43 results about "Mental state" patented technology

Exemplary embodiments include a computer-implemented method of training a neural network and / or a large language model for automatically collecting, analyzing, and transmitting data to induce a mental state and change a behavior in a mammal.

A method for assessing the mental state of a subject based on physiological data, a storage medium, and a device. The method comprises: presenting to a subject at least one task in a first composite task, wherein the first composite task comprises at least two types of tasks (S101); and acquiring at least two pieces of physiological data of the subject during first composite task execution, so as to perform auxiliary assessment on the tendency of mental illnesses of the subject on the basis of analysis results of the at least two pieces of physiological data (S102). Therefore, auxiliary assessment can be efficiently and accurately performed on the tendency of mental illnesses of the subject, especially mental illnesses with similar symptoms.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a mental state of a patient based on a natural language input and determining whether a relevant subset of brain data is anomalous. One of the methods includes receiving a natural language input describing at least one aspect of a mental state or of a behavior of an individual; generating a prompt based on at least in part the natural language input; submitting the prompt to a large language model; receiving at least one functional network that influences the at least one aspect of a mental state or of a behavior; for each network of the at least one functional network, analyzing MRI data for the individual to determine whether the network is anomalous; displaying to a user each network and whether it is anomalous; and taking an action in response to the displaying.

The invention discloses a psychological state text classification method based on large model generative data enhancement, and relates to the technical field of natural language processing. According to the method, patient psychological semantic clusters are automatically found by clustering a limited number of original samples, and then a natural language semantic template of each patient psychological semantic cluster is extracted; then, under the dual control of emotional polarity and psychological themes by utilizing a large language model, according to the natural language semantic templates, psychological state texts with consistent themes and diversified expressions are generated as enhanced samples; according to the method, high-quality sample generation is carried out by utilizing a natural language semantic template obtained based on clustering through a large language model so as to expand a model training sample, and the authenticity and diversity of the generated enhanced sample can be ensured through the large model generation type data enhancement method; therefore, the classification accuracy and generalization ability of the psychological state text classification model obtained through training are improved.

A method of predicting an group mental state according to the present disclosure includes acquiring video data of a first individual and a second individual, extracting first image data of the first individual from the video data, extracting first audio data of the first individual from the video data, extracting second image data of the first individual from the video data, extracting second audio data of the first individual from the video data, extracting first semantic text data from the first audio data, and extracting second semantic text data from the second audio data. The method further includes identifying a first set of features, identifying a second set of features, predicting a first mental state value, predicting a second mental state value, predicting a third mental state value, predicting a fourth mental state value, and generating first and second average mental state values.

The invention relates to the technical field of mental health management, in particular to a teenager mental health risk assessment method and system, and the method comprises the steps: obtaining basic information parameters of teenagers to be subjected to mental health assessment; analyzing psychological state related factors based on the basic information parameters of the teenagers to obtain a psychological influence factor set; based on the psychological influence factor set, matching a plurality of factor evaluation indexes corresponding to the psychological influence factor set; carrying out data acquisition on the plurality of factor evaluation indexes to obtain a psychological state associated data list; obtaining a standard psychological state data list corresponding to the basic information parameters based on the basic information parameters; constructing a psychological state difference matrix through the collected psychological state associated data list and the standard psychological state data list; inputting the psychological state difference matrix into a preset psychological health risk prediction model to generate a psychological health risk probability; and comparing the psychological health risk probability with a preset health risk threshold, and generating a psychological health assessment result of the teenager.

A method of predicting mental state includes extracting image data, audio data, and semantic text data from video data, where the video data portrays a first individual. The method further includes analyzing the image data to identify a first feature set, analyzing the audio data to identify a second feature set, analyzing the semantic text data to identify a third feature set, and predicting a mental state for the individual based on the first feature set, the second feature set, the third feature set, and a multidimensional mental state model. The predicted mental state is output. The multidimensional mental state model includes a first dimension, a second dimension, and a third dimension. The first dimension corresponds to a first aspect of mental state, the second dimension corresponds to a second aspect of mental state, and the third dimension corresponds to a third aspect of mental state.

The present disclosure provides systems and methods for assessing a mental state of a subject in a single session or over multiple different sessions, using for example an automated module to present and / or formulate at least one query based in part on one or more target mental states to be assessed. The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and / or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. The data can be processed using one or more individual, joint, or fused models. One or more assessments of the mental state associated with the subject can be generated for the single session, for each of the multiple different sessions, or upon completion of one or more sessions of the multiple different sessions.

The invention discloses an intelligent monitoring method and system for the mental health state of teenagers, and particularly relates to the technical field of mental health monitoring. The method comprises the following steps: collecting emotional response data and intervention feedback data generated by teenager individuals in a psychological intervention process, and constructing an initial psychological state feature sequence; performing quantitative evaluation on a matching effect between the intervention measure and the teenager psychological state based on the initial psychological state feature sequence to obtain intervention measure dynamic effectiveness evaluation index data; obtaining intervention measure utility attenuation trend prediction data and intervention measure instant utility response feature data by analyzing and identifying a long-term interaction change trend and a short-term action effect between the intervention measure and a psychological state; the psychological state matching adaptation degree data is generated through feature combination and optimization, the intervention measures are dynamically optimized, the real-time optimization intervention strategy is output, and the stability and the individualized adaptation degree of the psychological intervention process are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a multi-agent architecture-based gamification psychotherapy healing system, which comprises four agents, namely a trigger, a negative thought generation module, a guider module and a planner module, and is characterized in that the trigger is used for generating a dynamic virtual scene; the negative thought generation module is used for generating a negative thought matched with the psychological state of the patient; the guider module is used for generating a structured suggestion of the corresponding iteration process according to the dynamic virtual scene and the negative thought of the current iteration process and compressing the structured suggestion into a key abstract; and the planner module is used for generating a strategy instruction according to the key abstract of the current iteration process and the patient feedback information. According to the method, the dynamic scene is generated through multi-agent cooperation, so that a patient can more easily substitute own emotion in a virtual environment, the immersive experience is enhanced, and the common emotion is remarkably improved; feedback of a patient can be sensed in real time, scene content and role behaviors are dynamically adjusted, and a personalized chemotherapy healing path is achieved; and various psychological themes are supported, and the method can be expanded to different psychological health intervention scenes.

An arousal estimation system is proposed herein that estimates a human psychophysiological state on an arousal scale, using an arousal detection model that outputs a single arousal score characterizing arousal, rather than a particular target state, such as drowsiness, stress, or cognitive load. Based on an estimated arousal score, the human state can be characterized as either within or outside a desired range of arousal levels associated with optimal performance of relevant tasks. The arousal detection model is trained on a wider set of sensor data than may be applicable to a specific mental state. By training the arousal detection model on the wider set of sensor data associated with different arousal levels, an arousal score generated by the arousal detection model may generate a more accurate estimation of the human state than the conventional single state detectors, even when combined.

The invention relates to the technical field of health monitoring, in particular to a psychological state visual monitoring method, system and device and a storage medium. The method comprises the following steps: acquiring multi-dimensional index data; after dimensionality reduction and clustering processing are conducted on the multi-dimensional psychological index data, a thermodynamic diagram is generated and displayed; performing multi-scale decomposition, trend line fitting and layered rendering on the multi-dimensional psychological index data, and then dynamically displaying the data; mapping the multi-dimensional psychological index data into a three-dimensional sphere for display; and carrying out risk early warning based on abnormal analysis and trend analysis of the multi-dimensional psychological index data. According to the method, abstract psychological data is converted into visual experience through visual modes such as an emotion thermodynamic diagram, a three-dimensional sphere and a dynamic trend curve, the limitation of traditional character and number expression is broken through, risk early warning is carried out through anomaly analysis and trend analysis, and active prediction is achieved.

The present invention discloses a real-time assessment method and system for the physical and mental state of the elderly based on facial expression recognition. The method comprises: constructing a facial expression recognition network framework based on an attention module, the facial expression recognition network framework based on the attention module comprising an image sequence, a three-dimensional convolution Conv3D, a three-dimensional residual block 3D Residual Block, a fully connected layer FC, a Softmax classifier based on cross-entropy loss, and an efficient attention module EAM Block; constructing a training data set according to user needs; inputting the constructed training data set into a facial expression recognition model to train the facial expression recognition model; inputting a video of the elderly's facial expression to be processed into the trained facial expression recognition model to determine the elderly's emotion or physical and mental state. The present invention constructs an efficient attention module EAM for facial emotion recognition, models the information features of different channels and video frames, improves the network's attention to important information, and more accurately and efficiently recognizes the elderly's physical and mental state based on the elderly's facial expression video.

The invention discloses a non-contact physical and psychological state detection method and device for a driver, and the method specifically comprises the steps: obtaining a video image of the head of the driver through a common camera, and screening and optimizing effective video frames meeting the physiological and psychological index calculation through an image data enhancement and face correction method, a face attribute recognition model is used for recognizing shielding conditions of head and eye areas of a driver, a corresponding face to-be-detected area is extracted according to a recognition result, a direct-current component and an alternating-current component of pulse waves BVP are calculated in the area, and meanwhile, a convolutional neural network model is used for completing psychological emotion recognition of the driver. Physiological and psychological indexes of the driver are obtained; and associating the physiological and psychological indexes under the current effective video frame to construct a physiological and psychological mapping table, and generating a psychological state prediction model according to a multi-frame accumulation result to realize psychological state prediction of the driver. The physiological and psychological indexes of the driver can be monitored and predicted in real time, so that the physiological and psychological health condition of the driver can be comprehensively and systematically evaluated and predicted.

A work assistance system (300) is provided with a controller (50). The controller (50) uses psychological state information, which is information relating to the psychological state of the operator (MP) when the operator (100) is manipulated and / or when the simulator (200) of the operator (100) is manipulated, to determine the content of work assistance for improving the psychological state of the operator (MP) when the operator is manipulated.

The invention relates to a psychological state detection method and device, computer equipment, a storage medium and a product, and relates to the technical field of computers. The method comprises the following steps: acquiring an image frame set of a target object; the image frame set is extracted from a real-time image data stream; performing facial expression recognition on each image frame in the image frame set to obtain an emotion recognition result of each image frame; determining the current emotional state of the target object based on the emotion recognition result of each image frame; determining a psychological state of the target object based on the current emotional state and a historical emotional state of the target object; according to the method, the psychological state can be judged by utilizing a non-contact visual analysis means, so that the universality of psychological state judgment and the environmental robustness are improved, the continuity and tendency analysis of the psychological state is realized, and the timeliness and the judgment accuracy of early warning for potential psychological health risks are improved.

The present application belongs to the technical field of computer vision, in particular to a psychological condition evaluation method based on channel attention and variable-length sequence processing, comprising: obtaining and processing psychological evaluation video data of a subject to construct an original feature tensor; constructing an adaptive multi-feature spatiotemporal attention network to obtain aligned features; constructing an adaptive aligned channel attention module to obtain enhanced features; inputting the enhanced features into a regression prediction head network, and performing end-to-end training on the network based on a joint loss function to evaluate the subject's psychology; and the present application solves the technical problems of large differences in actual psychological evaluation task duration, significant sequence length changes, and low processing efficiency and large information loss of variable-length sequences in the prior art.

The present application discloses a method and device for detecting mental states. The method includes the following steps: collecting a video stream of a human head, and obtaining multiple head images from the video stream; processing the multiple head images to locate the contours of multiple organs related to the head, and obtaining the vibration frequency of each pixel point within the contours; calculating the temporal change data of the resonance frequency related to the head within a preset time according to the vibration frequency of each pixel point; and outputting multiple mental states and the levels of each mental state according to the temporal change data. According to the present application, the temporal change data of the resonance frequency within a preset time is calculated based on the vibration frequency of each pixel point within the contours of multiple organs related to the head, so that the mental state can be objectively and accurately detected, and thus has the advantages of wide application range, convenient use, and high accuracy.

The invention discloses a television program recommendation method, a storage medium and equipment, and relates to the technical field of smart home / smart home, and the television program recommendation method comprises the steps: obtaining physiological feature data of audiences in front of a television, the physiological feature data comprises data detected by a millimeter wave radar and / or face images of audiences shot by a camera; and determining a mentality change index of the audience based on the physiological feature data of the audience, and determining a recommended program based on the currently played program, the mentality change index of the audience and the program recommendation index of each candidate program in the plurality of candidate programs. According to the method, the mind state change of the user is detected in real time through the millimeter wave radar and the image acquisition equipment, accurate detection of the mind state change of audiences is realized, the accuracy of a program recommendation result is improved, and the use experience of the user is improved.

This application relates to the fields of computational psychology and affective computing, and in particular to a method and system for mental health analysis based on a large model and multimodal input. The method includes acquiring user data to be analyzed and preset mental health assessment goals; analyzing the data to be analyzed based on the preset mental health assessment goals to obtain initial psychological data; generating multimodal interactive psychological stimulus scenarios based on the initial psychological data and a multimodal model; acquiring multimodal response data of the user during the interaction with the psychological stimulus scenarios; performing spatiotemporal alignment and correlation analysis on the multimodal response data and the stimulus timelines corresponding to the psychological stimulus scenarios to obtain correlation analysis results; calculating the user's psychological schema parameters based on the correlation analysis results and an inverse mapping model; and generating a mental state assessment report based on the psychological schema parameters. This method has the advantages of enabling dynamic interaction in the mental assessment process, in-depth exploration of analytical dimensions, computable transformation of psychological patterns, and improved ecological validity.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a mental state of a patient based on a natural language input and determining whether a relevant subset of brain data is anomalous. One of the methods includes receiving a natural language input describing at least one aspect of a mental state or of a behavior of an individual; generating a prompt based on at least in part the natural language input; submitting the prompt to a large language model; receiving at least one functional network that influences the at least one aspect of a mental state or of a behavior; for each network of the at least one functional network, analyzing MRI data for the individual to determine whether the network is anomalous; displaying to a user each network and whether it is anomalous; and taking an action in response to the displaying.

The invention discloses a psychological state self-adaptive grading method and system based on multiple modes, and belongs to the technical field of artificial intelligence. Collecting four modes of vision, voice, behavior and text of an identified person, and calculating through each mode identification sub-model to obtain a single-mode confidence coefficient; a self-adaptive weight distribution mechanism based on a sliding window is introduced to determine a dynamic stability score of each mode; obtaining a time-varying weight by adopting index normalization to obtain a multi-modal fusion score; a two-stage time smoothing mechanism is adopted to obtain a time sequence score finally used for early warning judgment; according to the time-varying global threshold, the sensitivity bandwidth, the proportion of the upper threshold and the continuous threshold exceeding duration, determining a grading early warning trigger criterion, and then performing grading early warning on the time sequence score to obtain an early warning grade; an autoregressive correction factor is introduced into a decision-making layer, so that the early warning level depends on the state of the previous moment, and the early warning level depending on the previous moment is obtained. The method not only has multi-mode perception, but also can perform graded early warning.

A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

[Problem] The physical strength and mental state of a person was unable to be conventionally ascertained by using behavior information about the person. [Solution] A behavior analysis device 10 comprises: a knowledge storage unit 1011 in which a physical strength score, which is the degree of recovery or the degree of depletion of the physical strength of a user when the user has performed a behavior, and a mental score, which is the degree of recovery or the degree of depletion of mental fatigue, are stored in association with two or more pieces of behavior information specifying the behavior of the user; a reception unit 102 which receives the behavior information about the user; a score acquisition unit 1031 which acquires, from the knowledge storage unit 1011, the physical strength score and the mental score associated with the behavior information received by the reception unit 102; and a score output unit 1041 which outputs the physical strength score and the mental score acquired by the score acquisition unit 1031 in an associated manner. This behavior analysis device makes it possible to ascertain the physical strength and mental state of a person by using behavior information about the person.

The invention relates to the technical field of self-adaptive interviews, and discloses a visited object potential state determination method and system based on a self-adaptive interview model, and the method comprises the steps: carrying out the data preprocessing and comprehensive feature processing operation of the current interview data of an interview object in the current interview conversation round, and obtaining a data processing result; determining the current potential state information of the visited object for the current interview conversation round according to the self-adaptive interview model of training convergence and the data processing result; the interview object comprises a visited object, and the current potential state information comprises one or more of a credibility index result, a fatigue index result, a participation index result and a topic interestingness index result. Therefore, the accuracy, the reliability, the determination efficiency and the timeliness of the potential psychological state of the visited object can be improved, the interview object is helped to perceive the psychological state of the visited object in real time so as to carry out interview adjustment timely and accurately, and the interview accuracy, the interview efficiency and the interview quality are improved.

This application discloses a method for classifying psychological state texts based on large-scale generative data augmentation, relating to the field of natural language processing technology. This method automatically discovers patient psychological semantic clusters by clustering a limited number of original samples, then extracts the natural language semantic template for each patient's psychological semantic cluster. Subsequently, under the dual control of emotional polarity and psychological theme, a large-scale language model is used to generate psychological state texts with consistent themes and diverse expressions according to these natural language semantic templates as augmented samples. This method utilizes the large-scale language model to generate high-quality samples based on the natural language semantic templates obtained from clustering to expand the model's training samples. This large-scale generative data augmentation method can ensure the authenticity and diversity of the generated augmented samples, thereby improving the classification accuracy and generalization ability of the trained psychological state text classification model.

[Problem] To make it possible to estimate the mental state of a user. [Solution] An information processing system characterized by comprising: a detection unit that detects the level of an attribute of a user by analyzing a video obtained by filming the user; a calculation unit that calculates a second variation level of a first variation level pertaining to the level of the attribute in the video; and an estimation unit that estimates the mental state of the user on the basis of at least the second variation level.

The invention discloses a psychological state sensing method and system. The method comprises the following steps: acquiring a plurality of psychological state questions for performing psychological state perception on a target user; answering a plurality of psychological state questions perceived by the psychological state through the target user to obtain a target answer result; acquiring physiological parameters of the target user; inputting the physiological parameters into the physiological parameter judgment coordinate system to obtain intervals corresponding to the physiological parameters; determining a first psychological state of the target user and a probability value of the first psychological state based on an interval corresponding to the physiological parameter; and determining a target psychological state of the target user based on the target answering result, the first psychological state, the probability value of the first psychological state and the historical psychological state of the target user. The technical problem that the psychological state perception of the user is inaccurate due to the fact that the factor for determining the psychological state perception is single is solved.

The invention provides a psychological state diagnosis method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a student training data set; based on the student training data set, separately determining student behavior characteristics of each student; calling an initial consumption psychological association model, and determining psychological characteristics of each student based on the student behavior characteristics of each student; calling an initial psychological health prediction model, and determining a psychological health prediction result of each student based on the student behavior characteristics and psychological characteristics of each student; performing model training on the initial consumption psychological association model and the initial psychological health prediction model based on the psychological health prediction result and the training label of each student to obtain a target consumption psychological association model and a target psychological health prediction model, the target consumption psychological association model and the target psychological health prediction model are used for psychological state diagnosis. The embodiment of the invention can improve the accuracy of psychological state diagnosis.

The present application provides a multi-modal mental state detection method and system based on a spatio-temporal attention mechanism. After obtaining facial expression data and speech data, the method can extract facial expression features and speech features from the facial expression data and speech data, and unify the feature dimensions. Then, the facial expression features and speech features with unified feature dimensions are input into a spatio-temporal attention transformer to obtain spatio-temporal fusion features. Finally, the spatio-temporal fusion features are input into a mental state classifier to obtain a classification result. The method uses two types of modal data, namely, human faces and speech in video stream data, extracts temporal features and spatial features in each single modality respectively, and fuses the spatio-temporal features of the two modalities, improving the complementary and collaborative effects of features between modalities. Furthermore, it can extract features that can distinguish different mental states, realize the detection of mental states using the social media data of users, and improve the accuracy of mental state detection.