Detection of Drowsiness and Impending Microsleep from Eye Movements

Silvia Makowski, Paul Prasse, Lena Ann Jäger, Tobias Scheffer
Proceedings of The 2nd Gaze Meets ML workshop, PMLR 226:142-160, 2024.

Abstract

Drowsiness is a contributing factor in an estimated 12% of all road traffic fatalities. It is known that drowsiness directly affects oculomotor control. We therefore investigate whether drowsiness can be detected based on eye movements. To this end, we develop deep neural sequence models that exploit a person’s raw eye-gaze and eye-closure signals to detect drowsiness. We explore three measures of drowsiness ground truth: a widely-used sleepiness self-assessment, reaction time, and impending microsleep in the near future. We find that our sequence models are able to detect drowsiness and outperform a baseline processing established engineered features. We also find that the risk of a microsleep event in the near future can be predicted more accurately than the sleepiness self-assessment or the reaction time. Moreover, a model that has been trained on predicting microsleep also excels at predicting self-assessed sleepiness in a cross-task evaluation, which indicates that upcoming microsleep is a less noisy proxy of the drowsiness ground truth. We investigate the relative contribution of eye-closure and gaze information to the model’s performance. In order to make the topic of drowsiness detection more accessible to the research community, we collect and share eye-gaze data with participants in baseline and sleep-deprived states.

Cite this Paper

BibTeX
@InProceedings{pmlr-v226-makowski24a, title = {Detection of Drowsiness and Impending Microsleep from Eye Movements}, author = {Makowski, Silvia and Prasse, Paul and Ann J\"ager, Lena and Scheffer, Tobias}, booktitle = {Proceedings of The 2nd Gaze Meets ML workshop}, pages = {142--160}, year = {2024}, editor = {Madu Blessing, Amarachi and Wu, Joy and Zario, Danca and Krupinski, Elizabeth and Kashyap, Satyananda and Karargyris, Alexandros}, volume = {226}, series = {Proceedings of Machine Learning Research}, month = {16 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v226/makowski24a/makowski24a.pdf}, url = {https://proceedings.mlr.press/v226/makowski24a.html}, abstract = {Drowsiness is a contributing factor in an estimated 12% of all road traffic fatalities. It is known that drowsiness directly affects oculomotor control. We therefore investigate whether drowsiness can be detected based on eye movements. To this end, we develop deep neural sequence models that exploit a person’s raw eye-gaze and eye-closure signals to detect drowsiness. We explore three measures of drowsiness ground truth: a widely-used sleepiness self-assessment, reaction time, and impending microsleep in the near future. We find that our sequence models are able to detect drowsiness and outperform a baseline processing established engineered features. We also find that the risk of a microsleep event in the near future can be predicted more accurately than the sleepiness self-assessment or the reaction time. Moreover, a model that has been trained on predicting microsleep also excels at predicting self-assessed sleepiness in a cross-task evaluation, which indicates that upcoming microsleep is a less noisy proxy of the drowsiness ground truth. We investigate the relative contribution of eye-closure and gaze information to the model’s performance. In order to make the topic of drowsiness detection more accessible to the research community, we collect and share eye-gaze data with participants in baseline and sleep-deprived states.} }
Endnote
%0 Conference Paper %T Detection of Drowsiness and Impending Microsleep from Eye Movements %A Silvia Makowski %A Paul Prasse %A Lena Ann Jäger %A Tobias Scheffer %B Proceedings of The 2nd Gaze Meets ML workshop %C Proceedings of Machine Learning Research %D 2024 %E Amarachi Madu Blessing %E Joy Wu %E Danca Zario %E Elizabeth Krupinski %E Satyananda Kashyap %E Alexandros Karargyris %F pmlr-v226-makowski24a %I PMLR %P 142--160 %U https://proceedings.mlr.press/v226/makowski24a.html %V 226 %X Drowsiness is a contributing factor in an estimated 12% of all road traffic fatalities. It is known that drowsiness directly affects oculomotor control. We therefore investigate whether drowsiness can be detected based on eye movements. To this end, we develop deep neural sequence models that exploit a person’s raw eye-gaze and eye-closure signals to detect drowsiness. We explore three measures of drowsiness ground truth: a widely-used sleepiness self-assessment, reaction time, and impending microsleep in the near future. We find that our sequence models are able to detect drowsiness and outperform a baseline processing established engineered features. We also find that the risk of a microsleep event in the near future can be predicted more accurately than the sleepiness self-assessment or the reaction time. Moreover, a model that has been trained on predicting microsleep also excels at predicting self-assessed sleepiness in a cross-task evaluation, which indicates that upcoming microsleep is a less noisy proxy of the drowsiness ground truth. We investigate the relative contribution of eye-closure and gaze information to the model’s performance. In order to make the topic of drowsiness detection more accessible to the research community, we collect and share eye-gaze data with participants in baseline and sleep-deprived states.
APA
Makowski, S., Prasse, P., Ann Jäger, L. & Scheffer, T.. (2024). Detection of Drowsiness and Impending Microsleep from Eye Movements. Proceedings of The 2nd Gaze Meets ML workshop, in Proceedings of Machine Learning Research 226:142-160 Available from https://proceedings.mlr.press/v226/makowski24a.html.

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