76 results about "Emotion detection" patented technology

The invention relates to the technical field of artificial intelligence, and particularly provides a multi-modal information fusion emotion detection method based on visible light and voiceprint. The method comprises the following steps: respectively acquiring video images and environment sounds of old people in a monitoring environment through a visible light image module and an audio voiceprint acquisition module; a facial expression detection module is used for recognizing a human face in the video image, and a visual anomaly signal is output; recognizing an audio voiceprint signal in the environmental sound according to a voiceprint detection module, and outputting an audio abnormal signal; according to the method, the detection accuracy is effectively improved, the missing report is reduced, the method is suitable for home and old-age care institution environments, and the method is of great significance to guarantee the safety of old people and alleviate serious consequences caused by falling down.

The invention provides a family-centered child development platform designed to support and enhance a child's academic, emotional, social, and cultural growth. By utilizing an AI-driven system for personalized learning, immersive VR / AR technologies for engaging educational experiences, and a community-building module for peer interaction, it offers an integrated solution for holistic child development. The system incorporates emotion detection, biometric feedback, and emotion-driven content adaptation, which dynamically adjusts the child's learning experience based on real-time emotional and physiological data. Additionally, the platform features culturally relevant content, promotes intergenerational collaboration, and includes a gamified learning environment to foster motivation and engagement. A secure interaction system ensures safe online communication, while the adaptive challenge system maintains an optimal level of difficulty tailored to the child's evolving needs. By combining these technologies, the platform provides a personalized, interactive, and supportive learning journey for children, supported by parents and educators.

The invention is suitable for the technical field of emotion detection, and provides an anxiety risk detection system based on multi-scale time sequence feature fusion, and the system comprises a short-term feature obtaining module which is used for obtaining short-term branch features of multi-modal data of a user; the short-term branch features are used for describing psychological state changes of the user in a recent short time period; the long-term feature acquisition module is used for acquiring long-term branch features of the multi-modal data; the long-term branch features are used for describing a stable psychological trend of the user in a long time period; the attention fusion module is used for fusing the short-term branch features and the long-term branch features based on an attention mechanism to obtain a fusion result; and the detection module is used for processing the fusion result to obtain an anxiety risk detection result of the user. The accuracy and interpretability of anxiety risk detection can be improved.

The embodiment of the invention provides an emotion detection method based on a multi-mode electroencephalogram eye movement signal. According to the method, a dual-path electroencephalogram modal encoder is used for determining time sequence characteristics of an electroencephalogram in a time domain path, determining frequency domain characteristics of an amplitude spectrum of the electroencephalogram in a frequency domain path, and obtaining dual-path time domain and frequency domain fused electroencephalogram characteristics based on the time sequence characteristics and the frequency domain characteristics; the dual-path eye movement modal encoder is used for determining pupil features of the eye movement data in the pupil diameter path, determining eye movement fixation features of the eye movement data in the fixation point path, and obtaining fused eye movement features based on the pupil features and the eye movement fixation features; and the multi-modal expert mixing module is used for dynamically performing modal calling on the multi-modal mixed characteristics which are subjected to modal alignment and are fused with the electroencephalogram characteristics and the eye movement characteristics, performing emotion detection by utilizing an expert with a modal corresponding to the multi-modal mixed characteristics, and outputting an emotion classification result. According to the embodiment of the invention, alignment is realized through multi-task pre-training, and the depressive emotion is accurately recognized.

Apparatuses, systems, and methods for low-latency audio-to-face animation with emotion detection are disclosed herein. The system may receive a first audio stream associated with a first device and a second audio stream associated with a second device, and provide, concurrently, a first segment of the first audio stream and a second segment of the second audio stream as inputs to an emotion detection artificial intelligence (AI) model to obtain first emotion data and second emotion data. The system may then provide, concurrently, a third segment of the first audio stream with the first emotion data and a fourth segment of the second audio stream with the second emotion data as inputs to a face animation AI model to obtain first face pose data and second face pose data, and provide the first face pose data to the first device and the second face pose data to the second device.

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 invention discloses a dialogue generation method and device, a customer service system, a storage medium and a program product, and the method comprises the steps: receiving request data inputted by a user, and carrying out the data preprocessing of the request data; performing intention analysis and emotion detection on the preprocessed request data to obtain user intention information and a user emotion state; performing data search on the user intention information to obtain problem analysis data; generating an initial reply text according to the user intention information and the question analysis data; and adjusting the initial reply text based on the emotional state to obtain a dialogue text. By adopting the embodiment of the invention, the intention and the perception emotional state of the user can be accurately analyzed, and multi-modal interaction is realized by fusing multi-element information, so that real-time response and efficient solution of questions of the user are realized.

The application provides an emotion detection method and device, and the method comprises the following steps: obtaining current dynamic category information of a target object; establishing a current three-dimensional dynamic portrait model of the target object according to the current dynamic category information of the target object; obtaining a current dynamic category result of the target object according to the current three-dimensional dynamic portrait model and a pre-trained emotion detection model; and prompting according to the current dynamic category result. The technical scheme of the application can obtain more accurate and less error results when detecting the unknown dynamic category of the target object, improve the detection efficiency, and improve the user experience.

The application discloses an emotion detection method and device, a terminal equipment and a readable storage medium, the method comprises the following steps: acquiring audio data, and converting the audio data into initial text data; splitting the initial text data into first text data and second text data, and adding bag labeling to the first text data; inputting the second text data into a preset language correction model to obtain third text data; inputting the third text data into an emotion detection model to detect emotion parameters corresponding to the third text data; based on the fact that the sample information collected by the bank outlets is inaccurate due to noise and noise, and single-sentence-level corpus labeling is difficult, the language model based on BERT is used to intelligently correct the corpus, the influence of noise data on the detection result is reduced, the mapping relationship from a paragraph to a single sentence is constructed through the emotion detection model, and finally the accuracy of the emotion detection of the bank outlets is improved.

The application discloses an emotion detection method in the field of artificial intelligence. The method comprises the following steps: acquiring a PPG signal of a pulse of a user through a PPG sensor; sending the PPG signal to a cloud server; receiving a user emotion state value sent by the cloud server, wherein the user emotion state value is obtained by processing a physiological feature vector according to a neural network model after the cloud server determines the physiological feature vector according to the PPG signal, and the physiological feature vector comprises blood oxygen saturation, heart rate and respiratory rate; and outputting a user emotion state according to the user emotion state value. The emotion detection method can improve emotion detection accuracy. The application also discloses a device capable of implementing the above emotion detection method.

A method of providing emotive text-to-speech includes obtaining input text characterizing a natural language response generated by an assistant LLM to a query input by a user during a conversation between the user and the assistant LLM, and processing, using the assistant LLM, the input text conditioned on an emotion detection task prompt to predict, as output from the assistant LLM, an emotional state of the natural language response. The method also includes determining, based on the emotional state of the natural language response predicted as output from the assistant LLM, an emotional embedding for the input text and instructing a TTS model to process the input text and the emotional embedding to generate a synthesized speech representation of the natural language response conveying the emotional state of the natural language response as specified by the emotional embedding.

A digital character emotion response system is disclosed, incorporating modules configured to process audio and video streams to predict emotional states. The system features an audio processing module, a video processing module, a fusion module for integrating audio and video features, and a machine learning module with an LSTM model for analyzing the combined data to predict and output emotional states with confidence scores.

The invention discloses a multi-modal abnormal emotion recognition method based on multitask, mixed data enhancement and contrast feature decomposition, and the method comprises the steps: transcribing voice data collected in psychological health census into text data, carrying out the feature extraction of the voice data and the text data, obtaining a word embedding sequence and frame-level acoustic features, and forming a sample; performing data enhancement on the word embedding sequence and the frame-level acoustic features to generate a diversified training sample; respectively constructing a text coding model and a voice coding model, and respectively training the corresponding coding models through word embedding sequences and frame-level acoustic features in the diversified training samples; carrying out dimension expansion on the obtained word embedding sequence and the frame-level acoustic features in a copying mode, and forming an abnormal emotion detection training sample by the expanded word embedding sequence and the frame-level acoustic features; and establishing a multi-modal abnormal emotion recognition model, and carrying out migration training on the multi-modal abnormal emotion recognition model through the abnormal emotion detection training sample.

A method includes receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker.

The invention discloses an emotion detection method based on spatial-temporal feature fusion of facial key points, and belongs to the field of computer vision and emotion calculation. The method comprises the following steps: acquiring a public data set and preprocessing the public data set to obtain 68 facial landmarks; designing a PLK algorithm to estimate the motion state of a facial landmark, and denoising the landmark through a bidirectional optical flow mechanism and Kalman filtering to obtain accurate landmark position information; modeling is carried out on the obtained accurate absolute position and relative change of the facial landmark through a BiGRU unit, and time sequence features of the facial landmark are extracted; constructing a face key point graph structure, and sending the face key point graph structure into a graph convolutional network in combination with the time sequence features of the face landmarks for iterative updating to obtain space-time fused face features; and sending the obtained space-time fused facial features into a linear layer for classification to obtain an emotion recognition result. According to the method, comprehensive extraction of time sequence features and space features related to the emotion is realized, and an efficient and high-accuracy method is provided for detection of the facial emotion.

Disclosed in the present application are an interaction method and system, and an electronic device. The electronic device 100 can perform emotion detection on the basis of physiological data of a user to generate an emotion detection result, and generate, on the basis of the emotion detection result, and optionally in combination with personalized information of the user (e.g., environmental information of the user, exercise health data, associated event information, and personal information of the user), a personalized cartoon image capable of representing the emotion of the user, wherein the cartoon image can be displayed locally on the electronic device 100, or can be displayed on a full-scene device of the user of the electronic device 100 or other devices. In this way, social exploration requirements of users can be effectively promoted, and the users can be concerned about themselves and others. In addition, the electronic device 100 can also support the interaction between the user and the cartoon image, such that different feedbacks can be given to the user on the basis of different emotional states. In this way, the emotions of users can be effectively adjusted, thereby improving the user experience.

The application discloses a kind of emotion detection methods based on facial key point space-time feature fusion, belong to computer vision and affective computing field.The application obtains public dataset and is preprocessed, obtains 68 facial landmarks;Design PLK algorithm to estimate the motion state of facial landmark, and through bidirectional optical flow mechanism and Kalman filtering to landmark denoising, obtain accurate landmark position information;The absolute position and relative change of the accurate facial landmark obtained are modeled by BiGRU unit, and the time sequence feature of facial landmark is extracted;The facial key point graph structure is constructed, and the time sequence feature of facial landmark is combined and sent into graph convolution network for iterative update, to obtain the space-time fusion facial feature;The space-time fusion facial feature obtained is sent into linear layer for classification, to obtain emotion recognition result.The application realizes the comprehensive extraction of emotion-related time sequence feature and spatial feature, and provides an efficient and high-accuracy method for facial emotion detection.

The invention discloses an EEG driving road rage emotion detection method, device and equipment, a medium and a program product, and belongs to the technical field of emotion recognition. Multi-channel electroencephalogram signals of a driver in a normal emotion and a road rage emotion are collected, emotion tags are added to the signals to construct samples, each channel in the electroencephalogram signal samples is used as a graph structure node, the phase correlation degree between the channels in the electroencephalogram signal samples is used as an edge weight, a graph structure is constructed, and the electroencephalogram signals are obtained. And training a to-be-trained EEG driving road rage emotion detection model by using the graph structure to obtain a trained EEG driving road rage emotion detection model used for detecting whether a driver has a road rage emotion during driving on a road surface. The synchronization degree between different channels is quantified through the phase correlation degree, the sensitivity to road rage recessive features is improved, the generalization ability of an EEG driving road rage emotion detection model is enhanced, and the accuracy of road rage emotion detection is ensured.

The invention relates to a user state recognition method and device, equipment and a storage medium. The method comprises the following steps: acquiring image data of a user in real time, acquiring environment data of the user and distance information between the user and the sensor through the sensor, generating target position information and target contour information of the corrected user based on the image data and the distance information, and determining the target contour of the user according to the target position information and the target contour information of the user. The method comprises the steps of obtaining a detection result of an object around a user, obtaining an emotion detection result of the user according to target position information, target contour information and the object detection result of the user, and identifying state information of the user according to the target position information, the target contour information, the object detection result, the emotion detection result and environment data of the user. According to the method, the state of the user can be accurately identified, the interaction between the user and surrounding objects and the emotional state are comprehensively considered, and the accuracy and reliability of dangerous behavior and abnormal emotion identification are remarkably improved.

The invention discloses a gait emotion recognition method based on a multi-modal hybrid expert model. The method comprises the steps of collecting and labeling walking video data of pedestrians with different emotions; processing the video data, and extracting human motion skeleton data to obtain first target data; processing the video data, and extracting human motion contour data to obtain second target data; performing data enhancement, processing and slicing on the first target data and the second target data; a multi-modal mixed expert emotion recognition model is constructed, space emotion features of gaits are obtained by using a skeleton space feature extractor and a contour feature extractor respectively, similar feature sequences are automatically allocated to corresponding time sequence expert models according to differences of gait time sequence modes under different emotions, and emotion features in time sequences are extracted. By recognizing walking postures of pedestrians with different emotions, emotion analysis of different pedestrians can be realized, and the problem of low accuracy caused by too large difference of different emotions in existing gait emotion detection is solved.

A system and method that provide a recommendation service to a user within a vehicle are provided. According to the method, a current emotional state of the user in the vehicle is detected in real time by an emotional detection device of the system; a recommendation service that conforms to a behavior habit of the user is determined by a recommendation engine of the system based on the current emotional state of the user in conjunction with a profile of the user, wherein the profile of the user is generated by analyzing historical data of the user when using a third-party service; and the recommendation service is outputted on the vehicle by an output device of the system.

An object of a system according to an embodiment is to promote sound communication in an online comment platform.SOLUTION: A system includes an emotion detection unit, a feedback provision unit, an erroneous information detection unit, and a tone evaluation unit. The emotion detection unit detects emotional wording of the comment. The feedback providing unit provides feedback for prompting cooldown on the basis of the emotional wording detected by the emotion detecting unit. The error information detection unit detects error information of the comment. The feedback providing unit provides accurate data or information based on the error information detected by the error information detection unit. The tone evaluation unit evaluates the tone and manner of the comment. The feedback providing unit provides feedback to prompt improvement based on the tone or manner evaluated by the tone evaluation unit.SELECTED DRAWING: Figure 1

The invention provides a memory and emotion detection system and device, and relates to the technical field of memory and emotion detection. The method comprises the following steps: acquiring a mental health state table of a target object; in response to the click operation, determining a task mode of the target object in a preset learning stage; the task mode is used for detecting memory and emotion; the task mode comprises a plurality of tasks, and the tasks comprise a starting task, an intermediate task and an ending task of which the difficulty is gradually increased. Taking the starting task as a starting point, responding to the click operation of each task, and generating an actual task index; adjusting a task difficulty parameter according to the actual task index, and generating a next task according to the task difficulty parameter; the task difficulty parameter is used for adjusting the difficulty of the next task. And if the actual task index of the ending task is generated, generating an intervention decision according to the mental health state table and the actual task index of each task. Therefore, the technical problem of poor detection accuracy of the memory and emotion detection system is solved.

The application discloses a kind of mood detection frequency determination method, device, equipment and medium, it is related to intelligent vehicle technical field, the method includes: obtaining mood detection reference frequency, and the vehicle driving information of total distance that vehicle driver has driven in current time period;According to vehicle driving information, the mood abnormality probability of vehicle driver in future time period is predicted;Wherein, future time period is adjacent to current time period, and located after current time period;According to vehicle driving information and mood abnormality probability in future time period, mood detection reference frequency is adjusted, obtains the mood detection frequency of future time period, to be detected according to mood detection frequency, vehicle driver is carried out mood detection in future time period, the flexibility of mood detection is improved, and the computing resource and electric power resource of vehicle are saved.

The embodiment of the invention provides a driver emotion adjusting method, device and equipment and a vehicle, and relates to the technical field of intelligent vehicles. The driver emotion adjusting method comprises the steps of performing emotion detection on a driver in a vehicle to obtain an emotion detection result; determining the emotion of the driver according to the emotion detection result; under the condition that the emotion of the driver is negative emotion, negative emotion adjusting parameters corresponding to all the environment illumination light sources are obtained based on the emotion of the driver and the interior materials of the areas where all the environment illumination light sources are located in the vehicle; and based on the negative emotion adjustment parameter corresponding to each environment illumination light source, controlling each environment illumination light source to perform environment illumination so as to adjust the emotion of the driver. According to the technical scheme, the negative emotion of the driver can be adjusted, and the driving safety is improved.

The invention provides a VR bloody scene induced emotion rapid detection method and system based on electroencephalogram, and relates to the technical field of electroencephalogram emotion detection. A subject wears virtual reality equipment and is connected with an eight-channel electroencephalogram acquisition device to obtain an electroencephalogram original signal with a scene time stamp. Time window electroencephalogram data are obtained through preprocessing and time window division, low-frequency energy characteristics are calculated at the frequency band of 1-10 Hz, and phase coupling characteristics between electroencephalogram channels are extracted based on 2 Hz narrow-band division. The coupling relation is divided into an opposite-side long distance and a same-side short distance in combination with the scalp space position, and the intensity characteristics are counted. Low-frequency energy features and long and short distance coupling strength are constructed into composite discriminant features to be input into a discriminant model, rapid detection of a bloody scene is achieved, and timeliness, objectivity and accuracy of emotion recognition are improved.

The invention discloses an emotion intervention method and system based on emotion detection, relates to the technical field of emotion intervention, and adopts chaos dynamics to analyze a Lyapunov index and a phase-space reconstruction dimension of a steering wheel, breaks through the static limitation of traditional threshold detection, realizes early quantitative recognition of an emotion sensitive state, and improves the accuracy of emotion intervention. The detection sensitivity of the road rage incubation period is obviously improved; a driving behavior state transition matrix is constructed through a hidden Markov model, and a dynamic evolution rule of an emotion accumulation process is effectively captured in combination with a time sequence feature of an accelerator pedal depth change rate, so that one-sidedness of single time point detection is avoided; road pressure index coupling environment pressure and physiological response are introduced, dynamic optimization of intervention opportunity is realized by means of a time convolutional network, and the problem of misjudgment caused by sudden change of external environment in a traditional scheme is effectively solved.

The present disclosure provides a method and device for dynamic recognition of emotion based on facial muscle movement monitoring, including: obtaining muscle movement data on one side and continuous frame images on the other side; obtaining multiple emotional states related to emotions, associating each emotional state with its corresponding continuous frame images, associating each emotional state with the muscle movement data corresponding to the continuous frame images at the same location and at the same time, to form a training set, building an emotion recognition model; inputting the muscle movement data and continuous frame images obtained in real time into the emotion recognition model to obtain a corresponding emotional state. This method builds an emotion recognition model through muscle movement data and continuous frame image, and uses motion data to make up for the parts of small action images that cannot be collected, thereby obtaining accurate emotion detection results.