11 results about "Effective interventions" patented technology

The invention belongs to the technical field of cognitive impairment intervention evaluation, and particularly relates to an evaluation system and method applied to cognitive impairment intervention. According to the method, the cognitive state is evaluated and the training tasks are allocated based on the real data of the user in the natural life scene, so that the one-sidedness of only depending on a laboratory or one-time evaluation is avoided, the training and intervention are closer to the real demand and life situation of the user, and the training efficiency is improved through multi-source data fusion. The influence of single signal noise or accidental fluctuation on judgment is reduced, the robustness and objectivity of evaluation are improved, indexes such as cognitive performance, intervention indexes and deviation degree are numeralized, long-term tracking and transverse comparison of the effects of different intervention measures are facilitated, interpretable quantitative evidences are provided for clinical personnel, and the accuracy of evaluation is improved. Intervention indexes are calculated in real time, intervention measures are automatically adjusted or reselected according to feedback results, and a quick-response closed-loop system is formed, so that the time from problem discovery to effective intervention is shortened, and the intervention conversion rate is increased.

The invention relates to the technical field of personalized intelligent dialogue and digital accompanying for old people, and discloses an AI interaction-based personalized knowledge graph dialogue generation method for old people, which comprises the following steps of: reading a role conversion event record from a family structure sub-graph of a personalized knowledge graph for old people, and generating a role fall feeling feature template; aI dialogue initiation records of the old in recent late night periods are obtained, the number of days of continuous late night dialogues, the average dialogue duration and the active listing frequency are counted, and an aloneness level label is generated; generating a personalized spiral blocking intervention scheme based on the spiral strength coefficient and the spiral type label; and executing the collaborative psychological support dialogue script to generate a dialogue, and outputting a continuous personalized psychological support dialogue sequence. According to the method, the technical challenge that in the AI interaction-based elderly dialogue service, when the psychological state is worsened spirally due to the fact that the sense of fall and the sense of loneliness of roles are enhanced mutually, it is difficult to generate effective intervention dialogues by means of the personalized knowledge graph is solved.

The invention provides a bidirectional emotion interaction head ring system based on a multi-mode brain-computer interface. The bidirectional emotion interaction head ring system based on the multi-mode brain-computer interface comprises an exclusive emotion recognition algorithm module, a bidirectional emotion interaction output module, an intimacy relation synchronization and emotion interaction module, a system control module and a head ring structure. The system control module and head ring structure comprises a master control MCU, a multi-mode fusion processor, a Bluetooth / ultra-low power consumption communication module, an electrode self-adaptive pressurization structure and an ultra-light flexible head ring structure. The bidirectional emotion interaction head ring system based on the multi-mode brain-computer interface has the advantages of being high in monitoring precision, timely and effective in intervention, high in family participation degree, capable of being continuously worn and the like, emotion management is converted into active adjustment from passive recording, scientific, stable and sustainable emotion management support can be provided for a user in all stages, and the user experience is improved. The overall mental health level of the user is improved, and the method has clear practical value and industrial application prospect.

The invention discloses a child overweight and obesity detection method and system based on multi-index cooperative determination, and the method comprises the steps: firstly deploying and initializing a special multi-mode detection tool, and inputting the basic information of a child; and then guiding the integrated device and the portable module for children to acquire physique indexes according to prompts, calling intelligent wearing behavior data, verifying the intelligent wearing behavior data, and storing the intelligent wearing behavior data into a local database of the terminal. Extracting multi-modal data, importing the multi-modal data into a built-in model, matching a WHO standard and a metabolic reference range according to age and gender, assigning differential weights according to growth stages, and correcting and outputting an obesity trend probability basic value in combination with a growth rate. The behavior data is quantified, a risk matrix is established, a cause is positioned, then a report result containing obesity probability, influence factors and early warning levels is generated through integration, and suggestions are given to abnormal children on site. And effective intervention of overweight and obesity of children is facilitated.

This application discloses an emotion intervention system for online learning among the elderly based on emotion analysis, including a recording module, a face recognition module, an emotion feature analysis module, a time-based emotion mapping module, a behavior dynamic transfer module, and an intervention strategy module. The recording module automatically records facial videos of the elderly during online learning to obtain facial emotion data. The face recognition module performs statistical analysis on the facial emotion data of all elderly individuals. This application belongs to the field of emotion intervention technology, and its purpose is to solve the problem that existing technologies cannot accurately capture facial expression data of the elderly in online learning scenarios, leading to low accuracy in subsequent emotion recognition and an inability to provide effective intervention measures. The achieved technical effect is: facilitating accurate capture of facial expression data of the elderly in online learning scenarios, improving the accuracy of subsequent emotion recognition, and enabling the provision of effective intervention measures.

This invention relates to the field of personalized intelligent dialogue and digital companionship technology for the elderly, and discloses a method for generating personalized knowledge graph dialogues for the elderly based on AI interaction. The method includes: reading role transition event records from the family structure subgraph of the elderly's personalized knowledge graph to generate a role loss feature template; obtaining recent AI dialogue initiation records during late-night hours, statistically analyzing the number of consecutive late-night dialogues, average dialogue duration, and frequency of proactive communication to generate loneliness level labels; generating personalized spiral blocking intervention plans based on spiral intensity coefficients and spiral type labels; and executing the collaborative psychological support dialogue script to generate dialogues and output a continuous personalized psychological support dialogue sequence. This invention addresses the technical challenge of generating effective intervention dialogues using personalized knowledge graphs when role loss and loneliness reinforce each other in AI-interactive elderly dialogue services, leading to a spiral deterioration of the psychological state.

The invention discloses a student psychological assessment early warning method and system based on group behavior clustering analysis, and the method comprises the steps: obtaining multi-source campus behavior time series data, extracting group behavior feature vectors, and dynamically dividing a plurality of behavior normal form groups; calculating a multi-dimensional behavior baseline of each behavior normal form group; calculating a group relative deviation degree between the current behavior feature vector of the target student and the multi-dimensional behavior baseline of the behavior normal form group to which the target student belongs; analyzing the continuous increasing trend of the relative deviation degree of the group based on a sliding time window to judge adaptive behavior deviation; and performing attribution analysis on the target student with adaptive behavior deviation, generating a semantic risk early warning signal, and outputting targeted assistance suggestions according to the association intervention strategy library. According to the invention, through dynamic group division and relative deviation analysis, accurate and explainable early warning and effective intervention guidance of student behavior risks are realized.

This invention relates to a surgical critical care auxiliary assessment method based on ensemble machine learning, comprising the following steps: S1: Segmenting and manually labeling historical monitoring and surveillance time-series data; S2: Completing data using random forest regression based on decision trees; S3: Correlation analysis of common indicators for critical care; S4: Establishing and training a preoperative auxiliary assessment model using ensemble machine learning; S5: Assisting physicians in assessing and analyzing preoperative critical care; S6: Clustering intraoperative historical monitoring and surveillance time-series data according to intraoperative feature values; S7: Calculating the optimal monitoring period using a loss function; S8: Calculating the quantification value of critical care severity using the optimal monitoring period; S9: Establishing and training an intraoperative auxiliary assessment model using ensemble machine learning; S10: Assisting physicians in assessing and analyzing intraoperative critical care. This invention can comprehensively and accurately predict the critical care period and severity, which is beneficial for assisting medical personnel in effective intervention.

The application provides a kind of two-way emotional interaction head ring system based on multi-modal brain-computer interface.The two-way emotional interaction head ring system based on multi-modal brain-computer interface includes: exclusive emotion recognition algorithm module, two-way emotional interaction output module, intimate relationship synchronization and emotion interaction module, system control module and head ring structure;The system control module and head ring structure include master control MCU, multi-modal fusion processor, Bluetooth / ultra-low power communication module, electrode adaptive pressure structure, ultra-light flexible head ring structure.The two-way emotional interaction head ring system based on multi-modal brain-computer interface provided by the application has multiple advantages such as high monitoring accuracy, timely and effective intervention, high family participation and continuous wearing, which changes emotion management from passive recording to active adjustment, provides scientific, stable and sustainable emotion management support for users at each stage, improves the overall mental health level of users, and has clear practical value and industrial application prospect.

The invention relates to the technical field of neuroscience, in particular to a modeling method of a post-traumatic stress disorder syndrome disease model. Comprising the following steps: selecting an experimental animal suitable for behavioral and neurobiological research, and enabling an experimental environment to meet experimental standards; a PTSD fear memory repeated reproduction process is simulated, and an animal model highly conforming to disease core symptoms is constructed; a behavioral evaluation system is constructed, model mouse behavior characteristics are quantitatively evaluated in a multi-dimensional mode, and PTSD core symptoms are accurately reflected; by simulating the model of the core symptom repeatedly reproduced by the fear memory of the PTSD, a new treatment target of fear memory disorder diseases such as the PTSD and the like is found, and an effective intervention strategy for treating the core symptom is realized.

The invention relates to the field of artificial intelligence, discloses an AI face emotion perception evaluation and analysis method and system, and aims to solve the problem that an existing student emotion perception system is insufficient in authenticity, continuity, multi-dimensional analysis and effective intervention and conflicts with students. The method comprises the steps that multi-modal student data such as face, voice, limbs and behavior logs are collected and preprocessed, and features are extracted; aggregated emotion vectors are generated through multi-head self-attention network fusion features; and analyzing the emotional state and combining the context to construct a personalized emotional portrait so as to generate an emotional evaluation report and an intervention suggestion. The system has the functions of multi-modal data acquisition, preprocessing, feature extraction, feature fusion, emotional state analysis, context fusion reasoning and report intervention suggestion generation. According to the technical scheme, the authenticity, continuity, accuracy and depth of emotion perception are improved, accurate intervention is provided, and the conflict psychology of students is effectively reduced.