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Non-intrusive Drowsiness Detection Techniques and Their Application in Detecting Early Dementia in Older Drivers

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 560))

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Abstract

Drowsy drivers cause the most car accidents thus, adopting an efficient drowsiness detection system can alert the driver promptly and precisely which will reduce the numbers of accidents and also save a lot of money. This paper discusses many tactics and methods for drowsy driving warning. The non-intrusive nature of most of the strategies mentioned and contrasted means both vehicular and behavioural techniques are examined here. Thus, the latest strategies are studied and discussed for both groups, together with their benefits and drawbacks. The goal of this review was to identify a practical and low-cost approach for analysing elder drivers’ behaviour.

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Authors and Affiliations

  1. College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Muhammad Tanveer Jan, Ali Hashemi, Jinwoo Jang, Kwangsoo Yang, Jiannan Zhai & Borko Furht

  2. Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, 33431, USA

    David Newman & Ruth Tappen

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  1. Muhammad Tanveer Jan
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Correspondence to Muhammad Tanveer Jan .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Jan, M.T. et al. (2023). Non-intrusive Drowsiness Detection Techniques and Their Application in Detecting Early Dementia in Older Drivers. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 560. Springer, Cham. https://doi.org/10.1007/978-3-031-18458-1_53

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