25 results about "Emotional Changes" patented technology

The invention provides a system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The invention relates to the technical field of service robots, and discloses an elderly care service robot based on an emotion adjustment system, and the robot comprises a multi-mode emotion recognition module which is used for carrying out the comprehensive perception of the emotional state of the elderly through combining various emotion signals of voice, facial expression and limb movement; the personalized emotion adjusting module is used for generating personalized emotion responses meeting the requirements of the old people; the emotion memory module is used for recording emotion changes, important life events and personal preferences of the elderly; the scene self-adaption module is used for sensing the living environment of the old people in real time and adjusting the emotional expression mode of the robot according to the environment information; the health intervention module is used for generating a health management scheme through long-term monitoring of the emotional state and health of the old people; and a safety monitoring module. According to the invention, the emotion state of the elderly is analyzed through the multi-modal fusion network, the emotion misjudgment probability is reduced, and the man-machine interaction experience and the emotion support effect of the elderly are remarkably improved.

The invention discloses a generative AI-driven physiological-music following response intelligent emotion regulation system. The system mainly comprises a physiological signal acquisition module, a signal preprocessing module, an emotion real-time decoding module, a music-emotion response calibration module and a music generation and emotion regulation module. On the basis of the generative AI technology, music expectation, a music tension release mode and other music psychology theories are fused, music capable of directionally adjusting the emotional state of the user is generated through real-time accurate decoding of the emotional state of the user through multi-mode physiological signals, the user expectation is gradually established and released at the high point, roller coaster-like emotional changes of the user are caused, and the user experience is improved. And the user can subjectively obtain pleasant and satisfied positive emotions and the like.

The invention discloses an AI psychological counseling method and system for generating an emotional map and a differentiation report, and relates to the technical field of artificial intelligence and psychology crossing. The invention discloses an AI psychological counseling method and system for generating an emotional map and a differentiated report. Through interaction of voice and characters, a visual emotional map is generated, personalized action suggestions are generated, and a visitor report or a psychological counselor report is generated. According to the method, a visual emotional map is generated through voice and character analysis, changes in the emotional map can be seen more visually, meanwhile, a proper psychological AI model is adapted, personalized action suggestions and corresponding related reports are generated, fine emotional fluctuations can be effectively captured through the accurate voice and character capturing capacity, and therefore the emotional effect is improved. A multi-dimensional emotion analysis model is arranged in the system, emotion fluctuation is monitored in real time, a detailed emotion change trend chart is generated, and the scientific basis is provided for psychological counseling.

This invention relates to the field of EEG emotion recognition technology, specifically to an emotion recognition method and system based on two-level KL divergence. The method includes: acquiring EEG data and calculating the first KL divergence between the left and right hemispheres in each region before and after external sensory stimulation, at each set frequency band; averaging the first KL divergence of each segment in each hemisphere as the feature before and after external sensory stimulation; calculating the second KL divergence based on this feature; and concatenating the feature before and after external sensory stimulation with the second KL divergence as input to a classification model. This invention deeply mines the emotionally sensitive features in EEG signals through two-level progressive distribution difference calculation and combines it with a classification network to achieve model training and accurate classification, thereby achieving precise recognition of subtle emotional changes.

This invention discloses an emotional intervention system integrating multi-sensory feedback, relating to the field of emotional intervention technology, and includes the following steps: S1, scene library construction; S2, scene-based preference collection; S3, personalized intervention scene generation; S4, real-time dynamic adjustment. This invention realizes the transformation of intervention content from standardized output to personalized customization. Based on a preference adaptation mechanism of children's autonomous feedback, it can accurately capture each child's unique interests, making the intervention content more aligned with their cognitive habits and emotional needs. This helps to stimulate children's active participation in the intervention process, reducing resistance. Furthermore, it achieves dynamic adaptation between the intervention content and the child's real-time state, enabling timely responses to fluctuations in children's attention and emotional changes during the intervention process. This avoids guidance failure due to rigid content, ensuring the intervention content remains attractive to children, thereby helping to maintain their focused attention.

Systems and methods for estimating emotional states, moods, affects of an individual and providing feedback to the individual or others are disclosed. Systems and methods that provide real time detection and monitoring of physical aspects of an individual and / or aspects of the individual's activity and means of estimating that person's emotional state or affect and change to those are also disclosed. Real time feedback to the individual about the person's emotional change, change or potential change is provided to the user, helping the user cope, adjust or appropriately act on their emotions.

The invention relates to the technical field of natural language processing, and discloses a common-situation reply generation model training method and device based on knowledge distillation. The method comprises the steps that a first sample sequence is input into a teacher model for reply generation, and a first reply sequence is obtained; inputting the emotional common sense sequence into an emotional encoder, and inputting the cognitive common sense sequence into a cognitive encoder to obtain an emotional global representation vector and a cognitive global representation vector; inputting the fusion representation vector into a decoder of the student model for reply generation to obtain a second reply sequence; performing emotion classification prediction to obtain emotion classification probability distribution; and carrying out joint training on the student model and the teacher model to obtain a common-situation reply generation model. According to the scheme, emotional changes and potential intentions of the user can be accurately captured, and personalized and diversified replies are generated in combination with a knowledge distillation technology and external common knowledge; dialogue historical information can be comprehensively and deeply analyzed, and it is ensured that generated replies are closely related to contexts, and logic coherence is achieved.

The invention discloses a student-oriented psychological counseling method based on a large model. The method comprises the following steps: S1, acquiring emotion expression text data; s2, establishing an emotional state feature vector based on the emotional intensity; s3, collecting emotional state feature vectors for multiple times in a preset time window; s4, generating an emotion buffering strategy based on a short-term emotion fluctuation monitoring result; s5, guiding the emotional state; s6, storing the emotional state feature data after the emotional state feature data is relieved into an emotional file of the student; s7, based on emotional state feedback data, adopting an adaptive learning algorithm to finely adjust the large model, optimizing the analysis of an emotional fluctuation state and the generation ability of an emotional buffering strategy, and gradually adjusting and optimizing an emotional buffering method; and S8, based on the emotional state feedback data and the emotional change trend of the student, repeatedly executing the steps S1-S7 to form an emotional buffer closed-loop feedback system. According to the invention, a more efficient and more intelligent solution can be provided for student psychological health management.

The application discloses an emotional risk identification method and platform combined with public emotional behavior spectrum, and the method comprises the following steps: extracting behavior features, text features and sound features in historical user interaction sequences, and splicing to obtain multi-source splicing features; based on the multi-source splicing features, outputting public emotional behavior spectrum through a public emotional behavior spectrum identification model and outputting personal emotional behavior spectrum through a personal emotional behavior spectrum identification model; splicing the public emotional behavior spectrum and the personal emotional behavior spectrum for training an emotional risk identification model; obtaining current real-time user complaint data, inputting the emotional risk identification model, and outputting an emotional risk level. By adopting the technical scheme, individual and group emotional behavior spectrum modeling can not only depict the fine-grained emotional evolution path of individuals, but also capture the group emotional aggregation and diffusion process, so that accurate risk grading early warning of personal emotional behavior can be realized in the background of public emotional change trend.

The invention discloses an intelligent interactive toy based on emotion recognition and management and an emotion monitoring method thereof, and relates to the technical field of intelligent toys, the intelligent interactive toy comprises a toy body, and further comprises a built-in sensor assembly used for capturing facial expressions, voices and intonations and body languages of children; the emotion recognition module is connected to the sensor assembly and used for analyzing the captured information to recognize the emotion state of the child; the interactive feedback module is used for conducting emotion guidance and management on the children through story telling, music playing or interactive games according to the recognized emotion states, and feedback content can be customized in a personalized mode according to the ages, genders and interests of the children; and the application program interface is used for communicating with an external application program, so that parents can monitor the emotion change of children and the interaction effect with the toy, and the intelligent interaction toy scheme based on emotion recognition and management provided by the invention improves the accuracy and reliability of emotion recognition, and also provides a personalized emotion guide management function.

A system is provided.SOLUTION: A system comprising: means for collecting biometric data by a wearable device; means for determining an emotional change detected by the wearable device; means for recording and summarizing audio associated with the emotional change; means for automatically generating a diary based on the summarized audio data; and means for notifying a user of the diary.SELECTED DRAWING: Figure 1

This invention provides a human-computer interaction method, device, electronic device, and medium, belonging to the field of artificial intelligence technology. It acquires current user voice data from AI dialogue, and inputs the extracted current user emotional features and user personality features extracted from the converted text data into preset emotion recognition models and preset personality recognition models, respectively, to obtain current user emotion type probability data and user personality type probability data. These data are then fused to obtain an emotion-personality fusion type and a fusion type probability. The emotion-personality fusion type with the highest probability value is used as the result of judging the user's current emotion, and the most suitable emotion guidance scheme is obtained based on this result. By incorporating individual user personality differences into the emotion analysis process, the judgment of the user's current emotional state is more accurate, and corresponding emotional guidance can be provided promptly and accurately according to the user's individual emotional changes, thus improving the user experience.

The invention discloses a speaker recognition method based on emotion transfer learning, which combines emotion transfer learning and an attention mechanism. Firstly, high-level feature representation in an emotional voice signal is extracted through a pre-trained deep neural network model; secondly, optimizing model parameters by utilizing an emotion transfer learning algorithm to adapt to the influence of emotion change on voice features; then, an attention mechanism is introduced to carry out weighting processing on the feature vectors, and feature information useful for speaker identity recognition is highlighted; the method has higher speaker recognition accuracy and robustness in an emotional voice environment, is suitable for the fields of identity verification, security monitoring and the like, and provides more reliable technical support for related fields.

The invention relates to the technical field of digital education, and discloses an intelligent digital ideological and political teaching monitoring system which comprises a base, supporting frames are fixedly connected to the two sides of the top of the base, a writing board is fixedly connected between the two supporting frames, and a display screen is arranged in the writing board. The top of the display screen is fixedly connected with a baffle, a monitoring camera is installed in the middle of the top end of the baffle and transmits collected data to a central control system, a lifting mechanism is arranged in the supporting frame, and a supporting moving mechanism is arranged at the bottom of the base. The lifting mechanism comprises a connecting rod. Student images and video data are collected in real time through the monitoring camera, behavior recognition and emotion analysis are combined, and a teacher is helped to know the classroom state and emotion change of students in real time. The data storage and analysis module effectively manages and summarizes classroom data, generates a teaching quality report and provides accurate teaching feedback.

This application provides a method and related equipment for implementing a psychological and emotional companionship service based on an intelligent agent. It relates to the technical field of data processing, including receiving user input information to generate session data, extracting emotional features to construct an emotional feature vector, and constructing an emotional change sequence based on consecutive session rounds to determine emotional trend information; matching the emotional feature vector and emotional trend information with an emotional companionship strategy, generating emotional companionship feedback content and outputting it to the user terminal; performing emotion recognition on subsequent user session data, and adjusting the emotional companionship interaction strategy when the emotion recognition result does not meet preset improvement conditions. By calculating the emotional feature deviation vector and re-matching an alternative strategy when the emotion does not meet preset improvement conditions, dynamic recognition of user emotional change trends and adaptive adjustment of the emotional companionship strategy are achieved, overcoming the problem of accurately recognizing user emotional change trends during emotional companionship interaction.

The invention discloses an emotional state dynamic monitoring and feedback method, which relates to the technical field of emotional monitoring and comprises the following steps: S1, collecting voice data, S2, monitoring equipment data, S3, determining an emotional score value, S4, calculating a baseline score value and S5, adding label information. According to the method, the accuracy of emotion recognition is improved by combining a time sequence modeling and weighted scoring mechanism of RNN and LSTM, the model not only can extract deep emotion features from sequence data, but also can reflect the actual contribution of different emotion dimensions to the overall state through weight adjustment, so that the score better fits the characteristics of the user, and the user experience is improved. Meanwhile, dynamic calibration and context perception of emotion evaluation are realized by establishing an emotion baseline in a non-interaction state, and a baseline score and a fluctuation value form an'emotion normal state 'reference system of the user, so that the system can distinguish emotion change caused by external interaction from emotion fluctuation of the user, and the user experience is improved. Therefore, the real emotion influence of the interaction behavior can be more accurately judged.

The invention discloses a middle and primary school emotion response type education auxiliary method and system and a medium, and the method comprises the steps: obtaining the facial expression data and voice data of a user, and extracting the facial emotion features of the facial expression data and the language emotion features of the voice data; analyzing the facial expression data and the voice data through an emotion recognition model, and performing learning emotion monitoring; and generating personalized teaching suggestions based on an emotional response mechanism. The emotional state of the student is accurately identified, the emotional change of the student is captured in real time, the teaching strategy is adjusted, and the emotional requirement and learning rhythm of the student are met.

The invention discloses an interaction and emotion response method of education agent hardware equipment, and relates to the technical field of education equipment interaction, and the method comprises the steps: S1, data collection: capturing an interaction feedback behavior of a user on emotion response in real time through an interaction feedback collection module of the education agent hardware equipment, meanwhile, acquiring emotional state change data of the user in real time through an emotional data acquisition module of the hardware equipment; according to the method, a human-computer interaction closed loop of response output-feedback acquisition-iterative optimization is constructed, an evaluation model is constructed in combination with multi-dimensional interaction feedback data and emotional state change data, real-time judgment on an emotional response effect is realized, layered iterative interaction mode optimization is triggered, and the evaluation efficiency is improved. The problems that in the prior art, emotion response lacks dynamic evaluation and automatic iteration and is insufficient in accuracy and adaptability are solved, continuous optimization of the emotion response based on user interaction behaviors is achieved, the emotion change and learning requirements of the user are effectively met, and the fitting degree of man-machine interaction and the learning experience of the user are improved.

To provide means for monitoring a facial expression of a student, and for monitoring the change in emotions, so as to detect bullying at an early stage.SOLUTION: A system is for detecting bullying at an early stage and it includes: means for monitoring a facial expression of a student; and means for monitoring the change in emotions.SELECTED DRAWING: Figure 1

The invention provides a man-machine interaction method and device, electronic equipment and a medium, and belongs to the technical field of artificial intelligence. Inputting the extracted current emotion features of the user and the character features of the user extracted from the text data converted from the current emotion features into a preset emotion recognition model and a preset character recognition model respectively to obtain current user emotion type probability data and user character type probability data; and obtaining an emotional character fusion type and fusion type probabilities, taking the emotional character fusion type corresponding to the maximum fusion type probability as a current emotion judgment result of the user, obtaining a most matched emotion guide scheme based on the result, and fusing individual character difference factors of the user to an emotion analysis process of the user. The current emotional state of the user is judged more accurately, corresponding sexual emotion guidance can be given timely and accurately according to the individual emotion change of the user, and the user experience is improved.

The beneficial effects of the method and system for analyzing the emotional evolution of learners based on causal graph neural networks provided by the embodiments of the present application are as follows: The present application designs a multi-level emotional association semantic description framework based on "learner-activity-emotion" to obtain multi-modal time-series data of learners; a cross-modal time-series fusion method based on Z-Transformer is used to fuse the multi-modal time-series data; according to the fused time-series data of learners, an emotional structure causal model for fine-grained emotional evolution of learners is constructed; according to the emotional structure causal model, an intervention variational graph auto-encoder is used to establish an emotional heterogeneous causal graph; according to the emotional heterogeneous causal graph, a causal graph neural network is constructed to analyze the emotional evolution of learners. The present application can dynamically track the real emotional state and its trend of learners, deeply analyze the spatio-temporal causal relationship of learners' emotional changes, realize the deep revelation of the emotional changes and their traceability laws of learners in the classroom, and help with precise teaching in the classroom.

Systems and methods for estimating emotional states, moods, affects of an individual and providing feedback to the individual or others are disclosed. Systems and methods that provide real time detection and monitoring of physical aspects of an individual and / or aspects of the individual's activity and means of estimating that person's emotional state or affect and change to those are also disclosed. Real time feedback to the individual about the person's emotional change, change or potential change is provided to the user, helping the user cope, adjust or appropriately act on their emotions.

The present invention discloses an emotion recognition method, program product, device and storage medium based on an autonomous and controllable large model. The method includes: obtaining the emotion recognition data to be recognized in the current round of dialogue, and preprocessing to obtain the word set of the current round of dialogue; predicting the confidence of the emotion of the current round of dialogue belonging to each type of emotion according to the word set of the current round of dialogue and the historical dialogue, forming the emotion distribution of the current round of dialogue, taking each type of emotion as a node, taking the emotion transfer probability between each type of emotion as a directed edge, establishing a multi-emotion transfer map, and calculating the emotion trajectory fluctuation probability value according to the multi-emotion transfer map; if the emotion trajectory fluctuation probability value is less than a first threshold value, or the distribution-level emotion transfer probability is less than a second threshold value, the current round of dialogue is judged as an abnormal emotion fluctuation trend, otherwise it is judged as a normal emotion fluctuation trend. The present invention takes into account the emotion recognition of multiple emotions coexisting in multi-round dialogues, and can also reflect the trend of user emotion changes.