341 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 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 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 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.

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 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 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 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 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.

The invention belongs to the technical field of medical treatment, and discloses a wearable intelligent method and device for monitoring the mental health of community old people, and the method comprises the steps: collecting the physiological indexes of the community old people in real time through an intelligent chest card, and achieving the dynamic collection of multi-dimensional data; data are transmitted to the cloud platform through Bluetooth or Wi-Fi, the equipment supports a data caching mechanism, and key data are temporarily stored when connection is abnormal; a dynamic evaluation model is constructed based on AI learning, and a multi-factor decision tree model and a natural language processing NLP algorithm are combined; a group psychological health trend and an environment risk thermodynamic diagram are presented in real time through a chest card APP, main factors influencing psychological health of the old are analyzed, and community managers are assisted in making an intervention plan. By making and applying the community old people mental health monitoring and early warning model, a unified mental health assessment system is provided for old people in different states, the applicability, universality and compliance of mental assessment are improved, home rehabilitation is achieved, and the overall medical cost is reduced.

The invention discloses a method and system for constructing a weak home screening evaluation and personalized intervention model based on a knowledge graph and a large model, and the method comprises the steps: data collection and arrangement: collecting multi-source data related to weak, including physical examination data, life style data, disease data and the like, cleaning, standardizing and integrating the data, and storing the data in a database; forming a structured weakness-related data set; a knowledge graph construction step of constructing a weakness knowledge graph based on the collected data, including entity recognition, relation extraction and knowledge fusion, and establishing a relation between entities by taking concepts, symptoms, risk factors, intervention measures and the like related to weakness as entities; preliminary screening of weakness: performing preliminary screening on the individuals by using an FRAIL scale, and evaluating whether the individuals are in weakness or in the early stage of weakness; asthenia risk factor assessment: further assessing the multi-modal risk factors of individuals with the preliminary screening result of asthenia or the early stage of asthenia, including lifestyle, hearing loss, environmental factors, drug management, mental health, coexistence of chronic diseases and multiple diseases, sleep quality, social isolation, malnutrition and cognitive problems; and making personalized intervention suggestions: providing the personalized intervention suggestions for the individuals according to the evaluation result in combination with intervention measures and individual characteristics in the knowledge graph.

The invention discloses an AI accompanying system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting and preprocessing user data, and generating a standardized data set; s2, performing dimension reduction by adopting diffusion mapping, extracting voice and behavior characteristics, and retaining a state change trend; s3, constructing a behavior knowledge graph based on symbol logical reasoning, and recording symbol state changes; s4, similar states and coping strategies are retrieved by using sparse distributed memory storage symbol representation; s5, combining manifold embedding, historical matching and symbolic reasoning, updating a user state and generating a personalized accompanying strategy; s6, analyzing the voice rhythm and the word frequency, constructing an emotion evaluation model, and generating a personalized emotion intervention scheme; s7, optimizing the knowledge graph by adopting incremental memory, and improving the companion strategy adaptability; and S8, recording feedback, continuously optimizing emotion intervention, and realizing intelligent accompanying. The AI accompanying system improves intelligence, personalization and long-term adaptability of AI accompanying, and is suitable for the fields of old-age care, mental health management and the like.

The invention discloses an electroencephalogram emotion recognition method based on multi-scale convolution and an attention mechanism. The method comprises the steps that firstly, original electroencephalogram signals are processed; secondly, extracting space and frequency features of the electroencephalogram signals by utilizing a feature pyramid network, and capturing multi-level information of local and global brain regions in a multi-scale convolution structure; a multi-scale attention aggregation module is further introduced, and brain region feature self-adaptive weighting is achieved through a parallel space and channel attention mechanism; secondly, global modeling and long-range dependence capture of time domain features are achieved through a Transform coding structure; and finally, outputting an emotion recognition result through a full-connection classifier. According to the method, the time domain, frequency domain and space domain features of the electroencephalogram signals can be extracted at the same time, efficient and accurate emotion classification is achieved, the robustness and universality of electroencephalogram emotion recognition are remarkably improved, and the method can be widely applied to the fields of intelligent human-computer interaction, mental health monitoring, emotion regulation and control and the like.

AI-assisted personalized mental health diagnosis and treatment system, including: a data acquisition module configured to capture multimodal data from speech, text, facial expressions, physiological signals, and user behavior; an AI-based diagnostic module that uses machine learning and natural language processing to assess mental health conditions; a personalized treatment module that generates therapy recommendations, medication suggestions, and lifestyle interventions; a real-time monitoring system that dynamically adjusts treatment based on user data; a clinician support interface that offers AI-driven reports and telemedicine integration; a data protection and security module that ensures compliance with healthcare regulations.

To provide a system for enabling persons who remain following the death of a decedent, to mitigate their feelings of loss and to recover their mental health.SOLUTION: A system includes a learning part, an incorporating part, an instruction part, an interactive part, and a stop part. The learning part generates AI having learned a user's voice, way of speaking, and personality. The incorporating part incorporates the AI generated by the learning part in a tablet terminal. The instruction part instructs delivery of a tablet terminal to a person specified by a user, after the death of the user. The interactive part interacts with a person specified by the user, by using the AI incorporated in the tablet terminal. The stop part stops services according to AI after the lapse of a predetermined period after the death of the user.SELECTED DRAWING: Figure 1

The invention discloses a college student mental health classification and early warning method based on multi-modal deep learning. The method comprises the following steps: firstly, collecting and screening high-quality student data, preprocessing the collected data, then constructing a BCL multi-modal deep learning model, extracting text features by using BERT, capturing multi-scale semantic information through a two-way CNN, fusing the text features and static features, and then connecting a multilayer perceptron through a residual error for classification. An integrated learning strategy is adopted, models are trained for five conversation topics respectively, prediction results are fused through an adaptive weight mechanism, and classification accuracy and robustness are improved; and finally, deploying the model, generating a mental health state classification result, performing early warning on high-risk students, and providing intervention suggestions. Through fusion of talk text records and personal background data features of the counselor and the students, the psychological health states of the students are accurately classified by using a deep learning model, and an effective early warning mechanism is provided.

The invention discloses a user mental health assessment method based on big data and a storage medium, and relates to the technical field of electric digital data processing. The method comprises the following steps: analyzing the fluctuation degree of student data, and judging whether to adjust the data acquisition frequency; analyzing student data grade division conditions and judging whether abnormal grade segmentation adjustment is carried out or not; performing visualization on the student data after the abnormal level segmentation adjustment, analyzing the visualization effect of the student data, and performing visualization effect adjustment; and after the visualization effect adjustment is finished, outputting a student data visualization image, and performing student mental health assessment in combination with the student data visualization image. By analyzing the student data visualization effect and adjusting the visualization effect, the visualization effect of the data fluctuation in the student psychological health assessment process is improved, and the problem that the visualization effect of the data fluctuation in the student psychological health assessment process is poor in the prior art is solved.

To solve a problem relating to mental health management by providing a system for determining a mental health condition, suggesting appropriate measures and generating / recommending content.SOLUTION: A system includes means for: determining a mental health condition; suggesting measures appropriate for an individual; and generating content and recommending the content.SELECTED DRAWING: Figure 1

The invention discloses an emotion recognition method, system and device for child psychological health and a storage medium, and relates to the technical field of psychological health assessment. Obtaining dialogue data between the child and the human-computer interaction interface, and performing natural language and acoustic feature processing on the dialogue data to obtain a current comprehensive emotion value; if the current comprehensive emotion value is smaller than the negative emotion threshold value, marking a time point corresponding to the voice information as an emotion starting moment; acquiring emotional dialogue text information according to the emotional starting moment; identifying the emotional dialogue text information, and calculating a first attribution score and a second attribution score associated with each candidate emotional reason; calculating the first attribution score and the second attribution score to obtain a final attribution score; and determining a core emotion reason according to the final attribution score, and generating an intervention measure based on the current comprehensive emotion value and the core emotion reason so as to intervene the child according to the intervention measure. By implementing the provided technical scheme, the accuracy of children mental health intervention is improved.

To provide a watching system, a remote command device, a robot, and a watching method that can urge users themselves to actively establish communication, thereby preventing the problem especially, loneliness and mental health of old people.SOLUTION: A watching system comprises: an information input unit 18 that receives input of an instruction or information given by a user 1; an information notification unit 14 that notifies the user 1 of predetermined information; a control signal output unit 15 that outputs, to a predetermined control target apparatus 3, a control signal for controlling the control target apparatus 3; and a control unit (first control unit 10). After the control signal output unit 15 outputs the control signal to the control target apparatus 3, the control unit inquires the user 1 about the state of response from the control target apparatus 3 through the information notification unit 14 on the basis of the instruction from the user 1 input to the information input unit 18.SELECTED DRAWING: Figure 3

The invention provides a comprehensive guidance system for the employment direction of college students, and relates to the technical field of employment guidance. The comprehensive guidance system for the employment direction of the college students comprises a three-dimensional evaluation and digital portrait generation module, a dynamic employment matching and path planning module, a full-period closed-loop service platform and a cloud computing and data security system. According to the invention, through deep fusion of three-dimensional evaluation (academic, vocational ability and mental health) and a dynamic employment matching engine, a student employment adaptation degree digital portrait is constructed. The system is in real-time butt joint with employment market dynamics and enterprise demands, a bidirectional dynamic weight neural network is adopted to generate priority post recommendation and medium and long term development paths, and efficient cooperation of counselers, students and enterprises is realized through a full-period closed-loop service platform. According to the system, the problems of insufficient individuation, data islands and low efficiency of cross-role cooperation in the prior art are solved, and the employment guidance accuracy, timeliness and sustainability are remarkably improved.

Devices, systems, and methods herein relate to evaluating and predicting sleep-related disorders. In some variations, a method of graphically representing health of a user may include the steps of: outputting, using a computing device, a notification for a user to input a sleep score, a physical health score, and a mental health score; and generating a user health graph on a graphical user interface of the computing device. For example, a plurality of shapes including respective colors may be generated based on respective sleep scores, physical health scores, and mental health scores. Positions of the plurality of shapes relative to a first longitudinal axis of the user health graph may be arranged based on a sleep score, a physical health score, and a mental health score.