Operator Fatigue Detection via Analysis of Physiological Indicators Estimated Using Computer Vision

Nikolay Shilov, Walaa Othman, Batol Hamoud

2024

Abstract

The complexity of technical systems today causes an increased cognitive load on their operators. Taking into account that the cost of the operator’s error can be high, it is reasonable to dynamically monitor the operator to detect possible fatigue state. Application of computer vision technologies can be beneficial for this purpose since they do not require any interaction with the operator and use already existing equipment such as cameras. The goal of the presented research is to analyze the possibility to detect the fatigue based on the physiological indicators obtained using computer vision. The analysis includes finding correlations between the physiological indicators and the fatigue state as well as comparing different machine learning models to identify the most promising ones.

Paper Citation

in Harvard Style

Shilov N., Othman W. and Hamoud B. (2024). Operator Fatigue Detection via Analysis of Physiological Indicators Estimated Using Computer Vision. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 422-432. DOI: 10.5220/0012730500003690

in Bibtex Style

@conference{iceis24,
author={Nikolay Shilov and Walaa Othman and Batol Hamoud},
title={Operator Fatigue Detection via Analysis of Physiological Indicators Estimated Using Computer Vision},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2024},
pages={422-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012730500003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Operator Fatigue Detection via Analysis of Physiological Indicators Estimated Using Computer Vision
SN - 978-989-758-692-7
AU - Shilov N.
AU - Othman W.
AU - Hamoud B.
PY - 2024
SP - 422
EP - 432
DO - 10.5220/0012730500003690
PB - SciTePress