536 results about "Mental well-being" patented technology

A method for providing mental health advice, a method for training a deep-learning network and a deep-learning based mental health advisory system using Acceptance and Commitment Therapy (ACT). The method for providing mental health advice comprises the steps of: receiving textual input from a user in a consultation session; processing the textual input by applying a mental health condition relationship to the textual input to identify the mental health status of the user; and providing an output associated with the mental health status of the user.

The invention belongs to the field of artificial intelligence, and provides a multi-modal depression recognition system based on an MFE-CCANNN model, which comprises a data acquisition unit, a data preprocessing unit and an MFE-CCANNN model unit. The data acquisition unit synchronously acquires multi-mode data such as videos, audios, texts and fNIRS when a subject performs the same interview task. The data preprocessing unit comprises a video preprocessing unit, an audio preprocessing unit, a text preprocessing unit and an fNIRS preprocessing unit. The MFE-CCARNN model unit comprises a video, audio, text and fNIRS neural signal feature extraction module, a multi-modal feature fusion module and a classification module, and depression recognition and classification result output are achieved. The system supports four-level depression degree discrimination, is high in recognition precision, portable in deployment, high in interpretability and the like, and is suitable for psychological health screening and clinical auxiliary evaluation scenes.

The invention discloses a multi-modal data fusion student mental health early warning method and system. The method relates to the technical field of property detection, and comprises the following steps: collecting student psychological health data, analyzing the student psychological health data, and comprehensively analyzing, optimizing and adjusting the student psychological health early warning method. The student psychological health data is acquired and processed, the student psychological health data is analyzed, the student psychological health data image quality evaluation index and the student psychological health data fusion network volatility evaluation index are obtained, and the student psychological health early warning method is comprehensively analyzed, optimized and adjusted. The accuracy of the student psychological health early warning data quality is improved, and the problem that the accuracy of the student psychological health early warning data quality is insufficient in the prior art is solved.

Described herein are systems and methods for identifying the severity of a mental health condition or symptoms of same by listening to a human-to-human conversation by receiving conversation data, processing the conversation data to generate a language model output and / or an acoustic model output using one or more language models and / or acoustic models. Further described herein are systems and methods for automatically tracking and providing analytics on self-report questionnaires administered during the conversation.

The invention discloses a psychological state pre-screening system and method based on multi-modal data. The psychological state pre-screening system comprises a data acquisition module, a data preprocessing module, a psychological state analysis module, a psychological state evaluation and screening module and a feedback and early warning module. The system collects multi-modal data such as facial expressions, voices, texts, physiological signals and the like, and performs psychological state analysis by using a deep learning algorithm. The psychological state score is calculated through a multi-modal data fusion algorithm, and feedback or early warning is provided, so that the user or related mechanisms can perform further intervention. According to the method, the accuracy and the real-time performance of psychological state screening can be improved, and the problem of misjudgment of a single data source is avoided. The system can be integrated to a smart phone, a smart bracelet, a computer and other equipment, realizes low-cost and non-perceptual mental health monitoring, is suitable for personal health management, enterprise employee mental monitoring, psychological counseling auxiliary diagnosis and school psychological screening, and is beneficial to early discovery and intervention of mental health problems.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention provides a multi-mode-based student classroom psychological health early warning method and system. The method comprises the steps that the activity type of a current classroom is detected in real time through a plurality of sensors; an individualized participation standard is generated; the participation behaviors of the students in the classroom are monitored in real time, difference analysis is carried out on the participation behaviors and the personalized participation demand model, whether abnormal behaviors exist or not is judged, and if yes, marking is carried out; generating layered mental health early warning according to a difference analysis result; constructing an intelligent feedback model according to the type of the psychological health early warning and the activity type of the classroom, and feeding back the psychological health early warning to the teacher for intervention according to the type of the psychological health early warning; the individualized participation demand model is optimized according to the feedback of the teacher and the subsequent behaviors of the students, so that the model adapts to the long-term performance of the students, and closed-loop optimization is formed. According to the invention, the accuracy of mental health early warning is improved, the classroom management efficiency is improved through an effective intervention mechanism, and the method has a wide application prospect.

The invention discloses a human body physiological signal acquisition and analysis system in an Internet of Things environment. An operation method of the system comprises the following steps: step 1, acquiring a multi-modal physiological signal; 2, intelligent prediction and early warning of the body health state; 3, the adaptability of the system is enhanced through an environment sensing function; 4, mental health analysis is conducted on the user, and the data collection module is used for comprehensively collecting physiological signals and mental health indexes of the user; the intelligent analysis module is used for establishing a prediction model by using artificial intelligence and a machine learning algorithm, analyzing and predicting multi-modal data, and early warning possible health risks in advance; the user service module is used for providing personalized health suggestions and intervention measures according to the physical and mental health conditions of the user and helping the user to improve the health state; the system has the characteristics of comprehensive human body data acquisition and accurate physiological and psychological data analysis.

The invention relates to the field of psychological health, and provides a personalized mind-correcting and pressure-reducing system and method based on multi-modal physiological signals. The system comprises a multi-mode physiological signal acquisition module, an emotion recognition module, a mind content generation module and a virtual / augmented reality presentation module. The acquisition module is used for acquiring and preprocessing various physiological signals by using an advanced sensor; the emotion recognition module establishes a signal and emotion mapping relation according to a deep learning algorithm and classifies the signal and emotion mapping relation; the content generation module generates content according to the emotion and preference of the user by means of a big language model and a retrieval enhancement technology; and the presentation module is used for providing immersive experience by combining Unity 3D with VR / AR (Virtual Reality / Augmented Reality) equipment. The system has the advantages of being capable of accurately monitoring emotions in real time, intelligently matching contents, providing immersion experience and forming closed-loop feedback, having expansibility, achieving the emotion recognition accuracy rate of 95%, and being capable of effectively improving mental health and life quality.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention relates to the technical field of old-age care robots, in particular to a medical monitoring type old-age care robot which comprises a physiological monitoring unit, an air particle regulation and health feedback unit, an emotion and health fusion unit and a nursing auxiliary unit. The physiological monitoring unit is used for collecting physiological data of the old people in real time; the air particle regulation and control unit is used for detecting pollutants and biological particles in the air, analyzing the air quality in real time and automatically regulating air purification equipment or an external ventilation device in the robot body according to the particle concentration in the air and physiological data of the old people; the emotion and health fusion unit analyzes the mental health of the elderly; the nursing auxiliary unit comprises a mechanical arm and a voice control system and assists the old in daily activities. According to the invention, psychological support and emotional care are provided for the elderly in time, the life quality of the elderly is improved, and psychological health problems are effectively prevented.

The invention discloses a multi-mode mental health risk intelligent early identification system and method. On the basis of awareness and voluntary, the system can collect 9 types of data sources in an anonymization mode, a quintuple privacy protection architecture is constructed, and the quintuple privacy protection architecture specifically comprises irreversible code ID generation, national cryptographic algorithm and quantum key dual encryption, federal learning localization processing, block chain evidence storage and dynamic desensitization erasing. The innovation point is that based on a multi-modal fusion model and a dynamic scoring algorithm, a dynamic psychological scale is used for scoring, and graded early warning of psychological health risks is realized. By means of a federated learning framework, continuous optimization and cooperation of parameters of the cross-calibration scale can be realized. On the premise of strictly protecting privacy, early recognition and grading suggestion of student mental health risks can be achieved, medical diagnosis is not involved, and the requirements of GDPR and other data protection laws and regulations are met.

The invention discloses a medical health assessment system and method for old people, and relates to the technical field of internet medical treatment. Comprising a data acquisition and preprocessing module, an initial risk assessment module, a multi-dimensional health assessment module, a dynamic monitoring and feedback adjustment module, a comprehensive decision support module, a health management plan generation module, a system verification and iteration module, an old people interface and interaction module and a safety and privacy protection module. Through a multi-dimensional health assessment model integrating physiological health, psychological health, lifestyle, social participation and environmental factors, the overall health condition of the old people is comprehensively assessed, not only a single health index is concerned, meanwhile, an intelligent clinical decision rule engine is introduced, and the health condition of the old people can be comprehensively assessed according to preset clinical rules and health data of the old people. Decision logic is automatically executed, and health suggestions and treatment schemes are generated.

The invention discloses an elderly psychological state assessment early warning method and system, and relates to the technical field of elderly psychological health monitoring, and the method comprises the steps: collecting electroencephalogram signals of an elderly assessment object in a daily fixed time period, calculating the power spectral density ratio of the electroencephalogram signals, and constructing a personal reference feature set of the elderly assessment object; obtaining a current electroencephalogram signal of the aged evaluation object, calculating a power spectral density ratio of the current electroencephalogram signal, comparing the power spectral density ratio with the personal reference feature set, and determining a short-term fluctuation amplitude and an accumulated deviation amount; and judging the abnormal degree of the psychological state according to the short-term fluctuation amplitude and the accumulated deviation, and generating a graded early warning signal containing a risk grade and a timestamp. According to the invention, the detection accuracy and early warning timeliness of the abnormal psychological state of the elderly are improved, and a further solution is provided for psychological health management of the elderly.

The invention relates to the technical field of artificial intelligence prediction, in particular to an artificial intelligence-based teenager psychological health development trajectory prediction system, which comprises a data acquisition module, an emotion recognition module, a trajectory prediction module, a risk assessment module and an intelligent feedback module, and provides a feature extraction method based on a point cloud structure. Time sequence feature modeling is carried out by combining K nearest neighbor with Transform, and global feature extraction is carried out by using position sensing optimization and Max Pooling, so that key features of the mental health state of the teenagers are extracted more comprehensively; according to the method, a clustering-based bidirectional LSTM is combined with a GRU prediction model, firstly, DBSCAN is used for clustering, different mental health state modes are defined, Bayesian optimization and Bayesian filtering are adopted for improving the prediction precision, and finally more accurate mental health development trajectory prediction is provided.

Provided are deuterated analogs of MDMA, including deuterated empathogens. In some embodiments, such compounds are monoamine releasers or inhibit monoamine transporters. In some aspects, features of the compounds provide stability, such as metabolic stability, and efficacy. Also provided are methods for the preparation of deuterated empathogens and pharmaceutical compositions comprising the same. Methods of using the deuterated empathogens, alone or in combination with other therapeutic agents, are provided. In some embodiments, deuterated empathogens are used to treat CNS disorders, such as mental health conditions and neurodegenerative disorders.

According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.

A computer implemented preventative health care and health intervention application is described. The application includes a scoring system to score an overall health of the member, including breakdowns scoring the member's initial physical health, mental health, and an initial score for the lifestyle of the member. One or more health care providers are assigned to and / or selected by the user. The health care providers assess which health intervention programs are best suited to meet the goals of the member and to improve the score for the member's health. The score is re-evaluated when the member meets a minimum threshold of participation and attendance in the one or more selected health and health intervention programs. The score evaluation includes review of attendance, participation information, data collected from sensors and wearable devices, as well as based on predictive analytics integrated into the preventative health care and health intervention program.

The invention discloses a psychological health education auxiliary system based on big data, and relates to the technical field of psychological health education, the psychological health education auxiliary system uses a camera to collect eye movement data, combines CNN to extract static features and LSTM to capture time sequence changes, improves anxiety detection objectivity, combines embedded online learning, short video watching or classroom scenes, and improves anxiety detection accuracy. Daily data collection is carried out, the anxiety trend is continuously tracked by adopting a dynamic analysis method, and the change of the psychological state is monitored and evaluated in real time; the psychological intervention content can be dynamically adjusted based on anxiety scores and personalized feedback strategies, for example, relaxation training, psychological adjustment videos or emotion evaluation questionnaires are pushed, so that psychological health education is more personalized, and invalid or generalized psychological education information is reduced; in addition, a reinforcement learning algorithm is introduced, recommendation content is continuously optimized through interaction data of a tested object, the intervention effect is improved, and psychological education resources can adapt to dynamic changes of individual psychological states.

The invention discloses a health assessment method and device for an operator, electronic equipment and a storage medium, and the method comprises the steps: collecting the last physiological data and psychological data of the operator before operation, and obtaining first physiological data and first psychological data; performing clustering analysis on other physiological data and psychological data except the last collection of the operator before operation to obtain second physiological data and second psychological data; respectively generating corresponding probability scores according to the first physiological data and the first psychological data; respectively calculating and generating corresponding probability scores according to the second physiological data and the second psychological data; and according to the first physiological health probability score, the first psychological health probability score, the second physiological health probability score, the second psychological health probability score and a preset weight, generating a health score of the operator, and according to the health score, determining a health state of the operator. According to the invention, the method can achieve the precise evaluation of the health state of an operator.

The invention discloses a psychological health full-process intelligent evaluation and intervention system, and mainly relates to the technical field of psychological health evaluation and intervention. Comprising a gamification general measurement module used for collecting multi-modal initial data of a user through multiple gamification tasks; the multi-modal fine screening module is connected with the gamification general survey module and is used for collecting multi-dimensional fine data of the user when the general survey risk score reaches a preset threshold value; the self-adaptive intervention module is connected with the multi-modal fine screening module and used for matching a corresponding intervention scheme according to the risk level and continuously collecting physiological data of the user in the intervention process to dynamically adjust the intervention scheme; and the data interaction and control module is connected with the gamification general measurement module, the multi-mode fine screening module and the self-adaptive intervention module and is used for realizing data transmission and cooperative control among the modules. The method has the beneficial effects that the evaluation precision is remarkably improved, and meanwhile, the intervention response speed is increased.

The invention provides a psychological health auxiliary diagnosis method based on stress response intelligent identification of other evaluation tables, and relates to the technical field of medical information processing and image identification, and the method comprises the steps: carrying out the preprocessing of a plurality of types of stress response information of a user, which is obtained in advance, through an improved dense connection network and a neural network based on multi-kernel cavity convolution, obtaining a plurality of types of stress response information of the user; feature extraction is carried out on the multiple kinds of preprocessed stress reaction information, and physiological features are obtained; performing weighted fusion on the physiological features, constructing a feature matrix, inputting the feature matrix to a 2D selective scanning module, and outputting an auxiliary diagnosis result; and combining an auxiliary diagnosis result with an actual clinical diagnosis result, and carrying out adaptive learning updating and optimization on a predicted correct result. According to the method, the problem of inaccurate judgment on the psychological state of the user in a single mode is solved, and meanwhile, the speed and accuracy of psychological health auxiliary diagnosis are improved.

The invention relates to novel bacteria of the species Lactobacillus plantarum and to compositions comprising Lactobacillus plantarum, and methods and uses of said compositions for preventing and / or treating mental illness, symptoms affecting mental health and / or conditions associated with chronic stress, in a mammal. The invention can be applied in restoring, maintaining, and / or promoting mental (including cognitive) health and has potential applications, inter alia, in the area of dietary and food supplements, medicaments and pharmaceuticals.

The present invention discloses a method for detecting and analyzing mental health based on a large model, and relates to the technical field of electronic digital data processing. The method for detecting and analyzing mental health based on a large model comprises the following steps: extracting features; obtaining a data anomaly evaluation index; and performing anomaly level classification. The present invention obtains student data features by extracting features of student data corresponding to the students to be detected, and then obtains a data fluctuation coefficient by analyzing the data feature parameters of the historical time period, obtains a data anomaly evaluation index by combining the student data features and the data feature parameters obtained by inputting the student data features into a preset model, and finally obtains a data anomaly group by performing an anomaly level classification of the students to be detected in combination with a preset evaluation threshold, thereby improving the effectiveness of data visualization in mental health detection and analysis, and solving the problem of low effectiveness of mental health detection and analysis by data visualization in the prior art.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention relates to the technical field of natural language processing, in particular to a mental health intelligent assessment method and system based on text feature fusion. The method comprises the following steps: acquiring a handwriting image of a subject, carrying out handwritten character recognition based on an OCR (Optical Character Recognition) technology to pre-process the image and text data, and collecting a psychological questionnaire after the handwriting image of the subject; according to the preprocessed image and text data, multi-scale handwriting features are extracted based on an improved MobileVit model, personalized handwriting features are extracted based on standard word stock comparison, text semantic features are extracted based on an improved Bert model, and then cross-modal feature fusion is carried out; cross-modal fusion features are analyzed and predicted through a large-scale pre-training model, and intelligent assessment of mental health is achieved; and in combination with the psychological questionnaire condition, the psychological health intelligent assessment result is verified and evaluated, so that the accuracy and reliability of the assessment result are ensured. According to the invention, intelligent and accurate evaluation of the mental health state of the subject can be realized.

The invention relates to the technical field of artificial intelligence and emotion calculation, and discloses an emotion state evaluation method based on multi-modal data, and the method comprises the steps: obtaining and decoupling an implicit signal, an explicit signal and situation data of an evaluated object; and respectively generating implicit features, explicit features and context vectors through double-flow decoupling coding and context knowledge graph coding. And the core engine determines the internal emotional state and the extrinsic emotional representation in parallel, and dynamically regulates and controls a bridging conversion process from the internal state to the predicted extrinsic representation by utilizing the situation vector. Finally, the emotion regulation intensity is calculated by comparing actual and predicted extrinsic representations, and the emotion regulation intensity, the internal state and the extrinsic representations are jointly used as a multi-dimensional evaluation result to be output. According to the method, the dynamic relationship and the adjustment strategy between the internal feeling and the external expression can be disclosed, a deeper and more three-dimensional analysis view angle is provided for the fields of mental health, human-computer interaction and the like, and the method has remarkable application value.

The present invention discloses a method and system for evaluating students' psychological quality, and relates to the technical field of health data analysis. The entire evaluation system is divided into four parts, namely a health data aggregation module, a health evaluation module, a data storage and analysis module, and a statistical adjustment module. The technical points are as follows: corresponding data are collected according to different types of students, so that the mental health needs of students of different age groups can be more comprehensively understood and supported; based on multiple factors of students' mental health reflected in the corresponding data, comprehensive evaluation is performed through analysis and calculation, so that multi-dimensional information of students' mental health can be more comprehensively captured. When a psychological counseling mechanism is subsequently triggered, it is helpful to improve the efficiency and effect of coping with students' mental health problems; according to the ratio of the number of students who trigger the psychological counseling mechanism to the total number of students in the school, the number of psychological teachers employed is dynamically adjusted, thereby realizing effective allocation and utilization of resources.

Methods, systems, and computer-readable media are provided for managing health status of persons with a chronic condition including providing dynamic, adaptive monitoring, detection, and prediction of suicide risk to a person at risk for suicide related to mental health. In an embodiment, a patient-assessment application is used to obtain information periodically on a patient's mental health status. Based on this information, a logistic regression model is employed to determine a patient's probability of attempting suicide. The probability is evaluated against a default threshold to determine if the patient's status has changed significantly, and if the threshold is exceeded, an action is evoked. In one embodiment, the action includes providing notice to the patient's care provider, caregiver, or case manager.

The invention relates to the technical field of psychological health service and digital twinning, and discloses a psychological health digital twinning construction and dynamic intervention system, which comprises a multi-modal data acquisition module, a psychological digital twinning modeling module, a psychological state prediction module, a dynamic intervention decision module and a feedback optimization module, each module realizes real-time data interaction through a distributed data bus; wherein the multi-modal data acquisition module is responsible for acquiring individual physiological, psychological, behavior and environment multi-dimensional data, and the psychological digital twin modeling module constructs a dynamic virtual mapping body of an individual psychological state based on the data. According to the method, real-time monitoring of the psychological state is realized through multi-modal data acquisition and twinborn dynamic mapping, and the hysteresis of traditional evaluation is overcome; on the basis of a two-stage matching algorithm, interference strategies are screened in combination with user static characteristics and dynamic states, and limitation of a homogenization scheme is avoided.