A Framework for Recognizing and Estimating Human Concentration Levels

TL;DR

This paper presents a framework combining deep neural networks and Kalman filters to accurately estimate subtle human concentration levels in online education using minimal body movement data.

Contribution

It introduces a novel framework that estimates nuanced concentration levels, overcoming previous limitations of discrete classification in online learning contexts.

Findings

Successfully extracted concentration levels from minimal body movement data.

Framework can be used to assist lecturers in real-time monitoring.

Potential to extend the approach to other fields requiring concentration assessment.

Abstract

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.