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Authors: Nikolay Shilov ; Walaa Othman and Batol Hamoud

Affiliation: SPC RAS, 14 Line 39, St.Petersburg, Russia

Keyword(s): Operator, Fatigue Detection, Computer Vision, Physiological Indicator, Machine Learning.

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.

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Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 422-432. DOI: 10.5220/0012730500003690

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Shilov, N.
AU - Othman, W.
AU - Hamoud, B.
PY - 2024
SP - 422
EP - 432
DO - 10.5220/0012730500003690
PB - SciTePress

- Science and Technology Publications, Lda.
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