2 results about "Dividing attention" patented technology

The invention belongs to the technical field of wisdom education, and discloses a student classroom interest intelligent evaluation system and method based on multi-visual perception, and the system comprises a behavior detection module which employs a YOLOv11 model to recognize key behaviors such as side sitting, desk lying, standing and the like; the attention estimation module judges whether the student focuses on a blackboard or not by using technologies such as head posture estimation, and divides attention states into concentration, distraction and separation; the emotion recognition module evaluates the emotion titer and the awakening degree level of the student based on the facial expression; the identity matching module is used for matching the face image of each student with source data collected before class; the quantitative evaluation module converts the extracted related information into numerical scores, and a final interest score is generated after weighted summation and integration. According to the method, the behavior, attention and emotion characteristics are extracted from the visual data in a non-intrusive and non-contact mode, the classroom interest level of students is automatically evaluated, and valuable technical support is provided for intelligent classroom interest evaluation.

This application relates to a method and system for attention training that combines EEG and behavioral data, belonging to the field of brain-computer interface data processing technology. The method simultaneously collects EEG and behavioral data during attention training tasks, divides continuous EEG time windows based on a unified time reference, and maps behavioral data to corresponding windows; preprocesses the EEG data and calculates a reliability index, extracting attention features to obtain candidate attention values; extracts behavioral attention features to generate behavioral representation values, and performs consistency verification based on the deviation between the two; combines the reliability index and verification results to perform gating correction on the candidate attention values, obtaining fused attention values ​​and dividing attention state segments; adaptively adjusts the training load according to the type and duration of attention state segments; after training, generates evaluation results based on the distribution of attention state segments and changes in behavioral data, determining the initial configuration for the next cycle. This application can improve the stability of attention state recognition in children's scenarios.