A Framework for Recognizing and Estimating Human Concentration Levels
This addresses the need for more precise concentration estimation in online education to aid lecturers, but it is incremental as it builds on prior discrete classification methods.
The paper tackled the problem of estimating subtle human concentration levels in online education by using a framework combining a Deep Neural Network and Kalman Filter with minimal body movement data, achieving successful extraction of concentration levels.
One of the major tasks in online education is to estimate the concentration levels of each student. Previous studies have a limitation of classifying the levels using discrete states only. The purpose of this paper is to estimate the subtle levels as specified states by using the minimum amount of body movement data. This is done by a framework composed of a Deep Neural Network and Kalman Filter. Using this framework, we successfully extracted the concentration levels, which can be used to aid lecturers and expand to other areas.