Microsleep And Occupational Safety Integration Of Biometric Detection, Wearable Technology, And Preventive Policies For The Modern Industry

Article Sidebar

PDF
Issue
Vol. 22 No. 2 (2025): Jurnal Kesehatan Lingkungan Volume 22 No. 2, Juli 2025
Published: Aug 18, 2025
Keywords:
Microsleep,
Fatigue Detection,
Wearable Technology,
Occupational Safety,
Fatigue Management

Main Article Content

Muhammad Helmy Ilhamsyah
Master of Applied Occupational Health and Safety, Vocational School, Universitas Gadjah Mada
Savitri Citra Budi
Department of Health Information Services, Vocational School, Universitas Gadjah Mada
Irawadi Prihaswan
Master of Applied Occupational Health and Safety, Vocational School, Universitas Gadjah Mada

Abstract

Microsleep is one of the factors that causes a decrease in vigilance that can have a serious impact on occupational safety, especially in industrial sectors with high risk levels such as heavy equipment and vehicle operations. This condition occurs suddenly and often escapes the detection of conventional surveillance systems, thus increasing the likelihood of work accidents. These challenges are even greater in a work environment with high operational burdens and rotating work patterns that affect the quality of sleep of workers. This study was conducted to evaluate the application of biometric-based detection technology and wearable devices combined with fatigue prevention policies. The study used a systematic literature review method on 25 primary articles published in the period 2020–2024 in a number of indexed international journals. The results of the analysis showed that the integration of real-time biometric monitoring with flexible work policies can increase the accuracy of microsleep detection by up to 85% and help reduce the potential for work accidents. This integrated approach is expected to be able to encourage the transformation of the occupational safety system to be more proactive, adaptive, and data-based, so as to create a safe and sustainable work environment.

Article Details

Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

(CC BY-SA) : This license allows to Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material, for any purpose, even commercially.

The copyright of the received article shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.

Authors are permitted to disseminate published articles by sharing the link/DOI of the article at the journal. Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from the journal with an acknowledgment of initial publication to this journal.

References

Sangeetha SKB, Mathivanan SK, Muthukumaran V, Pughazendi N, Jayagopal P, Uddin MS. A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal. Math Probl Eng. 2023;2023.

Malafeev A, Hertig-Godeschalk A, Schreier DR, Skorucak J, Mathis J, Achermann P. Automatic Detection of Microsleep Episodes With Deep Learning. Front Neurosci. 2021 Mar 24;15.

Zaleha SH, Wahab NHA, Ithnin N, Ahmad J, Zakaria NH, Okereke C, et al. Microsleep Accident Prevention for SMART Vehicle via Image Processing Integrated with Artificial Intelligent. In: Journal of Physics: Conference Series. IOP Publishing Ltd; 2021.

Anniss AM, Young A, O’Driscoll DM. Microsleep assessment enhances interpretation of the Maintenance of Wakefulness Test. Journal of Clinical Sleep Medicine. 2021 Aug 1;17(8):1571–8.

Buriro AB. A Review of EEG-based Prediction of Microsleep. Quaid-e-Awam University Research Journal of Engineering, Science & Technology. 2023 Dec 29;21(2):53–61.

Des Champs de Boishebert L, Pradat P, Bastuji H, Ricordeau F, Gormand F, Le Cam P, et al. Microsleep versus Sleep Onset Latency during Maintenance Wakefulness Tests: Which One Is the Best Marker of Sleepiness? Clocks Sleep. 2021 Jun 1;3(2):259–73.

Makowski S, Prasse P, Jäger LA, Scheffer T. Detection of Drowsiness and Impending Microsleep from Eye Movements. Vol. 1, Proceedings of Machine Learning Research. 2023.

Pham N, Dinh T, Raghebi Z, Kim T, Bui N, Nguyen P, et al. WAKE: A behind-the-ear wearable system for microsleep detection. In: MobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery, Inc; 2020. p. 404–18.

Kumagai H, Kawaguchi K, Sawatari H, Kiyohara Y, Hayashi M, Shiomi T. Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel. Accid Anal Prev. 2023 Jul 1;187.

Skorucak J, Hertig-Godeschalk A, Achermann P, Mathis J, Schreier DR. Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment. Front Neurosci. 2020 Jan 23;14.

Achim Stoltz, Heilbronn, Felix Wulf, Ludwigsburg. METHOD AND APPARATUS FOR RECOGNISING MICROSLEEP IN A DRIVER OF A VEHICLE. 2020.

M.S. Prabhu. Design of Microsleep Alerting System of Pilot to Reduce Air Accidents. International Journal of Engineering and Management Research. 2021 Feb 5;11(1):42–5.

Gaines AR, Morris MB, Gunzelmann G. Fatigue-related aviation mishaps. Aerosp Med Hum Perform. 2020;91(5).

Hertig-Godeschalk A, Skorucak J, Malafeev A, Achermann P, Mathis J, Schreier DR, et al. Microsleep episodes in the borderland between wakefulness and sleep. 2019; Available from: https://academic.oup.com/sleep/advance-article-abstract/doi/10.1093/sleep/zsz163/5536744

Weddell SJ, Ayyagari S, Jones RD. Reservoir Computing Approaches to Microsleep Detection. 2020.

Gandu Eko Julianto Suyoso, Veronika Vestine, Bakhtiyar Hadi Prakoso, Sugeng Hartanto, Rusdiarti. Edukasi Tentang Microsleep Sebagai Upaya Pengurangan Risiko Kecelakaan Kerja pada TRC BPBD Kabupaten Jember. Politeknik Negeri Jember. 2024;

Moorjani AI, Leksmono D, Putranto S. HUBUNGAN ANTARA RAWAN BOSAN DAN KEMUDAHAN TERTIDUR DENGAN MICROSLEEP SAAT MENGEMUDI. Vol. 4, Agustus. 2021 Aug.

Benny Irawan S. Duna, Yunida Iashania, Nuansa Mare Apui Ganang. MANAJEMEN FATIGUE UNTUK MENCEGAH MICROSLEEP PADA DRIVER SARANA. Jurnal Teknik Pertambangan (JTP). 2023 Aug;23.

N. Sulaiman, K. S. Goh, M. Rashid, S. Jadin, M. Mustafa, M. Z. Ibrahim, et al. Offline LabVIEW-Based EEG Signals Analysis to Detect Vehicle Driver Microsleep [Internet]. 2020. Available from: http://www.springer.com/series/11564

Ayyagari SSDP, Jones RD, Weddell SJ. Detection of microsleep states from the EEG: a comparison of feature reduction methods. 2021; Available from: https://doi.org/10.1007/s11517-021-02386-y

Soon CS, Vinogradova K, Ong JL, Calhoun VD, Liu T, Zhou JH, et al. Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes. Neuroimage. 2021 Aug 15;237.

Reynaldi Parulian Firdaus Ariandika Nadapdap, Jaja Muhamad Jabbar, Motris Pamungkas. FAKTOR RISIKO MICROSLEEP PADA DRIVER OJEK ONLINE DI ANTAPANI TAHUN 2023. STIKES Dharma Husada. 2023;

Sawatari H, Kumagai H, Kawaguchi K, Kiyohara Y, Konishi N, Arita A, et al. Risk factors for collisions attributed to microsleep-related behaviors while driving in professional truck drivers. Sci Rep. 2024 Dec 1;14(1).

Hidalgo-Gadea G, Kreuder A, Krajewski J, Vorstius C. Towards better microsleep predictions in fatigued drivers: exploring benefits of personality traits and IQ. Ergonomics. 2021;64(6):778–92.

Adão Martins NR, Annaheim S, Spengler CM, Rossi RM. Fatigue Monitoring Through Wearables: A State-of-the-Art Review. Vol. 12, Frontiers in Physiology. 2021.