A Framework for Recognizing and Estimating Human Concentration Levels
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 23 Apr 2021.
Builds on
5 of 13 references.
References · 5
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
- 2016Convolutional Pose Machinescited by 325
- 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
Built on by
Not cited yet.