566 results about "Mood state" patented technology

The invention discloses a comprehensive regulation and control method and system based on multi-mode electromagnetic and ultrasonic stimulation, and the method comprises the steps: collecting brain wave signals in real time through a multi-electrode EEG system, synchronously collecting skin electroreaction and heart rate variability signals, preprocessing the collected signals, and carrying out the feature extraction; the extracted signal features are subjected to multi-modal feature fusion through a CNN, and the multi-modal features are subjected to time sequence dependency analysis through a long-short-term memory network to generate a real-time emotional state quantization vector and identify a current emotional state category. Through synchronous acquisition and feature fusion of multi-electrode EEG, EDA and HRV signals, a real-time brain function connection network is constructed, accurate recognition and stimulation strategy making are performed on emotion regulation and control nodes based on graph theory analysis, the accuracy and reliability of emotion recognition are improved, personalized regulation and control of individual emotion states are realized, and the emotion regulation and control efficiency is improved. The cognitive function and language expression ability of an individual can be improved, and movement coordination disorder caused by emotional fluctuation is reduced.

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

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 real-time emotion recognition, regulation and control method and device combined with a brain-computer interface. The method comprises the following steps that S1, electroencephalogram signals of a user are collected in real time through an electroencephalogram sensor; s2, preprocessing the original electroencephalogram data; s3, processing the electroencephalogram signals in the S2 by adopting a time-frequency analysis technology, and extracting time-frequency characteristics related to the emotional state; s4, inputting the extracted time-frequency features into a trained long-short-term memory network model, performing time sequence analysis, and identifying the emotional state of the user; s5, according to the emotion state recognized in the step S4, combining historical emotion data and individual features of the user, and dynamically adjusting an emotion regulation strategy; and S6, comprehensively evaluating the emotional state of the user through a multi-modal data fusion technology. By combining the advanced time-frequency analysis technology, the long-short term memory network model and the multi-modal data fusion technology, accurate recognition and dynamic regulation and control of the emotional state of the user are achieved.

The invention discloses an AI digital human interaction system based on emotion recognition, relates to the technical field of emotion calculation, and improves the accuracy of emotion recognition through multi-modal emotion data acquisition and processing of an emotion primary recognition module and a secondary recognition module. The system effectively distinguishes complex emotions, such as fatigue and depression, of the trainee in the virtual education task by calculating the first emotion coefficient and the second emotion coefficient; secondary emotion recognition is carried out, so that misjudgment is reduced, and the emotion state recognition accuracy is ensured; through an emotion feedback module, the system dynamically adjusts the expression, intonation and interaction frequency of the AI digital human according to a second emotion coefficient, so that the emotion feedback is more natural, and the emotion recognition sense of the student is enhanced; besides, the culture adaptation analysis module collects cross-culture emotion data based on the culture background of the trainee, calculates and evaluates a culture adaptation coefficient, and automatically adjusts an emotion recognition and feedback strategy, thereby improving the adaptability of the system in a multi-culture environment and the accuracy of emotion expression.

A system for generating highly personalized image content with generative artificial intelligence (AI) that incorporates brainwave data collected from a Brain-Computer Interface (BCI) device worn by the user while they are immersed in their experiences. The system can identify a mood (i.e., cognitive, mental, and emotional states) felt by the user during that time. The system develops a prompt that describes the target scene and modulates the weights assigned to each parameter in the prompt based on the mood identified. The generative AI engine can then to apply the mood to the target scene by adjusting image attributes such as hue, saturation, and lighting. The outputted image can offer a visual representation not only of the objects that were in the user's environment, but also the user's general perception and experience of the occasion. The process is passive and requires no interaction from the user to define their emotional affect.

The invention discloses a student sentiment analysis method and system based on a multi-modal dynamic memory large model, and belongs to the field of multi-modal sentiment analysis. The method comprises the following steps: firstly, acquiring facial expression images and corresponding real emotional state category labels when different students watch an online education course to form a training data set, training a multi-modal dynamic memory large model based on the data set, and in the training process, performing prompt fine tuning on an image encoder and a text encoder so as to obtain a multi-modal dynamic memory large model; visual aggregation representation generated in the historical training process is stored through a dynamic feature space, and model parameters are optimized through cross entropy loss; and finally, inputting a facial expression image to be classified into the trained multi-modal dynamic memory large model, and outputting an emotional state category prediction result of the facial expression image. The method can be effectively applied to the field of education, and is beneficial for teachers to know the emotional state of students, thereby improving the participation degree and learning effect of the students.

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-based emotional state evaluation method, which comprises the following steps: S1, collecting multi-modal data of a user to be tested, the multi-modal data comprising voice data, facial micro-expression data and facial heart rate data; s2, constructing a multi-modal deep neural network, inputting the multi-modal data into the multi-modal deep neural network, and dynamically adjusting the weight of the modal data by the multi-modal deep neural network by using an attention mechanism so as to output and obtain fusion features; and S3, judging the emotional state of the to-be-detected user based on the fused features. Accordingly, the invention further discloses a multi-mode-based emotional state evaluation system, and the multi-mode-based emotional state evaluation system can be used for implementing the emotional state evaluation method, so that the physiological status of the patient is more accurately and comprehensively considered, the emotional state of the patient is evaluated, and the accuracy of the emotional state evaluation is improved. Therefore, professional psychologists are assisted in diagnosing depression.

The invention discloses a pet pacifying method, and relates to the technical field of emotion recognition, and the pet pacifying method comprises the following steps: obtaining pet behavior data, and inputting the pet behavior data into a pet emotion recognition model to recognize the current emotion type of a pet; according to the current emotion type of the pet, determining a pacifying strategy corresponding to the current emotion type; and pacifying the pet based on the pacifying strategy. According to the method, the emotion of the pet is recognized from multiple dimensions, the accuracy of pet emotion recognition is improved, whether the pet is in an anger, happy or calm state is judged, corresponding strategies are selected for pacifying according to different emotional states of the pet, and the anxiety, tension and other emotions of the pet can be relieved more effectively.

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.

A stuffed, interactive children's toy for assisting individuals in self-calming and / or self-regulating exercises. The toy may include one or more weights, a microphone, a motor, one or more light emitting diodes (LEDs), and / or one or more speakers. The toy optionally further includes one or more buttons corresponding with one or more emotional states. For example, a red button, a yellow button, and / or a green button. Upon pressing the button, the toy may deliver either a random and / or a preprogrammed intervention. The toy may be used in combination with a children's book. In another example, the toy may be in communication with a mobile application (app) via a Bluetooth connection. In yet another example, the toy may be in communication with a website and / or a remote-control device. The app may be used to record emotional data, physical date, and / or to assign preprogrammed stimuli to the one or more buttons.

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.

A method including receiving an input from a user for an interaction application that is executing. The method including receiving a first plurality of multimodal cues from a plurality of trackers, wherein each of the plurality of trackers is configured to track a corresponding cue using artificial intelligence. The method including providing the first plurality of multimodal cues to an artificial intelligence (AI) model configured to classify one or more emotions of a user, wherein the AI model generates a first predicted emotion based on the first plurality of multimodal cues. The method including determining a modification to content generated by the interaction application in response to the input, wherein the modification to the content is determined based on the predicted emotion. The method including providing the modification to the interaction application, wherein the interaction application is configured to generate modified content based on the modification.

The invention discloses a personalized emotion automatic detection method based on a patient before and after an operation, and the method specifically comprises the following steps: 1, carrying out the preprocessing of a patient expression video; 2, discovering change of a human face action unit related to the emotion AU intensity difference; 3, humanized threshold value selection based on the emotion and human face action unit relation; after non-positive face frames of the video in the step (1) are removed, OpenFace mature toolkit software is used for intensity prediction and existence detection of a face action unit; and 4, judging a personalized emotion evaluation application scene through a threshold value of the AU. According to the method, data support such as an accurate emotion change trend chart is provided for clinical emotion management and operation recovery monitoring, meanwhile, the emotion state of a patient is scientifically and quantitatively displayed for a doctor, the doctor is assisted in performing subsequent decision diagnosis, and the treatment effect is assisted to be 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 an EEG feedback optimization method and system for building appearance visual features and emotion regulation, and the method comprises the steps: constructing a building appearance 3D model matched with the building features of a target region, and carrying out the fine processing, and obtaining a 3D model library of a plurality of visual stimulation sources; constructing a virtual scene comprising a building appearance 3D model and at least one user, and configuring the view angle and layout of the corresponding user in the virtual scene according to the action, implementation and behavior mode of the participant in the real environment; obtaining EEG signals of participants and extracting emotional characteristic indexes; based on the predicted emotional state of the participant under the current building appearance, the influence of the building appearance on the emotional pressure and the positive emotion is analyzed; and on the basis of the current building appearance 3D model parameters, predicting an emotion regulation effect corresponding to the parameters so as to minimize the difference between the predicted emotion regulation effect and a preset target, and iterating the parameters of the building appearance 3D model.

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 subject of the invention is the recognition of a user's emotional state and supporting their mood, particularly when experiencing loneliness. A specific goal is to help the user obtain sufficient interaction with relatives and friends when their mood is slipping into a concerning area. The objective is also that the connection is automatically opened to exactly the right person who can best help the user return to a safe and healthy mood and mental state. As an alternative to making contact, for example, when a suitable contact person is not reachable, providing the user with music, games, podcasts, videos, or other mood interventions appropriate to the emotional state is also targeted. These objectives are achieved through a system built into a smartwatch (101) and smartphone (121) that measures, recognizes, and monitors the user's vitality (107) and emotional state (127) throughout the day, and opens a communication link (140) or a mood intervention suitable for the situation when set conditions are met.

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.