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Article | Green Intelligent Systems and Applications | Volume 3 - Issue 2 - 2023 | 98-110 | https://doi.org/10.53623/gisa.v3i2.323
© 2023 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

IoT-based Heart Signal Processing System for Driver Drowsiness Detection

Author(s): Yunidar Yunidar 1 , Melinda Melinda 1 , Khairani Khairani 1 , Muhammad Irhamsyah 1 , Nurlida Basir 2
Author(s) information:
1 Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala, Banda Aceh, Indonesia
2 Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia

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Green Intelligent Systems and Applications

Volume 3 - Issue 2 - 2023

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Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.

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SUBMITTED: 18 September 2023
ACCEPTED: 07 November 2023
PUBLISHED: 26 November 2023
SUBMITTED to ACCEPTED: 50 days
DOI: https://doi.org/10.53623/gisa.v3i2.323

Cite this article
Yunidar, Y., Melinda, M., Khairani, K. ., Irhamsyah, M. ., & Basir, N. . (2023). IoT-based Heart Signal Processing System for Driver Drowsiness Detection. Green Intelligent Systems and Applications, 3(2), 98–110. https://doi.org/10.53623/gisa.v3i2.323
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