181 results about "Mental well-being" patented technology

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 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 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 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.

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.

The invention discloses a method for dynamically monitoring emotion of a user by using wearable equipment and a related product thereof. The method includes detecting at least one physiological signal associated with a user and determining, in a first layer of the method, at least one first emotion associated with the detected at least one physiological signal. The method further includes determining, in a second layer of the method, at least one symptom of the user, wherein the at least one symptom is based on the at least one first emotion. The method further includes determining, in a third layer of the method, at least one mental health indicator associated with a mental health of the user, wherein the at least one mental health indicator is based on the at least one first emotion and the at least one symptom.

The application discloses a depression disorder risk assessment method combining heart rate variability and behavior data, relates to the field of digital mental health, and comprises the following steps: continuously collecting heart rate variability and multidimensional behavior data of a user through a wearable device, fusing the data into a unified feature vector after cleaning, feature extraction and standardization; constructing a depression risk assessment model based on a random forest through weight training and feature screening, optimizing and explaining feature importance by using cross-validation and SHAP value analysis, dividing a risk level according to a risk probability output by the model, and providing an abnormal prompt.The application has the advantages that HRV data and multidimensional behavior data are collected by combining an ECG device and a smart watch, the data are preprocessed and feature screened, and a depression disorder four-level risk assessment is realized based on a random forest model, so that data reliability, assessment accuracy and clinical operability are achieved.

The invention provides a psychological health analysis and intervention method based on multi-Agent collaborative assessment, which comprises the following steps of: shunting information and dialogue contents input by a user through an initial shunting Agent module, and then performing parallel and independent multi-dimensional psychological assessment by using a plurality of different assessment Agent modules. Meanwhile, each assessment Agent module corresponds to a psychological dimension and is provided with a special vertical domain knowledge base, then, structured output results of the assessment Agent modules are received and integrated through the central coordination and treatment agent, a collaborative and unified psychological health assessment report is generated, and finally, based on the assessment report, a psychological health assessment report is established. The treatment intervention Agent module is used for generating a personalized mental health intervention scheme matched with the user, and a precise intervention scheme is provided for the user.

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 system according to this embodiment aims to support workplace communication and employee mental health in real time. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a decision unit, and a proposal unit. The acquisition unit acquires images and voices of employees. The analysis unit analyzes the employee's facial expressions, tone of voice, and language use based on the information acquired by the acquisition unit. The decision unit determines an action in accordance with the analysis results obtained by the analysis unit. The proposal unit proposes the action determined by the decision unit to the employee.

The utility model relates to the technical field of evaluation recording devices, and discloses a teenager mental health evaluation recording device which comprises a stable placing seat, a supporting connecting block is fixed at the top of the stable placing seat, and a plurality of positioning connecting grooves are formed in the top of the supporting connecting block. By arranging the evaluation record adjusting mechanism, the operation is simple and convenient when the horizontal placing positions of the evaluation record placing pad and the paper evaluation record file are adjusted, the use angles of the support connecting pad and the evaluation record placing pad can be adjusted, and the angle adjustment can be performed according to the comfort level of a user; the purpose of being beneficial to teenager mental health evaluation recording is achieved; and by arranging the stabilizing mechanism, after the stable placing seat is placed on the table top, the lower bottom surface of the supporting plate is attached to the table top, a limiting nut on the outer side of a metal threaded rod plays a limiting role, and it can be ensured that the stable placing seat is stably placed on the table top.

This invention discloses an intelligent mental health counseling robot and its service system, comprising a robot body, a robot head, a central processing unit, a voice recognition module, a network signal receiving module, a camera module, and a display module. The robot head is fixedly connected to the upper end of the robot body, and the central processing unit is installed inside the robot head. The system also includes: a water tank installed on the upper end of the robot head, with a delivery pipe installed on the side of the water tank and a start / stop valve fixedly connected to the delivery pipe; and a drain outlet located inside a transfer block, with a blocking rod installed inside the drain outlet. This intelligent mental health counseling robot and its service system can effectively alleviate user fatigue during psychological counseling, preventing users from becoming more agitated after prolonged counseling, and also preventing the robot from overheating during long-term operation.

This invention discloses a dynamic mental health risk early warning assessment and intervention system for geriatric depression, belonging to the field of medical information technology. Specifically, it includes: a data acquisition module for acquiring natural language dialogue content between the user and a smart home assistant, and acquiring standardized psychological scale response data filled in by the user; a dialogue recognition module for separating instruction and response statements from the dialogue content and extracting interaction feature vectors; a scale analysis module for extracting scale feature values ​​from the psychological scale response data; and a risk verification module for establishing a dynamic mapping relationship between the interaction feature vectors and scale feature values, calculating the degree of contradiction, initiating a risk verification process when the degree of contradiction exceeds a preset threshold, integrating the interaction feature vectors and the degree of contradiction index to output a depression risk value, and triggering an intervention command when the risk value continuously exceeds a set threshold. This invention constructs a multi-dimensional assessment system with dual-source mutual verification of objective dialogue interaction data and subjective scale data.

1. The name of the design product: card (sunflower IP character). 2. The use of the design product: used for mental health education. 3. The design points of the design product: in the pattern. 4. The picture or photo that best indicates the design points: front view. 5. The design product is a flat product, and other views except the front view and the rear view are omitted, and there are no design points in other views.

The invention relates to the technical field of data processing, in particular to a dialogue expression recognition system and method based on deep learning, and the method comprises the steps: a multi-modal data synchronous collection module collects original visual and audio data with a time synchronization mark; the preprocessing and data enhancement module performs data enhancement by adopting an adversarial training structure; the feature extraction module realizes interactive calculation of visual features and audio features through a cross-modal attention mechanism; the hierarchical time sequence modeling module calls a historical emotion feature mode stored in an external memory unit and performs time sequence modeling in combination with a meta-learning strategy; and the emotional state continuous decision-making module outputs an emotional state track by adopting a sequence decision-making algorithm. According to the method, the problem that the dynamic evolution modeling ability of the subtle expressions in the time sequence dimension is insufficient in the prior art is solved, and continuous and accurate tracking of the emotional state in multiple rounds of psychological health dialogues is realized.