comprises the peripheral physiological data, such as heart rate, body temperature and skin electricity, monitored by a flexible sensor based on epidermal electrons; and switching to a personalized interactive virtual scene according to the emotional state of the patient in real time, and in the interaction process, obtaining eyeball movement data by the eye movement tracking instrument, and knowing attention distribution, psychological state and micro facial expression change of the user. In combination with multi-modal data of peripheral physiological data, eye movement data and micro-expression change, key features are automatically extracted from multi-modal input data, and emotion analysis and recognition are performed through a multi-modal deep learning method with an attention mechanism. Data can be collected and analyzed in real time, the emotional state of a patient is objectively reflected through the combination of peripheral physiological data and micro-expression analysis, the requirements of the patient are met in a personalized mode, and the treatment effect is improved.

The invention provides an emotion prediction method and device based on electroencephalogram signals. The method comprises the steps that the electroencephalogram signals of a target user are acquired; performing time-frequency analysis on the electroencephalogram signal to obtain a target electroencephalogram characteristic signal, and extracting electroencephalogram characteristics of different frequency bands from the target electroencephalogram characteristic signal to obtain multi-dimensional electroencephalogram characteristics; calibrating the resting emotional state baseline according to the multi-dimensional electroencephalogram features to obtain a calibrated emotional state baseline, and extracting features corresponding to the calibrated emotional state baseline to obtain calibrated electroencephalogram features; inputting the calibrated electroencephalogram features into an emotion prediction model, performing emotion prediction by using the emotion prediction model, and outputting to obtain an emotion prediction result; and executing real-time emotion monitoring and feedback based on the emotion prediction result. According to the method and the device, the problem of low emotion recognition precision caused by neglecting data individual differences and non-stationary features in an emotion recognition process in the prior art is solved.

The invention discloses a multi-modal emotion calculation enabling adaptive interactive language learning system and a matching method thereof, and belongs to the technical field of artificial intelligence, and the system comprises a multi-modal emotion real-time perception and quantification module MHAE-Net, a personalized adaptive dialogue and intervention strategy module ADIS-RL, and a virtual dialogue partner interactive interface module EC-NLG. The multi-modal emotion real-time perception and quantification module MHAE-Net is composed of a voice signal acquisition and processing unit, a voice emotion analysis unit, a text emotion analysis unit, a facial expression emotion analysis unit, a physiological signal emotion analysis unit and a multi-modal emotion fusion and decision-making unit. The interactive strategy is dynamically adjusted, the oral anxiety of the language is effectively relieved, the oral confidence is improved, the system adopts the multi-mode emotion calculation technology, the accuracy and robustness of emotion recognition are improved, the targeted interactive strategy is designed for different emotion states, and personalized emotion support and language practice are provided for learners.

The invention discloses an intelligent accompanying method and an emotion monitoring device, which are applied to an emotion monitoring intelligent bed, and the method comprises the steps: obtaining emotion data of a user, the emotion data comprising voice features, expression features and posture change features; judging different emotion data to obtain different emotional states, and obtaining a target emotional state of the user according to the different emotional states and preset weight information of the different emotional states; when the target emotional state is an abnormal state, sending out a first feedback signal; and according to the first feedback signal, guiding the user to perform voice interaction through a semantic analysis model, and outputting an audio reply corresponding to the user voice information. According to the method, the emotion state of the user can be detected and fed back in real time, the emotion of the user is adjusted in a voice dialogue communication mode, and the nursing quality of the user is improved.

The invention relates to the technical field of artificial intelligence, and discloses a brain-like multi-mode emotion recognition network, a brain-like multi-mode emotion recognition method and an emotion robot, and the brain-like multi-mode emotion recognition method comprises the steps: converting multi-mode emotion information into a pulse sequence; on the basis of the obtained pulse sequence, constructing a continuous pedigree emotion representation space, and realizing continuous representation of an emotional state; a continuous pedigree emotion representation space is utilized to generate pulse codes of mixed emotions, and a complex emotional state is represented; according to the pulse codes of the mixed emotions, establishing an emotional state conversion probability model, and describing a conversion relation between the emotions; based on an emotional state transition probability model, realizing prediction and processing of an emotional gradual change process, and capturing subtle emotional changes; according to the method, the working principle of human brain neurons can be used for reference, fine representation and accurate prediction of the human emotional state are achieved, and meanwhile high calculation efficiency and biological interpretability are achieved.

The invention discloses an emotional state monitoring method and system based on skin resistance change, and relates to the technical field of physiological signal monitoring and emotional state recognition. Preprocessing the signals and extracting adaptive features; dynamically correcting an individual baseline based on skin resistance variation; time sequence modeling is introduced to capture the dynamic change of the emotional state; customized emotion health suggestions are provided in combination with individual historical data. The method has remarkable innovativeness and practicability in the aspects of multi-dimensional signal collection, data processing, individual correction, time sequence modeling and historical data utilization, the accuracy, stability and personalized recommendation effect of emotional state monitoring can be effectively improved, and the method has wide application prospects and market value.

The invention belongs to the field of intelligent psychological counseling, and provides a multi-agent dynamic task integrated allocation method and system, and the method comprises the steps: extracting user historical semantic memory fragments through a long-term memory agent, and constructing a memory node set; obtaining current and historical emotional states of a user by an emotion analysis agent, and constructing an emotion node set; based on the semantic correlation and emotion similarity between the memory node set and the emotion node set, constructing an emotion-memory resonance map; according to the type of a resonance mode in the atlas, activating a corresponding function to drive a marshalling unit; and an intention attraction field is constructed according to the state change of the resonance spectrum in the time dimension, and is used for dynamically guiding the task to flow to the current intention node with the most active degree, and scheduling distribution is carried out on the corresponding intelligent agent in the function-driven marshalling unit based on the attraction field.

The invention discloses a depressive symptom assessment method and system based on multi-modal physiological data, relates to the technical field of artificial intelligence, and solves the problems of relatively high subjectivity, single data dimension, insufficient linkage and lack of a dynamic induction mechanism during depressive symptom assessment in the prior art. The method comprises the following steps: performing emotion induction on a subject by using a VR scene, collecting multi-dimensional physiological signals, behavior performance and subjective feedback information at the same time, and realizing feature extraction and multi-modal feature fusion; a depression risk assessment model trained by a deep neural network model is utilized to predict the current emotional state, depression symptom severity and potential abnormal reaction characteristics of a subject, and self-iterative learning of the depression risk assessment model and regulation and control of a VR situation push strategy are realized. The problems of single emotion induction mode and unstable reaction are solved, and the defects of high subjectivity and limited data dimension in a traditional evaluation method are effectively overcome.

The invention relates to the field of psychological disease early warning, and discloses a psychological disease early warning method and system based on artificial intelligence, and the method comprises the steps: carrying out the feature extraction and data fusion of collected and preprocessed physiological and behavior data, carrying out the emotion analysis of the emotional state of a user through the fused data, and carrying out the early warning of psychological diseases. The method comprises the following steps: preliminarily judging whether individualized emotion is abnormal or not, comparing physiological features and behavior features of abnormal emotion according to an analysis result of emotion abnormality, performing time sequence analysis on the physiological features and behavior features of known psychological disease symptoms, detecting whether a user has psychological diseases or not, and further evaluating and analyzing the severity of the diseases. According to the method and the system, the disease is subjected to graded early warning, so that the user is helped to realize own emotion change, the psychological disease is discovered and intervened as soon as possible, the analysis and detection of the psychological state are more accurate and reliable, the detection efficiency of the psychological disease is improved, and the disease risk is reduced.

The invention provides an automatic adaptation method and system for a multi-climate zone emotional response strategy. The method comprises the steps that S1, the type of a climate zone where a vehicle is located currently is recognized; s2, collecting multi-dimensional environment data reflecting the environment where the vehicle is located, and constructing a structured environment state vector based on the multi-dimensional environment data; s3, collecting multi-modal data of the target object, wherein the multi-modal data comprises a facial image, voice information and physiological information; extracting a feature vector reflecting an emotional state based on the collected multi-modal data, and identifying a current emotion category based on the extracted feature vector reflecting the emotional state; and S4, according to the identified climatic zone type, environment state vector and emotion category of the current vehicle, searching and matching an optimal strategy template in an emotion response strategy library, and generating equipment control parameters.

The invention relates to the technical field of large language models, in particular to a multi-modal digital human interaction method and system based on a large language model, and the method comprises the steps: obtaining video data, audio signals and text data of a single conversation when a user interacts with a digital human; extracting an envelope line of the audio signal, and determining an emotion evaluation value and a semantic contrast ratio of a single dialogue; identifying emotions contained in each frame of image in the video data to form an emotion sequence of a single dialogue; obtaining a semantic deviation value of a single dialogue; obtaining a semantic guide value of a single dialogue; and generating a corresponding emotional dialogue in combination with the emotional dialogue generation model. According to the method and the device, the deviation between the understanding of the intention expressed by the user and the perception of the emotion can be reduced, and the emotion state of the user can be more accurately judged, so that the generated emotion reply content is matched with the emotion state of the user, and the user interaction experience is improved.

The invention discloses an emotion rehabilitation intervention method based on face recognition. The method comprises the steps that S1, a user face image sequence is collected through a front camera; s2, performing image preprocessing to generate a standardized image tensor set; s3, facial dynamic features are extracted through an improved BiFormer network, emotional states are recognized, and a multi-dimensional emotional state sequence and a facial expression coding sequence are generated; s4, jointly analyzing the emotion change trend and the expression behavior mode of the user through a multi-mode RETNet network, and outputting a psychological state grade and a future emotion fluctuation score sequence of the user; s5, matching an emotion rehabilitation intervention strategy through a preset rule engine; s6, executing the emotion rehabilitation intervention strategy, collecting user data in real time, and generating a user response scoring sequence; and S7, performing incremental updating on the multi-modal RETNet network and a preset rule engine based on the user response score sequence. According to the invention, the accuracy of emotional state recognition and the intelligent level of intervention strategy pushing are improved.

The invention relates to the technical field of artificial intelligence and emotion calculation, and discloses an emotion state evaluation method based on multi-modal data, and the method comprises the steps: obtaining and decoupling an implicit signal, an explicit signal and situation data of an evaluated object; and respectively generating implicit features, explicit features and context vectors through double-flow decoupling coding and context knowledge graph coding. And the core engine determines the internal emotional state and the extrinsic emotional representation in parallel, and dynamically regulates and controls a bridging conversion process from the internal state to the predicted extrinsic representation by utilizing the situation vector. Finally, the emotion regulation intensity is calculated by comparing actual and predicted extrinsic representations, and the emotion regulation intensity, the internal state and the extrinsic representations are jointly used as a multi-dimensional evaluation result to be output. According to the method, the dynamic relationship and the adjustment strategy between the internal feeling and the external expression can be disclosed, a deeper and more three-dimensional analysis view angle is provided for the fields of mental health, human-computer interaction and the like, and the method has remarkable application value.

The invention discloses an emotion regulation system based on multi-dimensional perception and an evaluation method thereof. In the invention, through the emotion classification and intensity evaluation module using the support vector machine algorithm, the system not only can accurately divide the emotion of the user into different categories, but also can evaluate the intensity of the emotion, so that a more personalized emotion regulation scheme is provided for the user. The accurate emotion recognition enables the system to provide corresponding intervention measures according to the real-time emotion state of the user, such as music relaxation, breathing guidance or emotion catharsis games, so that the user is effectively helped to relieve negative emotion, and the emotion self-management ability is improved. The emotion trend analysis module predicts the emotion change trend by using an autoregressive moving average (ARIMA) model, so that the system can have perspectiveness, identify possible emotion fluctuation in advance and take preventive measures. The method can quickly respond when the emotion of the user is abnormal, and timely assistance is provided, so that the influence of emotion problems on the daily life and work of the user is reduced.

The invention provides an elderly user emotional state interaction method and system based on an intelligent accompanying large model. The senior user emotional state interaction method comprises the steps that daily data information of senior users is collected in real time, data processing is conducted on the daily data information, and a standardized time sequence data set corresponding to the daily data information is generated; fusing the daily data information of the elderly user by using multi-modal emotion fusion to obtain fused emotion parameters, and judging whether the elderly user has emotion abnormity or not by using the emotion parameters of the elderly user; and when the senior user has the abnormal emotion, sending prompt information to a guardian, and performing voice interaction with the senior user according to the emotion state of the senior user. The system comprises modules corresponding to the steps of the method.

The invention relates to the field of neuroscience and music therapy, and discloses an emotional chord music generation method and system based on EEG signals, and the method comprises the following steps: selecting an area which is located in the brain and is related to basic emotional processing, emotional regulation and cognitive processing as an EEG electrode position; acquiring a signal of the position of the EEG electrode, and preprocessing the signal; classifying the EEG signals by using a machine learning algorithm, and identifying different emotional states; constructing an emotional state-music parameter mapping multi-dimensional model; and mapping the identified emotional state to a music parameter conforming to the harmony of the trichord so as to generate music matched with the emotional state. According to the method, the EEG signal and music generation are creatively combined, the trichord music which is matched with the emotional state and has a harmonious pitch relation is generated according to the emotional state, and the individuation and accuracy of music generation are improved.

The invention relates to the field of cross fusion of artificial intelligence and brain science, and particularly discloses a brain-computer coupling-based agent common-situation response method under the guidance of the emotion of a learner, and the method comprises the steps: firstly, collecting the multi-modal emotion data of the learner oriented to the agent common-situation response; secondly, performing emotion analysis on the acquired multi-modal data of the learner under brain-computer coupling by adopting a multi-modal data emotional state judgment method; and designing a multi-modal common-situation response generation framework based on the emotion recognition result and the dialogue content, constructing a common-situation response model based on multi-modal representation, and carrying out joint learning training on the common-situation response model to form an agent common-situation response model based on a large language model multi-agent system under the guidance of the learner emotion. The emotional state of the learner is accurately identified, and the dialogue content of the intelligent agent is dynamically adjusted based on the emotional state, so that the emotional ability cultivation of the learner is enhanced.

The invention relates to the technical field of emotion recognition, and discloses an emotion monitoring system and method based on a brain-computer interface, and the emotion monitoring method based on the brain-computer interface comprises the steps: synchronously collecting and preprocessing physiological signals through multiple sensors; calculating credibility scores and screening available modes; adopting a credibility weighted attention mechanism to obtain a fusion feature vector and calculating feature quality; constructing a multi-branch inference network to perform emotion recognition, and outputting an emotion state and an overall credibility score; and detecting distribution drift and carrying out individualized multi-branch inference network calibration. According to the invention, through cooperative work of multi-modal physiological signals, when the quality of a single-modal signal is reduced, the stability of the overall performance can be ensured, and the emotion recognition accuracy is improved; through an online continuous learning mechanism, model calibration can be performed according to an individual emotion expression mode of a user, physiological feature differences of different users are adapted, and a complete solution is provided for practical application of an emotion monitoring technology.

The invention discloses a man-machine interaction method and device, equipment and a medium, and relates to the technical field of natural language processing, and the method comprises the steps: responding to a dialogue instruction triggered by a user, and collecting the voice of the user and an image of the user; the method comprises the following steps: analyzing voice of a user through a voice emotion analysis technology to obtain a first result, recognizing an image of the user through a facial expression recognition technology to obtain a second result, and obtaining an emotion state of the user according to the first result and the second result; judging whether the intention of the user is matched with the intention in the candidate intention set or not according to the voice of the user and the emotional state of the user; if the intention of the user is matched with the first intention in the candidate intention set, reply content corresponding to the first intention is replied to the user, and the method can improve the accuracy of a machine to understand user requirements, so that the accuracy of reply of the machine to the user is improved, and the interaction experience of the user is improved.

A method for increasing the well-being of an occupant in a vehicle comprises training a plurality of machine learning models to predict the emotional state. The method may further comprise recording a plurality of sensor data using at least one vehicle sensor. The method may further comprise determining an occupant personality profile. The method may further comprise selecting a selected model from the plurality of machine learning models to predict the emotional state based at least in part on the occupant personality profile. The method may further comprise determining an emotional state of the occupant based at least in part on the plurality of sensor data using the selected model from the plurality of machine learning models to predict the emotional state.The method may further include adjusting operation of an autonomous driving system of the vehicle based at least in part on the emotional state of the occupant.

The invention discloses a wearable emotion regulation system and method integrated with an artificial intelligence algorithm, and belongs to the technical field of intelligent wearing, system hardware comprises an Arduino microcontroller, a biosensor array, an audio output module, a man-machine interaction module and a communication module; software comprises a multi-mode emotion recognition model, a self-adaptive music recommendation engine and a self-healing logic controller, implementation steps include data acquisition and preprocessing, emotion state recognition, music interaction strategies and closed-loop feedback adjustment, the system is integrated to clothes, and the system has specific sensor layout and a safety mechanism. The method can be used in the fields of depression adjuvant therapy, motion monitoring and the like.

The invention relates to the field of sleep medicine, in particular to a wearable monitoring system for sleep-awakening rhythm disorder and a method thereof. Comprising a monitoring module, a signal processing module, a signal analysis module and an intervention scheme execution module. The monitoring module is used for acquiring collective acceleration, PPG signals and emotional state data; the signal processing module is used for filtering and down-sampling; the signal analysis module extracts time-domain and non-time-domain features by using a pulse neural network; and the intervention scheme execution module selects a personalized phototherapy and vibration intervention scheme by adopting a Q-learning algorithm according to the feature data. According to the system, through technical innovation and ingenious design, high intelligence and individuation are achieved, the sleep quality and the daytime function of the user are effectively improved, and a new tool and method are provided for sleep medical research.

The invention discloses a sound mixing and noise reduction method and system, and the method comprises the steps: firstly, carrying out the recognition of the emotional state of a person in a vehicle through an emotion recognition method; and then, whether the person in the vehicle has a bad emotion which can influence the behavior state or not can be judged according to the emotion recognition result. And if the person in the vehicle does not have the impatient emotion, the current noise in the vehicle does not need to be processed. On the contrary, if the person in the vehicle has a restless emotion, the sound signal in the vehicle is immediately collected through the sound pick-up. And finally, a sound wave identification method can be adopted to extract a noise feature vector of a noise sound segment which affects the emotion of people in the vehicle from the collected sound signals, and a corresponding sound mixing sound wave band is generated based on the extracted noise feature vector and is fused with the noise sound segment which affects the people in the vehicle, so that the sound mixing sound wave band becomes a sound segment which can be accepted by people. Therefore, the influence of noise on the behavior state of people is reduced.

The invention discloses an intelligent cabin healthy light environment adjusting system and method based on biological feedback, and belongs to the technical field of intelligent cabin and vehicle control. The method comprises the following steps: collecting a multi-modal physiological signal of a passenger; physiological signals are processed in parallel through two paths; determining a feed-forward control weight based on the dynamic change degree between the current steady-state emotional state and the future emotional state trajectory; according to the feed-forward control weight, performing weighted fusion on the current steady-state emotional state and the future emotional state track to generate a comprehensive light environment adjustment instruction; according to the comprehensive light environment adjusting instruction, a dynamic light and shadow relieving strategy is generated and executed, and the strategy at least comprises at least one of the color temperature, the brightness or the dynamic rhythm of the cabin light environment. By introducing the feedforward control mechanism, the emotional fluctuation of the passenger can be predicted and intervened in advance, the response delay of the system is reduced, the conversion from passive response to active prediction is realized, and the timeliness and accuracy of emotional relief are improved.

The invention relates to the field of education, and discloses an interactive feedback system for modern education, which comprises a state monitoring module, a feedback optimization module, a learning recommendation module, a teaching analysis module and a long-term tracking module, the state monitoring module is used for collecting expression, voice and eye movement data of the student in real time to judge the emotional state and understanding condition of the student; the feedback optimization module dynamically adjusts a feedback strategy according to the learning state and presents the feedback strategy to the teacher; the learning recommendation module generates a learning resource list according to individual demands of students and pushes the learning resource list to the student terminals; the teaching analysis module analyzes the data of the student group and provides teaching strategy optimization suggestions for teachers; and the long-term tracking module records learning data of the students and performs time sequence analysis to form a long-term feedback report. According to the invention, real-time feedback, personalized learning support and data-based teaching strategy optimization for students can be realized, and the teaching efficiency and the learning effect of the students are improved.

The invention relates to the technical field of video recognition, in particular to a smart home emotion interaction method and system based on a monitoring camera, and the method comprises the following steps: deploying the monitoring camera in a living space, capturing continuous video frames in the living space, and generating preliminary biological feature data; and based on the preliminary biological characteristic data, analyzing expressions and postures of the residents, and generating expression and posture analysis results. According to the invention, facial expressions and posture changes of residents are captured through video streams, a multi-dimensional emotion recognition system is established, and dynamic evaluation of short-term and long-term emotional states is realized. Expression and posture sequences are processed independently, emotion changes can be recognized, and misjudgment caused by singleness of facial features is avoided. The dynamic early warning mechanism improves the recognition precision of the abnormal state through the comprehensive evaluation of the behavior pattern and the emotion trend, so that the detection of the abnormal emotion not only depends on the single-frame image information, but also carries out the judgment in combination with the time dependence characteristic of the behavior sequence.