298 results about "Mood" patented technology

The invention relates to the technical field of intelligent assessment of psychological states, and discloses an intelligent dynamic assessment method for psychological states of teenagers based on multi-modal data. The method comprises the steps that multi-modal psychological data of a target assessment object is acquired, the multi-modal psychological data comprises voice emotion information and expression behavior relations, and dynamic features are extracted from the multi-modal psychological data; performing feature fusion processing on the psychological data by adopting a trained machine learning model to generate a multi-modal feature set containing a mapping relation between modal type identifiers and time sequence codes; based on the multi-modal feature set, performing time sequence correlation analysis on the dynamic features through a state analysis network to obtain a fusion result containing emotion dimension features and cognitive mode features; and inputting a fusion result into an evaluation model for dynamic state deduction processing, and outputting psychological state evaluation elements. According to the invention, comprehensive and dynamic assessment of the psychological state can be realized.

The invention provides an intelligent virtuality and reality combined psychological health service device based on digital elements, and aims to improve the accuracy and individuation level of psychological health management. The device firstly obtains the physiological indexes, behavior data, environmental factor data and social economic data of an individual, and carries out multi-modal fusion to form comprehensive feature data. Based on this, a psychological health environment factor model is constructed to quantify the influence of the external environment on the individual psychological state, and the calculation weight and prediction logic of the negative emotion large model are optimized. And the optimized negative emotion large model is used for identifying an individual emotion state, analyzing factors such as social environment, economic pressure and life events in combination with the mental health environment factor model, and generating an individual mental health assessment result. And according to an evaluation result, the virtual digital doctor provides intelligent pre-inquiry and other services. Through data-driven intelligent analysis and virtual-real combined intervention means, the accessibility, accuracy and intervention effect of psychological health services are improved.

The invention provides a multi-modal emotion recognition fusion method based on a multi-head attention mechanism, and the method comprises the steps: carrying out the feature extraction and fusion of various data, capturing the internal relation of a modal through the multi-head attention mechanism, achieving the information complementation between modals through cross-modal interaction, and dynamically adjusting the weight according to the quality of the modals. And a self-built database containing a large amount of Chinese data is constructed, and a culture adaptation optimization strategy is combined, so that the generalization performance and culture adaptability of the model in Chinese user groups are improved. The system is deployed on a cloud server, optimized hardware and software configuration is adopted, efficient model reasoning and multi-user concurrent processing are achieved, and the large-scale real-time application requirement is met. The emotion of the user can be monitored in real time and early warning can be provided in scenes such as psychological counseling and group emotion monitoring, professionals are assisted in better understanding the emotion state of the user, and the service effect is improved.

The invention relates to the technical field of psychological assessment, in particular to a psychological state assessment method, system and terminal based on an interactive terminal, and the method comprises the steps: separating voice data and text data of a user into an emotion change sequence; based on the emotion tendency of the emotion change sequence, setting an emotion change quantity, and according to a matching relationship between the emotion change quantity and a dialogue situation, calculating a user response duration; obtaining the participation degree of the user at different moments by obtaining the response frequency corresponding to the response duration of the user; combining the degree of participation, the emotional variation and the emotional change probability into an emotional feature, and deducing an emotional classification and a transition label of the current emotional feature; on the basis of the emotion proportion of each emotion classification, dialogue strategy matching is carried out according to the participation amount of the emotion classification at adjacent moments, and a dialogue strategy is set; and the accuracy and effect of emotion recognition are improved.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The invention discloses a prompt-driven two-stage multi-modal emotion representation learning method. The method comprises the following steps: respectively collecting original video data from a plurality of public multi-modal emotion analysis data sets; preprocessing and feature extraction are carried out to obtain vectorized multi-modal feature representations of vision, audio and texts, and multi-source emotion clues are obtained; in a training stage, a prompt-driven two-stage multi-modal emotion representation learning model is constructed, inter-class separability is enhanced and intra-class intensity features are reserved through an emotion anchor point comparison and alignment stage, including prompt-based emotion anchor point learning and comparison and alignment between joint representation and emotion anchor points; capturing the dynamic change of emotion expression through an emotion intensity offset estimation stage; in the reasoning stage, emotion category prediction and final emotion state prediction are carried out. According to the method, shared emotion features among multiple modes can be stably captured, interference caused by individual differences is effectively suppressed, and the accuracy and robustness of emotion prediction are remarkably improved.

The invention provides an emotion recognition method and device, computer equipment and a storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining multi-modal data of a pet; performing feature extraction on the multi-modal data to obtain a feature vector of each modal; determining relevancy among the modals by using the feature vectors, and performing feature aggregation on the feature vectors by using a pre-trained graph neural network and the relevancy to obtain enhanced feature vectors of the modals; and performing attention fusion on each enhanced feature vector to obtain a fusion feature vector, and performing emotion recognition according to the fusion feature vector to obtain a target emotion type of the pet. Therefore, the relevancy between the modals can be determined by utilizing the feature vectors of the modals extracted from the multi-modal data, and feature enhancement is performed on the feature vectors of the modals according to the relevancy, so that the relevancy between the pet multi-modal data is fully mined, and the accuracy of pet emotion recognition is improved.

The invention discloses a depressive emotion intelligent identification and intervention system. The system comprises a data acquisition module used for acquiring original data of a user in real time; the data preprocessing module is used for performing layered preprocessing on original data; the feature extraction module is used for extracting each modal feature; the multi-modal fusion module realizes multi-modal dynamic fusion based on a bidirectional long short-term memory network and a cross attention mechanism, and supports multi-task output of depression dichotomy, PHQ-9 scale regression prediction and the like through a full-connection network; the user interaction module is used for providing task guidance, data acquisition control and intervention suggestion visualization for a user; the system integrates a self-developed VR (Virtual Reality) mind game and an AI (Artificial Intelligence) psychological big model, and provides intervention tools such as mind training, dialogue intervention and PC (Personal Computer) terminal severe games for different depression levels. The data management module is used for storing user information, original data, a depression emotion recognition process and evaluation result data, and a feasible technical means is provided for early recognition and auxiliary evaluation of depression risks.

The invention discloses an intelligent music recommendation method based on emotional perception and acoustic features, and relates to the technical field of intelligent recommendation systems and emotional computation.The method comprises the steps that physiological signals, music acoustic features and historical behavior data of a user are collected; preprocessing the multi-source features and mapping the multi-source features to the same dimension to construct a fusion matrix; building a double-branch deep learning model, fusing features through an attention mechanism and training parameters; an evolutionary algorithm is adopted to optimize hyper-parameter screening optimal combination; generating a recommendation list matched with the real-time emotion and preference; and continuously collecting user interaction data, and regularly and incrementally training the dynamic update model. According to the method, emotion and behavior dual-drive recommendation is achieved by fusing physiological signals and acoustic features, emotion perception is accurate, recommended content fits the real-time mood, model optimization is efficient, recommendation precision and diversity are remarkably improved, and the music consumption experience of a user is greatly improved.

The invention discloses an emotion recognition method, system and device for child psychological health and a storage medium, and relates to the technical field of psychological health assessment. Obtaining dialogue data between the child and the human-computer interaction interface, and performing natural language and acoustic feature processing on the dialogue data to obtain a current comprehensive emotion value; if the current comprehensive emotion value is smaller than the negative emotion threshold value, marking a time point corresponding to the voice information as an emotion starting moment; acquiring emotional dialogue text information according to the emotional starting moment; identifying the emotional dialogue text information, and calculating a first attribution score and a second attribution score associated with each candidate emotional reason; calculating the first attribution score and the second attribution score to obtain a final attribution score; and determining a core emotion reason according to the final attribution score, and generating an intervention measure based on the current comprehensive emotion value and the core emotion reason so as to intervene the child according to the intervention measure. By implementing the provided technical scheme, the accuracy of children mental health intervention is improved.

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process interactive communications between at least two participants. Speech and text, within the interactive communications, are analyzed using machine learning classifiers to extract prosodic, semantic and key phrase cues located within the interactive communications to identify changes to emotion, sentiments and key phrases. A summary of the interactive communications between a first participant and a second participant is generated at least, in-part, based on the extracted prosodic, semantic and key phrase cues and the summary is highlighted based on any of the changes to emotion, the sentiments or the key phrases.

A method of dynamically monitoring emotions of a user using a wearable device. The method includes detecting one or more physiological signals associated with a user and determining, in a first layer of the method, one or more detected emotions associated with the one or more physiological signals detected. The method also includes determining, in a second layer of the method, one or more symptoms of the user, whereby the one or more symptoms are based on the one or more detected emotions. The method further includes determining, in a third layer of the method, one or more mental wellbeing metrics associated with a mental wellbeing of the user, whereby the one or more mental wellbeing metrics are based on the one or more detected emotions and the one or more symptoms.

The invention belongs to the field of artificial intelligence, and particularly relates to a voice interactive intelligent terminal emotion adaptation system for teenager users. According to the system, accurate recognition, dynamic modeling and active guidance of adolescent emotions are realized through five units, namely, multi-modal emotional feature perception, developmental stage psychological modeling, nonlinear emotional evolution prediction, adaptive interaction strategy decision and feedback closed-loop regulation. According to the system, a development psychological theory is fused, acoustic, semantic and non-verbal features are combined, a personalized emotion interaction model is constructed, real-time and low-delay flexible emotion response is completed on the premise that privacy is guaranteed, and the affinity of human-computer interaction and the mental health supporting capacity are effectively improved.

This invention provides a method for generating empathic dialogues by integrating knowledge and emotional cause context graphs, belonging to the field of information processing technology. The invention proposes an empathic dialogue generation model integrating knowledge and emotional cause context graphs. This model includes six modules: discourse encoding, a knowledge-integrated dialogue context graph, a knowledge-enhanced KDCG network, emotion classification, emotion cause identification, and response generation. By introducing emotional cause context graphs, it effectively solves the problem of integrating contextual information in multi-turn dialogues. By incorporating background knowledge from a knowledge base combined with emotional cause context graphs, it enhances the system's ability to understand implicit emotions and intentions, ultimately comprehensively improving the dialogue system's depth of understanding of user emotions. Through the joint modeling of emotions and causes, the system can more clearly understand the source of the user's emotions, thereby generating more targeted and humanized empathic responses.

The invention particularly relates to a method for constructing a Sigma factor atlas structure driven by emotion clustering. According to the emotion clustering driven Sigma factor atlas structure construction method, semantic aggregation is performed on unstructured data in a knowledge base through emotion recognition and topic clustering, a dominant intention and a subtask chain are extracted, graph structure representation is formed, and finally a factor atlas capable of being used for decision reasoning grows. According to the emotion clustering-driven Sigma factor graph structure construction method, emotion information and memory management are innovatively combined, a brand new memory utilization normal form is provided for an artificial intelligence system, the structure organizability and context association degree of a knowledge graph are improved, the automation level is high, and the method is suitable for popularization and application. The method is suitable for multi-scene intelligent analysis tasks such as multi-mode RAG retrieval and fault root cause analysis.

The invention relates to a psychological counseling method and device based on an intelligent dialogue system, equipment and a medium. The method comprises the following steps: extracting a language structure feature, an expression frequency feature and a text auxiliary feature of a dialogue text, and carrying out coding processing on the language structure feature and the expression frequency feature by utilizing a pre-constructed Transform model to generate an emotion vector set, carrying out weighted fusion on the emotion vector set by adopting an attention mechanism based on text auxiliary features to obtain a mentality representation sequence; and performing time sequence correlation analysis on the psychological representation sequence in combination with a preset psychological representation set to generate a psychological evolution path, matching a corresponding dialogue mode rule for the psychological evolution path by utilizing a preset dialogue mode rule library, and generating a response text by adopting a pre-trained natural language generation model based on the dialogue mode rule. By adopting the method, intelligent and accurate psychological counseling response can be realized, the dynamic change of the mentality of the user can be effectively identified and adapted, and the individuation and adaptability of interaction can be improved.

The utility model provides an auxiliary emotion decompression device, which relates to the technical field of psychological counseling, and comprises a rubber main body, the front end face of the rubber main body is provided with an insertion holding groove penetrating through the rear end face of the rubber main body, and the rubber main body is internally provided with a hollow cavity right below the insertion holding groove; the two rubber abutting blocks are installed in the hollow cavity, the pressure sensor is installed between the two rubber abutting blocks, and through abutting cooperation of the pressure sensor and the rubber abutting blocks, when a patient has an emotional fluctuation reaction and the hand of the patient unconsciously makes a fist, the rubber main body is tightly clenched, and meanwhile the rubber main body is prevented from being damaged. The rubber pressing block is synchronously matched to press the pressure sensor, so that the generated pressure data is wirelessly transmitted to a computer for display, a psychologist can intuitively know the emotional fluctuation degree of the patient according to the pressure data, and the problem that the emotional fluctuation reaction of the patient is difficult to digitalize when the emotional pressure is relieved through psychological guidance at present is solved.

An object of a system according to an embodiment is to detect an emotion of a user and provide appropriate advice on the basis of the detected emotion.SOLUTION: A system according to an embodiment includes a smart emotion detection speaker, an emotion detection AI, and a mentoring generation AI. The smart speaker detects emotions from the user's voice and speech. The emotion detection AI generates words based on emotions. The mentoring generation AI listens to the user's distress and provides advice.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to improve the user's mental health by analyzing the user's emotions and responding to them individually. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's emotions. The provision unit provides advice based on the emotions analyzed by the analysis unit. The transmission unit sends messages to increase motivation. The optimization unit optimizes the user's schedule.

An object of a system according to an embodiment is to understand a user's emotion and situation in real time and provide appropriate advice and support.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires the emotion or situation of the user in real time. The analysis unit analyzes the information acquired by the collection unit and understands the emotion or situation of the user. The provision unit provides advice or support to the user on the basis of the information understood by the analysis unit.SELECTED DRAWING: Figure 1

The invention discloses a mediation strategy dynamic adjustment system based on multi-modal emotion perception. The core of the system comprises six modules: obtaining text, voice and video data through a multi-modal data acquisition module; high-accuracy emotion perception is realized through an emotion recognition and fusion analysis module; key emotional events are captured through an emotional event detection and early warning module; strategies such as verbal skills and rhythms are intelligently recommended / adjusted by means of a mediation strategy dynamic adjustment module in combination with a knowledge base; a visual interface and a feedback channel are provided through the user interaction and feedback module; and compliance is guaranteed by a data security and privacy protection module. The system can realize real-time accurate sensing of multi-modal emotions, dynamically adapt to a mediation strategy, reduce experience dependence of mediators, and support online and offline multi-mediation modes. The trial application shows that the multi-modal emotion recognition accuracy rate reaches 92%, the strategy suggestion adoption rate reaches 87%, the mediation success rate is increased by 15%, mediation intelligence and standardization can be enhanced, and the method has wide popularization prospects and social benefits.

In a system and method for enabling a user to identify the emotions of speakers during a telephone or online conversation, spoken audio input is pre-processed using a one-dimensional Mel Spectrogram and / or a two-dimensional Mel-Frequency Cepstral Coefficient (MFCC) matrix, reducing the two-dimensional matrix to a single dimension output, and identifying at least one emotion in the audio input using a convolutional or recurrent neural network.

We provide the system. [Solution] A natural language processing method that automatically analyzes customer inquiries, A response generation means that generates an appropriate automatic response based on the content analyzed by the analysis means, A sentiment analysis tool for identifying emotions from the aforementioned customer inquiry, A tone adjustment means that adjusts the tone of the response generated by the response generation means in accordance with the emotion identified by the emotion analysis means, A system that includes this.

An object of the system according to the embodiment is to analyze the content and emotion of a conversation and cause a AI character to interrupt at an appropriate timing.SOLUTION: A system according to an embodiment includes an analysis unit, an emotion analysis unit, a learning unit, and a control unit. The analysis unit analyzes the content of the conversation in real time. The emotion analysis unit analyzes an emotion on the basis of the content analyzed by the analysis unit. The learning unit learns the reaction of the user on the basis of the analysis result obtained by the emotion analysis unit. The control unit controls the interruption of the AI character based on the information learned by the learning unit.SELECTED DRAWING: Figure 1

The invention discloses an AI-based adolescent psychological counseling method and system, and particularly relates to the technical field of psychological risk assessment. The method comprises the following steps: collecting cross-modal synchronous data generated by teenagers in a psychological counseling process, extracting emotional characteristics, and identifying contradictory emotion signals; performing cross-modal association path tracking by using the contradictory emotion signal, determining a propagation relationship of the contradictory emotion signal between emotion features, executing psychological camouflage association analysis, and separating camouflage emotion features and non-camouflage emotion features caused by self-camouflage behaviors of teenagers; performing difference aggregation on the camouflage emotion features and the non-camouflage emotion features to form cross-modal psychological contradiction signal decoupling data; the teenager psychological risk assessment is performed by using the cross-modal psychological contradiction signal decoupling data, the risk assessment result of the teenager real psychological state is generated, and the accuracy and reliability of teenager psychological risk identification and intervention can be improved.

The invention discloses a sound-writing-psychology three-in-one treatment and healing method, which comprises the following steps: a preparation stage before treatment and healing: a1, a target consensus step: a guider and a participant communicate to clarify physical and psychological trouble appeals, and jointly formulate a treatment and healing target; a2, a base line evaluation step: guiding the participants to complete emotion state scoring, uncomfortable body part marking and recording of scenes and psychological factors causing body reaction. According to the sound-writing-psychology three-in-one treatment and healing system and method provided by the embodiment of the invention, professional and normative occupational identities are provided for physical and psychological recuperator practitioners, and the standardization level and professional dignity of services are effectively improved. The positioning emphasizes the fact that the health management initiative is handed back to an individual, the public is guided to change from passive help seeking to active health management, a feasible scheme is provided for connecting professional health service and daily self-recuperation through a structured intervention path, and innovative development of a health service mode is promoted.

The invention discloses a multi-source data fusion and self-adaptive threshold-based user emotional state judgment method and system, and belongs to the technical field of artificial intelligence. Four types of main modal features of a user are collected and extracted, and a fused emotional feature is obtained according to the four types of main modal features; time recursion updating is introduced to update the fused emotional features into time-based fused emotional features; dynamically correcting a self-adaptive threshold value of the system according to the global fluctuation and the feedback deviation; determining a weighted risk ratio and introducing a state smoothing item; according to the maximum probability criterion, when self-adaptive feedback adjustment is triggered, deviation is fed back; and judging the emotional state of the user to obtain the judged emotional state of the user. According to the method, the overall robustness and the intelligent level of the system are remarkably improved.

An object of a system according to an embodiment is to accurately grasp thoughts and feelings of a person to be cared for and a person to be medically treated and to appropriately perform communication.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires brain waves of a person to be cared for or a person to be treated. The analysis unit analyzes the brainwave data acquired by the acquisition unit and analyzes an emotion. The generation unit generates a linguistic expression based on the emotion data analyzed by the analysis unit. The display unit displays the linguistic expression generated by the generation unit on a monitor.SELECTED DRAWING: Figure 1

The application discloses a kind of self-help evaluation and individualized healing push of love loss mood, it is related to artificial intelligence technical field, which comprises the following steps: collecting multidimensional psychological evaluation source data, constructing user communication image, user behavior image and user psychological image based on multidimensional psychological evaluation source data, and inputting the comprehensive evaluation model constructed in advance, obtaining the user comprehensive risk score, and dividing the user state level and the user current healing stage based on the user comprehensive risk score;Based on the user state level and the user current healing stage, match the basic healing scheme from the preset healing scheme template library based on the healing scheme generation algorithm, fine-tune the basic healing scheme in combination with the recent behavior preference of user, to obtain the individualized healing plan;The individualized healing plan is pushed to user end, which helps to solve the problem that the prior art cannot realize accurate self-help evaluation of love loss mood and push individualized healing scheme.

The application belongs to the technical field of biomedical signal processing and computer vision, and specifically discloses a child emotion analysis method and system based on eye movement and micro-expression association, which comprises the following steps: synchronously acquiring an eye movement data stream and a facial video stream, triggering a dynamic analysis process by identifying a mode conflict between high physiological arousal and low emotional expression intensity after generating a physiological arousal and emotion hypothesis through preliminary analysis, generating a guide strategy according to a conflict degree, and enhancing original data in a time domain or a space domain to facilitate deep analysis, capturing suppressed micro-expression signals in the enhanced data to correct the preliminary emotion hypothesis, and finally fusing a physiological arousal level, a corrected emotion category and a quantified expression inhibition degree to output a multi-dimensional psychological state analysis result containing an emotion category, a physiological arousal level and an expression inhibition degree, so as to realize quantitative and multi-dimensional evaluation of real emotions of children.