26 results about "Mental state" patented technology

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.

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.

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 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.

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.

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.

Determining input data for at least one machine learning model based on electronic data of a user. Predicting, based on the input data and the at least one machine learning model, a mental state of the user, the mental state comprising mood values, uncertainty values, and magnitude values, each mood value being associated with a corresponding uncertainty value of the uncertainty values and a corresponding magnitude value of the magnitude values, the magnitude value indicating a relative strength or weakness of the associated mood value. Selecting and arranging, based on the predicted mental state, a subset of graphical elements, each graphical element being associated with a corresponding mood value of the set of mood values, and each graphical element of the subset of graphical elements being associated with the predicted mental state of the user.

An information processing device may include a first acquisition means, a second acquisition means, and an estimation means. The first acquisition means acquires a measurement result of an intellectual ability of a measurement target person. The second acquisition means acquires an estimation result of a mental state of the measurement target person, the mental state affecting the measurement of the intellectual ability. The estimation means estimates the intellectual ability based on the measured result of intellectual ability and the estimation result of the mental state. The information processing device can support decision-making based on the estimation result of the intellectual ability.

An object of a system according to an embodiment is to make care manager work more efficient and provide appropriate care plans and nursing services.SOLUTION: A system includes an interview agent part, an estimation part, a planning part, and a mediation part. The interview acting part acts for an interview with the family of the person to be cared or the person himself / herself. The estimation part estimates a nursing care state and a mental state from the contents of the conversation substituted by the interview substitution part. The planning unit plans an appropriate care plan or nursing care service based on the information estimated by the estimation unit. The mediation unit collects information on the care plan and the nursing care service planned by the planning unit, the availability of the nursing care facility, and the support business of the local government, and utilizes the information for proposal.SELECTED DRAWING: Figure 1

A method for providing real-time music stimuli to urge a subject having a time-varying mental state to attain a target state includes receiving a first physiological-signal that carries information that represents the time-varying mental state, using the physiological signal and information about the target state to generate a music stimulus in real time and transmitting the music stimulus for exposure thereof to the subject.

The invention discloses a mental state evaluation method, device and equipment based on face data analysis, belongs to the technical field of data processing, and aims to improve expressions in mental state evaluation into a neural network recognition method and assist with a space interaction search algorithm to achieve a better data learning effect. And finally, micro-expressions in the mental state evaluation process can be automatically recognized, mental state evaluation data can be standardized, automatic mental state evaluation is achieved, the data recognition effect can be effectively improved, and data errors caused by manual recognition can be avoided.

The invention relates to a psychological state evaluation scale entry scoring method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: acquiring a first scale entry of a psychological state; generating a plurality of dialogue scripts through a preset model based on the first scale entry; extracting training samples of a story introduction agent, a question synthesis agent and an answer scoring agent from each dialogue script; based on training samples of a story introduction agent, a question synthesis agent and an answer scoring agent, a preset large language model is finely adjusted through low-rank adaptation, and the story introduction agent, the question synthesis agent and the answer scoring agent are obtained; when the evaluation scale entries of the psychological state of the user need to be scored, multiple second scale entries corresponding to the user are obtained, and scores and explanations corresponding to the second scale entries are generated based on the story introduction agent, the question synthesis agent and the answer scoring agent. By adopting the method, evaluation can be quickly completed and interpretation deviation can be eliminated in interaction.

This invention discloses a method and system for assessing the psychological state of adolescents based on multimodal perception signals. The method includes: acquiring facial expression signals, speech signals, facial temperature signals, and heart rate signals of the subject; preprocessing each modality signal and inputting it into a built-in feature recognition model to obtain recognition results; binarizing and encoding the facial expression and speech signals to categorize them into an overt emotion group, and the heart rate and facial temperature signals into an implicit physiological group; weighting and summing the results according to preset modality weights to obtain a group composite value and comparing it with a preset group judgment threshold to obtain intermediate group results; and using preset two-group cross-validation rules to logically combine the two intermediate results to generate a final auxiliary assessment result and rule path identifier. The method and system of this application achieve full transparency and traceability in the judgment process, effectively identify implicit state characteristics, and possess feedback-based closed-loop iterative optimization capabilities.

The invention provides a psychological health assessment method and system based on VR interaction and multi-modal data, and belongs to the technical field of psychological emotion detection based on virtual reality, and the method comprises the steps: carrying out the feature extraction and fusion analysis of obtained voice signals and interaction behavior data through a pre-trained GRU-based deep learning model; obtaining a psychological state assessment result of the user; wherein the GRU-based deep learning model utilizes a GRU branch to deeply mine time sequence dynamic information in the audio, and the time sequence dynamic information is fused with behavior characteristics. According to the psychological assessment method, weak and dynamic physiological behavior modes of psychological sub-health people can be effectively captured, and by adopting the time sequence deep learning model, the recognition sensitivity and accuracy of early psychological state abnormity are improved.

The system according to this embodiment aims to analyze the user's emotions and mental state and provide appropriate advice. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives the user's statements. The analysis unit analyzes the content of the statements received by the reception unit and grasps the user's emotions and mental state. The provision unit provides advice based on the emotions and mental state grasped by the analysis unit. The customization unit customizes the chatbot's personality and dialogue style.

An object of the system according to the embodiment is to consistently perform determination of a mental state, proposal of a specific action or diet, generation of an action plan, and feedback of a result.SOLUTION: A system includes a mental state determination part, an action suggestion part, an action plan generation part, and a feedback analysis part. The mental state determination unit determines a mental state using the generated AI. The action suggestion unit suggests an action or a meal based on the mental state determined by the mental state determination unit. The action plan generation unit generates a specific action plan based on the action or the meal proposed by the action proposal unit. The feedback analysis unit feeds back an execution result of the action plan.SELECTED DRAWING: Figure 1