332 results about "Mood state" patented technology

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 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 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 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 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 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 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 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 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 provides a method, a device, equipment and a storage medium for improving a link establishment rate. Behavior data and text semantics are fused through a pre-trained feature extraction model to generate user feature vectors, a multi-level user tag system is constructed by using a tag generation model, and common features of both parties are extracted through a tag clustering model. And then, in combination with historical interaction data and a label system of the target user, obtaining a current emotion state by using an emotion analysis model, and displaying the emotion on an initiator interface in a pet expression form. And finally, inputting the matching features, the emotional state and the tag system into a copywriting generation model, generating an emotion-adaptive personalized recruitment, and dynamically optimizing each model parameter according to the response, thereby improving the correlation and the infectivity of the information, and further improving the success rate of establishing the recruitment.

The invention discloses a personalized interaction method and system based on multi-modal emotion perception, and relates to the technical field of intelligent interaction, and the method comprises the steps: collecting user historical interaction event data, and extracting interaction content features and user behavior features; obtaining interaction emotion data of the user in real time through a multi-mode sensor; fusing the interactive emotion multi-modal data to generate a dynamic emotion quantized value; generating an emotion correction factor; dynamically generating a personalized interaction strategy; and executing the interaction strategy, and collecting user response data to update a historical interaction event library. The method has the advantages that the key problem that a traditional interaction system is difficult to perceive the real-time emotional state of the user is effectively solved, a closed-loop optimization system of perception-decision-execution-feedback is constructed, and the user interaction satisfaction degree and the situation adaptive capacity of the whole system are continuously improved.

The invention relates to the technical field of emotion monitoring and adjusting, and discloses an emotion monitoring and adjusting system based on real-time dialogue semantic analysis, and the system is characterized in that an information collection module is used for collecting real-time voice information of two dialogue parties; the real-time dialogue semantic analysis module is used for performing semantic analysis on real-time voice information by adopting a MobileViT network, extracting emotional characteristics and dynamically capturing emotional association in a dialogue in combination with a CSN network; the bidirectional emotion regulation module is used for determining the current emotion state of the user based on the output of the real-time dialogue semantic analysis module, mapping the emotion state to a behavior mode, and respectively generating personalized emotion regulation strategies for two dialogue parties by adopting a Bandit algorithm; the hardware and software collaborative interaction module is used for controlling the bone conduction equipment and the LED feedback device according to the emotion regulation strategy generated by the bidirectional emotion regulation module; through cooperative work of software and hardware, real-time monitoring and effective adjustment of emotions of two parties of a dialogue are realized.

In accordance with the present disclosure, one or more computing devices and / or methods are provided. In an example, an emotional state of a user may be determined based upon one or more signals generated by one or more sensors and / or user activity of the user. A communication interface may be controlled based upon the emotional state.

An automated image processing method for assessing facially-expressed emotions of an individual, the facially-expressed emotions being caused by operation of a vehicle, machinery, or robot by the individual, including operating a vehicle, machinery, or robot by the individual and thereby expose a vision of the individual to a stimulus, detecting non-verbal communication from a physiognomical expression of the individual based on image data by a first computer algorithm, the image data of the physiognomical expression of the individual being caused in response to the stimulus, assigning features of the non-verbal communication to different types of emotions by a second computer algorithm, analyzing the different types of emotions to determine an emotional state of mind of the individual, and generating at least one of a prompt, an alert, or a change in a setting of an operational parameter of the vehicle, based on the emotional state of mind of the individual.

A method of transplanting a desired emotional state from a donor to a recipient, comprising determining an emotional state of the donor, recording neural correlates of the emotional state of the donor who is in the desired emotional state; analyzing neural correlates of the emotional state of the donor to decode at least one of a temporal and a spatial pattern corresponding to the desirable emotional state; converting said at least one of a temporal and a spatial pattern corresponding to the desirable emotional state into a neurostimulation pattern; storing the neurostimulation pattern in the nonvolatile memory; retrieving the neurostimulation pattern from the nonvolatile memory; stimulating the recipients brain with at least one stimulus modulated with the neurostimulation pattern to induce the desired emotional state in the recipient.

The invention discloses an artificial intelligence assistant method and system based on PAD emotion driving, and the method comprises the steps: collecting multi-modal data of a user, and carrying out the preprocessing of the multi-modal data; identifying the current emotion and communication intention of the user according to the preprocessed data; personality parameters and internal emotional states of the AI assistant are dynamically updated, and personification and consistency are kept in long-term interaction; long-term interactive memory of the AI is managed, and personality consistency is kept; generating a personalized recommendation and guide strategy for the user based on the personality and emotional state; generating an anthropomorphic natural language; verifying the key facts by using a knowledge graph; and performing state updating and guiding feedback. And the recommendation result not only is matched with the user behavior, but also better conforms to the evolution style and the guide target of the AI assistant, so that the acceptance and the exploration intention of the user to the recommendation are remarkably improved.

The invention discloses a customer service dialogue multi-modal dynamic emotion track detection method, system and device based on a large model and a medium, and belongs to the technical field of emotion track detection, and the method comprises the steps: collecting multi-modal original data in a customer service dialogue in real time; performing modal integrity analysis on the collected multi-modal original data, and identifying available modals and missing modals; extracting modal specific features based on the identified available modalities, and inputting the modal specific features into a pre-trained large language model; deducing the emotional state of the current dialogue fragment from the modal specific features by using a pre-trained large language model; according to the emotional states of the continuous dialogue segments, an emotional track is dynamically constructed, and the emotional track represents the change trend of emotions along with time; and outputting the emotion track to perform customer service quality control. According to the method, emotion recognition can still keep stable output under the condition of mode missing or signal degradation, and the reliability in a complex communication scene is remarkably improved.

The invention discloses a mine worker state recognition method and device, a medium and a product, and relates to the field of emotion recognition, and the method comprises the steps: carrying out the preprocessing of the multi-modal data of a mine worker, and determining the preprocessed multi-modal data; the multi-modal data comprises a physiological signal and a face image; performing feature extraction on the preprocessed multi-modal data, and determining a physiological feature set and a facial feature set; inputting the physiological feature set and the facial feature set into a multi-view Transform + fusion model, and determining an emotional state and a fatigue state of the mine operator; the emotional state comprises happiness, sadness, fear and neutrality; the fatigue states comprise normal, mild, moderate and severe states, and the method can reduce the state recognition error of the mine operation personnel and improve the recognition efficiency.

The invention discloses an emotional state recognition method, system and device, and relates to the technical field of biomedical signal processing and artificial intelligence emotional computation.The method comprises the steps that electroencephalogram signals of a to-be-detected individual are collected; extracting power spectral density (PSD) and differential entropy (DE) features of the electroencephalogram signals on each frequency band according to a preset frequency; performing structured combination on the PSD and DE features corresponding to each frequency band, and constructing a dual-channel spectrum feature matrix; inputting the dual-channel spectrum feature matrix into a pre-trained shared encoder, extracting emotional feature representation of the individual, and obtaining an emotional state recognition result according to the emotional feature representation; according to the method, the depression emotion state of a new individual can be evaluated without participation of target domain data, the robustness and recognition precision of the model under cross-individual and low-labeling conditions are effectively improved, and the method has relatively high popularization and application potential.

The invention discloses a behavior data analysis method and system based on intelligent perception. The method and system are used for improving the reliability of child behavior analysis. The method comprises the following steps: acquiring multi-dimensional monitoring data of a plurality of children in a monitoring environment, wherein the multi-dimensional monitoring data comprises visual information, auditory information and physiological information of each child; performing multi-source fusion on the visual information, the auditory information and the physiological information of each child to generate a behavior feature set; inputting the behavior feature set into a preset behavior and emotion classification model for classification to obtain a behavior mode and an emotion state of each child; on the basis of individual information of different children, the behavior modes and the emotional states in combination with scene information of the monitoring environment, knowledge graph construction on a time sequence is carried out, a children behavior graph is obtained, and the children behavior graph is used for displaying behavior events of the children in a preset time period.

The invention discloses an intelligent dialogue control method based on multi-mode emotion perception and a related device, and the method comprises the steps: enabling an intelligent outbound robot all-in-one machine to enter an intelligent dialogue monitoring mode, carrying out the dialogue monitoring processing of voice audio data of a target user in a current environment, and obtaining a dialogue monitoring result; entering a dialogue mode based on a dialogue monitoring result, and performing voice emotion recognition processing on the voice audio data to obtain voice emotion data; performing semantic analysis processing on the voice audio data to obtain semantic information data; based on the voice emotion data and the semantic information data, analyzing to obtain emotion state data; a response policy corresponding to the emotional state data is indexed, and response audio data is generated based on the response policy. In the embodiment of the invention, the emotional state data of the target user can be captured in time, and the response audio data is generated in time according to the emotional state data and the semantic information data for response, so that the target user is pacified in time.

The invention relates to the technical field of man-machine interaction, in particular to a service robot control method capable of self-adapting to human emotions, which comprises the following steps: acquiring an emotional state of a user in real time through a multi-mode emotion recognition model fusing an expression image, a voice signal and language content; based on the emotional state of the user, a dynamic behavior strategy mapping mechanism is adopted, and the voice intonation, the action speed and the service strategy of the robot are adjusted; and monitoring the response of the user to the adjusted service through a feedback module, and further optimizing an emotion recognition model and a behavior strategy mapping mechanism.

The invention discloses a music recommendation feedback method and system based on electroencephalogram emotion, a terminal and a medium, and relates to the technical field of electroencephalogram analysis, the method comprises the following steps: collecting electroencephalogram signals, preprocessing the electroencephalogram signals, and determining emotion state information based on the preprocessed electroencephalogram signals; music generation control parameters are determined based on the emotional state information, music content is synthesized based on the music generation control parameters, and the music content is recommended; feedback information of the music content is obtained, the adjusting effect of the music content on the emotional state information is determined, and on the basis of the feedback information, the step of determining the emotional state information on the basis of the preprocessed electroencephalogram signals and / or the step of determining music generation control parameters on the basis of the emotional state information are optimized. The emotion state is recognized through the electroencephalogram signals, the music content matched with the emotion state information is generated, and a closed-loop interaction adjustment mechanism between the emotion state of the brain of the user and music stimulation can be achieved.

The invention relates to the technical field of artificial intelligence, in particular to a personalized mental health assessment system based on artificial intelligence. According to the method, the multi-modal psychological characterization data is acquired to comprehensively describe the emotion and psychological characteristics of the user, the culture attribute characterization is acquired according to the word segmentation sequence and the voice expression data, and then the basic emotion expression vector is calibrated, so that the condition that the specific expression under a certain culture is misjudged as the general emotion characteristic is avoided, and the accuracy of the emotion expression is improved. According to the method, the problem of low emotion recognition accuracy caused by expression habit difference in a cross-culture scene is solved, then micro-expression features are analyzed through an optical flow algorithm, matching verification is carried out on the micro-expression features and emotion calibration vectors, association analysis is carried out on voice features and physiological signals, the problem of misjudgment of a traditional method on the emotional state with different words is solved, and the emotion recognition accuracy is improved. And finally, by analyzing the consistency of the emotion feedback vector and the physiological dominant representation vector, outputting comprehensive assessment data, thereby significantly improving the accuracy of mental health assessment.

The invention discloses a depressive emotion early screening and intervention system based on cross-modal heterogeneous information. The depressive emotion early screening and intervention system comprises a tester information registration module, a sensor test template, a resting state data acquisition module, a task state data acquisition module, a depressive emotion evaluation module, a digital intervention module, a report display module and a terminal control module. According to the method, scale, physiological data and behavioral signals are combined, and multiple scientific evaluation paradigms are constructed to perform multi-dimensional, objective and effective evaluation on the depressive emotion; the system further provides a personalized digital intervention scheme through an evaluation result, and the depressive emotion is preliminarily relieved. According to the invention, early screening and intervention of the depression emotion state can be realized, the detection efficiency is improved, and the method can be applied to various scenes such as schools, hospitals and families.