Smart Driver Drowsiness Detection Using LSTM Technology Based on Heart Rate Monitoring

Jon Endri; Inas Afifah; Suroso Suroso; Rumiasi Rumiasi; Carlos Rs; Ade Silvia Handayani

doi:10.2991/978-94-6463-678-9_31

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Smart Driver Drowsiness Detection Using LSTM Technology Based on Heart Rate Monitoring

Authors

Jon Endri¹, Inas Afifah¹, Suroso Suroso¹, Rumiasi Rumiasi¹, Carlos Rs¹, Ade Silvia Handayani¹^{, *}

¹Politeknik Negeri Sriwijaya, Palembang, 30139, Indonesia

^*Corresponding author. Email: ade_silvia@polsri.ac.id

Corresponding Author

Ade Silvia Handayani

Available Online 1 May 2025.

DOI: 10.2991/978-94-6463-678-9_31 How to use a DOI?
Keywords: driver drowsiness detection; long short-term memory; heart rate monitoring; microsleep
Abstract: This study presents the development of a smart driver drowsiness detection system using Long Short-Term Memory (LSTM) technology based on heart rate monitoring. Traffic accidents are frequently caused by factors such as drowsiness, sleep deprivation, alcohol consumption, or drug use. Among these, microsleep—a brief and involuntary lapse into sleep lasting a few seconds—poses significant risks, often resulting in fatal consequences while driving. The objective of this research is to implement the LSTM algorithm as an early warning system for microsleep in drivers and to develop a drowsiness detection device equipped with a pulse heart rate sensor. The LSTM algorithm, known for its ability to model long-term dependencies, has proven highly effective in time series prediction. It is utilized here for real-time analysis of drivers’ heart rate data.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 8th FIRST 2024 International Conference on Global Innovations (FIRST-ESCSI 2024 )
Series: Advances in Engineering Research
Publication Date: 1 May 2025
ISBN: 978-94-6463-678-9
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-678-9_31 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Jon Endri
AU  - Inas Afifah
AU  - Suroso Suroso
AU  - Rumiasi Rumiasi
AU  - Carlos Rs
AU  - Ade Silvia Handayani
PY  - 2025
DA  - 2025/05/01
TI  - Smart Driver Drowsiness Detection Using LSTM Technology Based on Heart Rate Monitoring
BT  - Proceedings of the 8th FIRST 2024 International Conference on Global Innovations (FIRST-ESCSI 2024 )
PB  - Atlantis Press
SP  - 332
EP  - 340
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-678-9_31
DO  - 10.2991/978-94-6463-678-9_31
ID  - Endri2025
ER  -

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