301 results about "Mental health" patented technology

Systems, devices and methods are provided for stimulating one or more nerves within a user. A system comprises stimulator having an electrode configured for contacting the outer skin surface at a target location and an energy source coupled to the stimulator, The energy source is configured to generate at least one electrical impulse and to transmit the electrical impulse transcutaneously from the electrode through the outer skin surface of the user to a selected nerve in the user. In certain aspects, the systems and methods are particularly useful for non-medical purposes, such as reducing stress and anxiety, enhancing relaxation, improving sleep, energy, concentration and mood, increasing mental or physical performance, promoting mental health, recovery and wellness, generally improving the health and wellbeing of a user and / or for temporarily or permanently improving intelligence, learning capacity, memory retention, recall, mood, alertness and / or sleep efficiency in human beings.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention relates to the technical field of health management, in particular to a psychological risk early warning method and system based on multi-dimensional emotion data of a user. The method comprises the following steps: acquiring user multi-dimensional emotion data corresponding to a user emotion expression image, a user emotion voice signal and a user emotion physiological index, and performing multi-source time sequence alignment processing on the user multi-dimensional emotion data to obtain user multi-dimensional time sequence synchronous emotion data; an emotion three-level feature extraction framework is constructed, modal emotion feature extraction and cross-modal fusion are carried out, and cross-modal fusion psychological features of the user are obtained; and obtaining user environment data and user social network data, and performing emotional psychological dynamic graph reasoning and psychological risk early warning on the user cross-modal fusion psychological features based on the user environment data and the user social network data to generate corresponding user emotional psychological risk early warning response measures. According to the invention, early recognition and intervention of psychological health problems can be realized.

The invention provides a psychological analysis method and system based on multi-modal features, and relates to the technical field of psychological assessment. The method comprises the following steps: synchronously obtaining voice data, video data, text data and physiological signal data of a user; respectively denoising the data to obtain each piece of denoised modal data; performing feature extraction on each modal data to obtain each modal feature; mapping each modal feature vector to a unified semantic space through an attention weighting mechanism to generate a fusion feature matrix; a trained psychological analysis model is adopted to perform multi-task learning on the fusion feature matrix, and emotion classification, pressure indexes and psychological health risk scores are synchronously output; and updating model parameters of the psychoanalysis model through an online incremental learning mechanism based on the real-time feedback data of the user and the newly added sample. Through accurate data denoising, efficient feature extraction and flexible model optimization, comprehensive and accurate analysis of the psychological state of the user is realized.

A method for developing a chatbot for mental health using generative Artificial Intelligence (genAI) and a system for recognition and recommendation are disclosed. The method comprises: designing a conversation flow, creating a flowchart that outlines the logical steps; defining guiding questions and the Depression, Anxiety, and Stress Scale (DASS) examination; fine-tuning large language models with the technical prompt engineering; collecting and labeling data for each model mental health issues detection: ill-being detection model and keyword recognition model; building pipeline and training Artificial Intelligent (AI) model for ill-being detection model and keyword recognition model with a Bidirectional Encoder Representations from Transformers (BERT) model or a pre-train model; collecting and processing mental health support resources such as Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, and effective coping strategies, and psychologists; developing mental health support resources system; and developing and integrating speech-to-text and text-to-speech models into the system.

The invention provides an artificial intelligence and cognitive behavior combined depression treatment intervention and early warning system, which comprises the steps of collecting patient interaction data, physiological perception data and third-party data through a dynamic on-demand authorization mechanism, and extracting four-dimensional feature indexes of cognition, emotion, behavior and physiology; constructing a first psychological matrix of cognition, emotion, behavior and physiological dimensions, dynamically adjusting dimension weights by adopting a Bayesian network to generate a second psychological matrix, and fusing weighted feature scores, change rates and historical mean values to output psychological health state vectors; generating a basic scheme group based on the state vector matching treatment strategy library; and calculating the unit time fluctuation intensity of the mental health state vector in real time, and triggering three-level early warning in combination with a self-adaptive Z-score safety threshold. According to the method, multi-modal data fusion analysis, dynamic personalized intervention and active risk prevention and control are realized, and the accuracy and safety of depression management are improved.

The invention provides a heart rate calculation method based on physical and psychological health analysis and a monitoring sensing device, and belongs to the technical field of medical equipment.The heart rate calculation method specifically comprises the steps that credible monitoring times in different stable monitoring times are determined by means of temperature change data, and the temperature change data in the different credible monitoring times are used for calculating the heart rate according to the credible monitoring times; the credible monitoring times are divided into different similar monitoring parameter groups, and when it is determined that the similar monitoring parameter groups with the health analysis results not meeting the requirements exist according to the analysis results of the heart rate data of the different credible monitoring times in the different similar monitoring parameter groups, the credible monitoring times are determined to be the credible monitoring times. According to the method, the distribution data and the temperature change data of the credible monitoring times in the different similar monitoring parameter groups are obtained, and the health analysis result of the monitoring analysis target is determined in combination with the health analysis results of the different similar monitoring parameter groups, so that the accuracy of the health analysis result is improved.

The invention discloses a cross-subject electroencephalogram emotion recognition method and device based on domain self-adaption and adversarial fusion, and belongs to the field of electroencephalogram signal processing and emotion calculation. The invention provides a cross-subject electroencephalogram emotion recognition method based on domain self-adaption and adversarial fusion, and aims to solve the problems that in the prior art, electroencephalogram signal preprocessing is insufficient in emotional feature retention capacity and cross-subject model generalization is poor, and the method specifically comprises the steps that an electroencephalogram signal data set is acquired, and electroencephalogram signals are preprocessed; after fractional order Fourier transform is carried out on the preprocessed electroencephalogram signals, differential entropy features are extracted; constructing a pre-training model; constructing a classification model; the classification model adopts an encoder in a pre-trained model after pre-training, and then a classifier is added; and adopting the classification model to realize target domain electroencephalogram signal emotion classification. Experiments show that the average accuracy of cross-subject emotion recognition on an SEED data set reaches 88.29%, and the method is suitable for scenes such as brain-computer interfaces and mental health monitoring.

The invention discloses a psychological health analysis method and apparatus, an electronic device and a storage medium, through the application, dynamic psychological assessment is generated through multi-modal data fusion analysis, and personalized scenarized dynamic interaction content is generated based on an assessment result and an interaction history, so that the psychological health analysis efficiency is improved. Meanwhile, continuous learning and correlation analysis are carried out on the psychological state of the user by utilizing a multi-dimensional memory architecture, so that the system can understand the change of the user state and adjust an interaction strategy like a human consultant, and therefore, the technical problems of insufficient emotional distraction and credibility of the user due to the adoption of a standardized reply mode in the prior art can be solved, and the user experience is improved. The technical effects of enhancing the emotion affinity of the system, establishing a continuous and credible interaction relationship and improving the durability of the psychological intervention effect are achieved.

The invention discloses a depression auxiliary evaluation method based on a large language model agent. The depression auxiliary evaluation method is characterized by comprising the following steps: a) performing structured processing on a Hamilton depression scale; b) constructing a dynamic questioning agent based on a large language model, and realizing dynamic selection and adjustment of theme questions; c) constructing a multi-dimensional scoring agent to obtain sub-item scores of the depressive symptoms; and d) constructing a clinical decision-making agent, generating risk early warning, giving personalized diagnosis and treatment and intervention suggestions and the like. Compared with the prior art, the method has the advantages that real-time interaction in a natural language form is realized, a real interview process is simulated, the method is closer to psychological consultation practice, user experience and evaluation reliability and validity are enhanced, user language behaviors and scoring results can be analyzed in real time, high-risk signals such as self-injury tendency and severe depression can be identified, and the method is suitable for popularization and application. Personalized diagnosis and treatment suggestions and intervention measures are output, intelligent assistance is provided for clinical decision making, and the intelligent level of mental health screening and management is improved.

The invention relates to the field of psychological health, and provides an employee psychological health management system and method based on cloud computing, and the system comprises a psychological archive management module which is used for collecting, classifying, storing and updating the psychological health data of employees, and calculating the comprehensive psychological health indexes of the employees; the intelligent psychological assessment module is used for acquiring psychological data of the employees and assessing psychological states of the employees by applying an advanced regression model to obtain psychological assessment results; the remote psychological counseling module is used for evaluating the psychological counseling effect of the employee to obtain a first evaluation value of the psychological counseling effect; the personalized psychological assistance plan generation module is used for generating a psychological assistance plan for the employee; and the virtual reality psychological treatment module is used for providing psychological treatment service for the employees through the virtual reality technology and evaluating the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect. According to the invention, the psychological health state of the employee can be comprehensively and accurately improved.

The invention discloses an intelligent evaluation system and intervention method for mental health of teenagers, and the method comprises the following steps: collecting multi-mode psychological signal data of the teenagers, and carrying out the preprocessing; establishing a cognitive energy field, and generating a cognitive energy time sequence curve; performing spectrum resonance analysis, and combining the spectrum resonance analysis with a cognitive energy time sequence curve; constructing a psychological topological graph, and calculating a topological complexity index; when the topology complexity index is continuously abnormal or the cognitive energy disturbance amplitude exceeds a set threshold value, a psychological risk early warning result is generated; after the psychological unstable state is detected, a cognitive energy feedback parameter set is generated, and cognitive energy balance intervention is executed; and after the intervention is finished, calculating an energy balance improvement rate, and when the energy balance improvement rate exceeds an energy balance threshold, judging that the intervention is finished. According to the method, multi-mode psychological signals and cognitive energy field modeling are fused, intelligent evaluation and dynamic intervention of the psychological state of the teenagers are achieved, and the method has the advantages of being high in accuracy, high in interpretability and closed in intervention loop.

The invention discloses a student mental health prediction method based on artificial intelligence, and particularly relates to the technical field of mental health prediction. A time sequence sample fusing multi-source behavior data is constructed, a deep learning model of an abrupt change detection mechanism is combined, the ability of recognizing short-term psychological abnormal changes of students is effectively improved, psychological risk signals are dynamically output when abrupt change occurs, and an individual intervention decision module is called to generate intervention suggestions, so that the accuracy of the short-term psychological abnormal changes is improved. Closed-loop management from risk identification to intelligent intervention is realized, the early warning capability and intervention timeliness of the system for sudden psychological crisis are remarkably enhanced, and the system has relatively high practical value and popularization prospect.

The invention provides a college faculty worker and student psychological health monitoring and intervention method and system, and relates to a college ideological and political care system, and the method comprises the steps: employing a Monte Carlo simulation method to generate a large number of negative emotion propagation paths, carrying out the statistics of the coverage range and influence of negative emotion nodes, determining the influence range and key propagation nodes of negative emotion, and carrying out the calculation of the negative emotion propagation paths. Quantitative indexes reflecting the propagation penetration degree of the negative emotion among different communities are obtained, wherein the quantitative indexes comprise the negative emotion propagation speed, the influence range and the duration time; in a college ideological and political education system, positive emotion information is pushed to a target community and key nodes through a social network platform, the negative emotion propagation dynamic state is monitored, according to the real-time change of emotion distribution in an emotion propagation network, an emotion intervention strategy is dynamically adjusted, diffusion of negative emotions is controlled, and community emotion management measures are enhanced; and community emotion interaction mode analysis is perfected.

The present disclosure provides compounds, pharmaceutical compositions, and methods for treating a mental disorder in a subject, which may include administering to the subject a pharmaceutical composition including a therapeutically effective amount of a non-hallucinogenic 5-HT2A receptor agonist or a pharmaceutically acceptable salt thereof, thereby treating the mental health disorder in the subject.

The invention discloses a multi-mode emotion recognition method for the depression tendency of teenagers, and relates to the technical field of mental health monitoring and evaluation. Comprising the following steps: acquiring an EEG signal data set, an ECG signal data set, a voice signal data set and a text signal data set; the obtained data set is preprocessed; performing feature extraction and fusion on the preprocessed data set to obtain a multi-modal feature matrix; constructing a multi-modal emotion recognition model based on the combination of a deep neural network and an attention mechanism; and inputting the multi-modal feature matrix into a multi-modal emotion recognition model to obtain an emotion recognition result. Through visual output of a cross-modal attention mechanism, the transparency of the model is enhanced, and the user trust degree in a medical scene is improved.

The invention relates to the technical field of health information processing, and particularly discloses a student psychological data analysis and early warning system based on artificial intelligence, which is provided with a data set complexity judgment module, a data slice judgment module, a GPU compatible matching module and a psychological data analysis and early warning module. The psychological health monitoring platform evaluates the comprehensive complexity by collecting a multi-source psychological data set of a student, and determines the number of psychological data slices and the respective complexity according to the comprehensive complexity. The platform monitors execution data of each GPU in real time, and evaluates an execution quality initial index and compatible complexity of the GPU. Then, through data processing, the comprehensive complexity of the psychological data slices is matched with the compatible complexity of each GPU, and distribution of the psychological data slices and the GPUs is completed. And finally, the platform analyzes the psychological data of the students by using the distribution relation, and finally realizes analysis and early warning of the psychological data of the students.

The invention discloses a psychological health screening method and system based on a large model, and relates to the technical field of psychological health, and the method comprises the following steps: scale data, micro expression video data and physiological signal data of a user are acquired, a multi-modal data acquisition module is constructed, and the physiological signal data at least comprises EEG (electroencephalogram) and PPG (pulse photoplethysmography); performing preprocessing and feature extraction on the scale data, the micro-expression video data and the physiological signal data; according to the psychological health screening method and system based on the large model, cross validation is carried out from three dimensions of subjective reports, unconscious behaviors and autonomic nerve responses by fusing multi-modal data such as scales, micro expressions, EEG and PPG; the problem that a traditional single scale method is easily influenced by subjective deliberate concealment, memory deviation and social sponsoring effect is fundamentally solved.

The invention discloses a non-inductive physical and psychological health monitoring and early warning system and method before a coal miner goes down a well, and belongs to the technical field of coal mine safety production and personnel health monitoring. A physiological monitoring unit comprises a millimeter-wave radar, an infrared camera and an imaging type photoplethysmography device, the millimeter-wave radar is installed in the center of the top of a monitoring channel, and the infrared camera is installed on the imaging type photoplethysmography device; the infrared cameras are symmetrically installed on the two sides of the channel, the imaging type photoplethysmography devices are integrated in the infrared cameras and used for synchronously collecting the core body temperature and the oxyhemoglobin saturation, and the integrated imaging type photoplethysmography devices focus on the forehead and cheek areas through the RGB channel. Reflected light intensity fluctuation signals caused by blood vessel blood volume changes are collected. Through cooperation of the three devices, non-contact synchronous collection of multiple parameters can be completed without deliberate cooperation of workers, comprehensive and continuous basic data are provided for subsequent physical and psychological health analysis, the wellhead passing efficiency is improved, and the cross infection risk of contact type devices is avoided.

The invention provides a control method for a pet washing and protecting machine and an intelligent pet washing and protecting machine, and the control method for the pet washing and protecting machine comprises the steps: determining a washing and protecting mode, a washing and protecting liquid formula and a drying mode which are matched with a target pet in combination with body parameters and health parameters of the target pet; according to the method, the target pet is cleaned and nursed, and adaptive adjustment is performed according to the emotion of the pet in the cleaning and nurse process, so that personalized and refined cleaning and drying requirements of different pets and pets under different health conditions and emotion conditions can be met, and the physical and psychological health of the pets is guaranteed while the cleaning and nurse effect is improved.

The utility model provides concentration equipment for gelatin extracting solution, which relates to the technical field of gelatin extraction and comprises a gelatin concentration tank, an inner container is arranged inside the gelatin concentration tank, two fixing frames are arranged on the outer side of the gelatin concentration tank, a vacuum pump is arranged at the top of each fixing frame, and a vacuum pump is arranged at the bottom of each fixing frame. And a treatment bin is arranged at the top of the other fixing frame. According to the gelatin concentration device, steam in the gelatin concentration tank can be sucked out through the steam inlet pipe by virtue of the vacuum pump and is conveyed into the treatment bin by virtue of the connecting pipe, and peculiar smell molecules and impurities in the steam can be adsorbed and filtered by virtue of the activated carbon filter plate; the activated carbon filter plate can be taken out of the treatment bin to be cleaned or replaced by grabbing and pulling a pull ring, purified steam can be controlled to be discharged from a discharge pipe through a first control valve, and therefore efficient filtration and purification of the steam are achieved, large-scale air pollution is avoided, and the physical and psychological health of workers is guaranteed; and actual use requirements are met.

The invention discloses a psychological health support dialogue generation method based on multi-modal input. The method comprises the steps that 1, text data are acquired and preprocessed; 2, calculating context similarity to obtain a high-quality example set; 3, extracting key visual clues from the image by utilizing a visual language model, wherein the key visual clues comprise facial expressions, actions and emotions; 4, integrating an external common knowledge base to understand the emotion and intention of the user; 5, generating a consultation strategy by using a large language model; and 6, dialogue generation in combination with the context. According to the method, the visual information, the dialogue context and the consultation strategy are comprehensively utilized, the performance of a large language model in the psychological health dialogue is enhanced, more detailed and similar supportive replies can be generated, the emotion recognition and response quality are remarkably improved, the emotion state of a user can be comprehensively understood and responded, and the user experience is improved. Therefore, the mental health support effect is effectively improved.

The invention, which relates to the technical field of artificial intelligence, discloses an AI-driven student behavior analysis and early warning system comprising a behavior data acquisition module, a behavior feature extraction module, a psychological analysis module, a psychological pre-diagnosis module, a psychological health management module and a map drawing module. The behavior data acquisition module acquires physiological and spatial trajectory data of students, the behavior feature extraction module extracts physiological rhythm and behavior pattern features, the psychological analysis module analyzes psychological stress indexes and social behavior abnormality, and the psychological pre-diagnosis module generates psychological health risk indexes. The psychological health management module implements graded early warning and closed-loop feedback, and the map drawing module performs multi-dimensional visual monitoring. Through the multi-modal data fusion and deep learning technology, real-time accurate analysis of student behaviors is realized, the early warning threshold is dynamically optimized, multi-dimensional visual support is provided, the psychological health problem recognition accuracy is remarkably improved, education managers are assisted in timely intervention, and physical and psychological health development of students is promoted.

The present invention relates to the field of health management technology, and in particular to a psychological risk warning method and system based on user multi-dimensional emotional data. The method comprises the following steps: obtaining user multi-dimensional emotional data corresponding to user emotional expression images, user emotional voice signals, and user emotional physiological indicators, and performing multi-source time series alignment processing on the user multi-dimensional emotional data to obtain user multi-dimensional time series synchronized emotional data; constructing a three-level emotional feature extraction architecture and performing modal emotional feature extraction and cross-modal fusion to obtain user cross-modal fusion psychological features; obtaining user environmental data and user social network data, and performing emotional psychological dynamic graph reasoning and psychological risk warning on the user cross-modal fusion psychological features based on the user environmental data and user social network data to generate corresponding user emotional psychological risk warning response measures. The present invention can achieve early identification and intervention of mental health problems.

The invention relates to the technical field of mental health management, in particular to a mental health intervention effect assessment method and system. Collecting demographic basic information, psychological health problem types, psychological state improvement evaluation values before and after intervention and intervention feature information of each intervention object; for any intervention object, screening a historical intervention object matched with the intervention object from a historical intervention database as a reference object according to the demographic basic information, the psychological health problem type and the intervention feature information of the intervention object; calculating a deviation difference value between the psychological state improvement evaluation value of the intervention object and the psychological state improvement evaluation value of the reference object, comparing and judging the deviation difference value based on a preset deviation threshold value, and judging whether the intervention object is a suspected effect abnormal object or not; and for any suspected effect abnormal object, obtaining intervention behavior data during the intervention period.

The present application discloses a healthy elderly care system, method, storage medium and computer program product, which relates to the field of elderly care service technology. The healthy elderly care system includes a user information collection module for collecting user voice information and user visual information, a voice analysis agent for performing voice analysis on user voice information, a visual analysis agent for performing visual analysis on user visual information, a mental health agent for performing mental health analysis based on the voice analysis results and the visual analysis results, a central control agent for generating digital human interaction information by integrating the analysis results of each agent, and a digital human interaction module for outputting digital human interaction information. The present application can effectively monitor and improve the mental health of the elderly.

The invention discloses a DeepSeek-based financial service intelligent decision-making system and method, relates to the technical field of financial science and technology, and solves the problems that complex fraud modes such as emotion stress are difficult to recognize and real-time intervention of relatives during fraud is lacked to be considered in the prior art, so that fraud success cases occur frequently, and the fraud efficiency is high. The fund loss and the physical and psychological health damage of the old people are caused. A behavior abnormity coefficient is generated according to personnel data; generating an association degree score according to the behavior data and the association data; a decision scheme and an alarm signal are generated according to the behavior abnormal coefficient and the correlation degree score, the real emotional state of the elderly and multi-source data such as behavior data are considered in real time to accurately recognize the abnormal behavior, and the abnormal threshold is dynamically adjusted to accurately judge the fraud behavior. Meanwhile, dynamic decision making and early warning are conducted according to the association degree between the contact person and the user, the number of times of occurrence of fraud success events is reduced, funds of the user are saved, and meanwhile the physical and psychological health of the user is maintained.

The invention relates to the technical field of psychological health monitoring, and particularly discloses a psychological risk dynamic assessment system and method based on sleep HRV characteristics, the system comprises a signal acquisition module, the signal acquisition module is integrated with a piezoelectric film sensor, a signal amplification circuit, an MCU and a data transmission module, the piezoelectric film sensor is arranged under a mattress, and the signal amplification circuit is connected with the signal acquisition module; the signal acquisition module is used for acquiring a BCG signal; the data processing module is used for processing the BCG signal and converting the BCG signal into an HRV (High Rate Voltage) characteristic value; the risk assessment module is used for generating a dynamic psychological risk assessment result through an HRV-emotion index mapping model on the basis of the HRV feature value; the user interaction module is used for displaying the dynamic psychological risk assessment result through a man-machine interaction device; bCG signals during sleep are collected in a non-contact mode, and HRV features are extracted based on the BCG signals, so that dynamic assessment of psychological risks is achieved.

There is provided a processor configured for executing code external to a ELM and interfacing with the ELM for: grounding the ELM to predefined disorder specific structured flows, determining a differential diagnosis of mental disorders, for a first mental disorder, generating prompts for guiding the ELM for conducting a structured interview with the subject via a user interface according to a first disorder specific structured flow, dynamically analyzing a response(s) for determining inconsistency with the first mental disorder and determining consistency with a second mental disorder, dynamically switching from the first disorder specific structured flow defined for the first mental disorder to a second disorder specific structured flow defined for the second mental disorder for guiding the ELM, and iterating the generating prompts for guiding the ELM, the dynamically analyzing, and the dynamically switching, for obtaining the differential diagnosis of mental disorders that the individual is most likely suffering from.

Provided herein are compounds of Formula (I), Formula (II), and Formula (III) or pharmaceutically acceptable salt thereof, wherein R1, R2, R3, R4, R5, R6, R7 and A are defined herein. Also provided herein are pharmaceutical compositions comprising a compound of Formula (I), Formula (II), or Formula (III) and methods of using a compound of Formula (I), Formula (II), or Formula (II), e.g., in the treatment of a mental health disease or disorder.