260 results about "Mental well-being" patented technology

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 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 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 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 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.

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 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.

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.

The invention discloses a feature analysis and prediction method for drawing a depression patient based on a convolutional neural network, and belongs to the field of artificial intelligence and mental health. The method comprises the following steps: acquiring drawing image data of a depression patient and a healthy control group, and constructing a standardized data set; preprocessing such as denoising, size normalization and edge enhancement is carried out on the image; constructing a convolutional neural network model comprising four convolutional layers, two pooling layers and a feature fusion layer, and automatically extracting key features such as line definition, spatial layout and detail richness by adopting a variable receptive field mechanism and an attention module; correlation analysis is carried out in combination with scores of a clinical evaluation scale, and the model is optimized through transfer learning and a focus loss function; and generating a risk assessment report. According to the method, objectivity and accuracy of depression screening are remarkably improved, subjective evaluation deviation is reduced, visual decision support is provided for clinicians, and early recognition and intervention of depression are achieved.

A method for predicting early onset of mental health issues in work environments using artificial intelligence (AI) comprising: a) collecting data from two sources: (1) derived from employee survey an

To provide a mental care system that resolves mental health issues as early as possible, ideally before they become serious illnesses. [Solution] The present invention is a mental care system comprising: a reception unit that receives journaling input from a user; a storage device that stores the journaling input made by the user to the reception unit in chronological order in association with the user so that only the user can access it; and an AI diagnostic unit that uses AI to diagnose the user's mental state based on the journaling input stored in the storage device.

The invention relates to the technical field of intelligent monitoring of mental health, and discloses a social function prediction system for teenager patients with depression. The system comprises a multi-source data acquisition module, a psychological feature fusion module and a prediction engine module. The multi-source data acquisition module is used for acquiring social interaction records of patients, constructing behavior dynamic characteristics, performing mode recognition and generating behavior mode data. And the psychological feature fusion module performs difference comparison on the behavior data and a healthy group benchmark, screens key behavior indexes, and calculates correlation degree values of the key behavior indexes and psychological assessment parameters in real time. The prediction engine module evaluates the social function degradation type based on the correlation degree, locates vulnerable nodes in the development process, and identifies specific dysfunction points. According to the system, continuous dynamic monitoring of social function states is realized, early warning of degradation risks and accurate positioning of weak links are realized through behavior-psychological correlation analysis, and technical support is provided for targeted intervention.

The present application relates to the technical field of mental health information monitoring, and discloses a method for dynamically monitoring the mental health state of college students based on side-writing data, which comprises the following steps: collecting multi-source side-writing data of college students within discrete time slices; obtaining calendar events, course arrangements and time period information and constructing a context vector; generating expected side-writing features through a context alignment counterfactual digital twin model based on historical summary states and the context vector; calculating counterfactual residuals according to actual side-writing features and expected side-writing features; dividing the feature domain and accumulating the time window to construct a drift evidence chain; combining uncertainty indicators to obtain risk intensity and risk level through risk inference; calculating the remaining stable period according to the risk intensity change trend; and executing active micro-measurement and updating the stable period sample set or risk inference parameters when the trigger condition is met. Through the joint modeling mechanism of constructing side-writing feature vectors and context vectors, semantic bias caused by independent analysis of single behavior features is avoided.

The development process of the elderly psychological health attainment evaluation tool provided by the embodiment of the invention comprises the following steps: carrying out literature review and quality research, and preliminarily combing dimensions and an entry pool of the elderly psychological health attainment evaluation tool; an expert consultation method is adopted, and the dimensionality and the entry pool of the elderly psychological health attainment evaluation tool are improved; writing a first draft of a scale according to the perfected dimensions and entries, and modifying and optimizing the scale to form a first version of elderly mental health attainment evaluation tool; and based on the modified and optimized scale, measuring indexes of the elderly psychological health attainment evaluation tool are detected, and a formal elderly psychological health attainment evaluation tool is formed. According to the method, a COSMIN standard is used as a methodology basis, and a new concept framework of mental health attainment is used as a basis for compiling evaluation tool entries. Through scientific question design and data collection and analysis methods, scientificity and reliability of the evaluation tool are ensured. The entry in the invention adopts an expression mode suitable for the cognitive level of the elderly, so that the elderly can accurately understand the evaluation content.

This invention relates to the field of physical and mental health monitoring technology, specifically to methods for assessing physical and mental health, methods for recommending exercise, and systems for supporting physical and mental growth. When assessing an individual's physical and mental health, this invention considers not only psychological health levels and physical indicators but also the individual's physical activity status. Since physical activity status is objective data, and existing research has shown a close correlation between physical activity and physical and mental health, this invention uses quantifiable and health-related objective data on physical activity status to assess physical and mental health, thereby improving the accuracy of the assessment results. Furthermore, this invention recommends exercise programs based on the assessment results of physical and mental health, ensuring that the recommended exercises can improve the individual's physical and mental health.

The present invention relates to a depression disease management system based on targeted metabolomics and machine learning models. The system comprises an intelligent diagnostic model module, a differential diagnostic model module, a hierarchical diagnostic model module, a companion diagnostic model module, a treatment endpoint outcome prediction model module, a relapse prediction model module, and a comprehensive judgment module. The system provided by the present invention can address current clinical challenges in depression diagnosis, treatment outcomes, and relapse, enabling accurate diagnosis, timely treatment, and improving the mental health of the general population.

The system according to this embodiment aims to improve the user's mental health by analyzing the user's emotions and responding to them individually. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a transmission unit, and an optimization unit. The analysis unit analyzes the user's emotions. The provision unit provides advice based on the emotions analyzed by the analysis unit. The transmission unit sends messages to increase motivation. The optimization unit optimizes the user's schedule.

Provided are digitally-implemented methods of treating a mental health issue in a subject. The methods include digitally delivering a standardized assessment to the subject; analyzing data collected from the standardized assessment; identifying an evidence-based treatment pathway based upon the analysis of the data; and administering the evidence-based treatment pathway to the subject.

A system and method for physical and mental health monitoring and service, comprising: at least one micro wearable sign signal acquisition device that acquires and / or computes at least the following data of a user: heart rate, exercise, blood oxygen, and PPG, the collected and / or calculated data is sent to the user side APP (200) in the form of original data and / or processed data; the user side APP (200) is used for analyzing the received data; and the data and server side (300) can communicate with the user side APP (200) to provide data and services. The system guides a user to perform specific tests through a micro wearable sign signal acquisition device and / or a user side APP (200), and performs multi-sign data fusion analysis through the user side APP (200) based on data acquired and / or calculated during the specific tests.

The invention relates to the technical field of artificial intelligence and psychological health assessment, and particularly discloses a psychological health longitudinal tracking and effect prediction method based on a time sequence, which reflects the change process of the psychological state of an individual by utilizing longitudinal time sequence data in data acquisition and processing, and predicts the psychological state of the individual. In result prediction, future psychological health trend prediction is realized through a time sequence model, accuracy of diagnosis and intervention evaluation is improved by fusing subjectivity such as scale, inquiry and objectivity such as cognitive test, physiological signal data and past examination reports, and the method is suitable for various application scenes such as clinical follow-up visit, school psychological health management and online psychological counseling platforms.

The invention discloses a mental health index intelligent monitoring device which structurally comprises a main body, pulleys, a keyboard, a display screen and a camera, the pulleys are arranged at the bottom of the main body, the keyboard is installed on the upper end face of the main body, the display screen is installed on the top of the main body, and the camera is arranged at the rear end of the top of the display screen; the main body comprises a monitoring device, a computer host, a radiator, a loudspeaker and a voice recognition system, the monitoring device is installed on the right side in the main body, the computer host is arranged at the bottom end in the main body, the radiator is located on the left side in the main body, the loudspeaker is arranged at the front end of the main body, and the voice recognition system is arranged at the front end of the main body; according to the invention, the camera is utilized to identify the information of the student and record the information, the student can wear the monitoring bracelet to monitor the heart rate of the student during question and answer communication, and the psychological health index of the student is calculated through artificial intelligence, so that the psychological health index monitoring efficiency of the student is improved.

This invention relates to the field of natural language processing technology and proposes a psychological and medical graph retrieval method based on an enhanced large model. The method includes: extracting topic entities using a large language model based on user input questions; obtaining user intent tags using a lightweight large model; controlling the relation filtering and path expansion process of the knowledge graph based on user intent tags to construct a semantic path set; determining whether the semantic path set has sufficient semantic support to answer the user question; and outputting the answer based on the user input question and the semantic path set when the determination is "yes". This invention, based on existing reasoning frameworks, introduces a finely tuned lightweight large language model to perform multi-level semantic intent recognition of user questions and controls the relation path selection process in the knowledge graph accordingly. This replaces the original framework's mechanism of relying on a large language model to score and rank paths hop by hop, ensuring that users can obtain scientific and complete mental health knowledge and improving the reliability of mental health services.

A kind of milk regulator, the milk regulator includes: base assembly, half-enclosed with a heating assembly is fixed, so that the top surface of heating assembly is exposed to base assembly outside;Water jug assembly is placed on base assembly, or mutually separate with base assembly;When the water jug assembly is placed on base assembly, the bottom surface of water jug assembly is closely combined with the top surface of heating assembly.The utility model discloses by the gravity of heating assembly is transferred to base assembly, the weight of water jug assembly is effectively reduced, to effectively improve the convenience of product use.Meanwhile, by the liquid in heating assembly and water jug non-direct contact, not only heating and heat transfer more evenly, but also will not produce piercing noise, to greatly reduce the negative influence of user's mental health.

The invention relates to an elderly self-care ability assessment method and system, electronic equipment and a storage medium, and relates to the technical field of smart pension, and the self-care ability assessment method comprises the steps: obtaining the physical condition of a supervision object according to a physical examination report and a figure posture image, and generating a body health state table; according to the psychological health test table, the home audio set and the key behavior mode, quantifying the psychological state of the supervised object to obtain a psychological health state table; according to the life behavior indexes, daily life habits of the supervision object are obtained, and a social health state table is generated; according to the body health state table, the psychological health state table and the social health state table, constructing personal portrait information of the supervised object in combination with drug compliance; adopting a self-care evaluation model to judge the self-care ability level of the supervised object according to the personal portrait information; the overall evaluation process is simplified, the information evaluation efficiency and the evaluation accuracy are improved, and the personalized service matching accuracy is improved.

Methods and related compositions using Dichrostachys glomerata to provide a variety of health and wellness benefits in a mammal. More specifically, methods and compositions using Dichrostachys glomerata to reduce anxiety, food cravings, stress and improve mood, physical health, mental health, energy and happiness and improve other quality of life factors in one or more mammals, are provided. In one embodiment, a composition for improving mood in a mammal is provided, wherein the composition comprises an effective amount of Dichrostachys glomerata fruit pods provided as an oral dosage unit in the form of a pill, capsule, liquid, lozenge or tablet.