Microsleep as a Risk Factor for Unsafe Behavior: A Systematic Literature Review

Ihsan, Elga Alifqiah; Djamalus, Hendra · 2026 · Crossref

DOI: 10.55324/josr.v5i7.3278

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Summary

This systematic literature review investigates microsleep as a critical risk factor for unsafe behavior in occupational and traffic settings. Motivated by the high prevalence of human error in workplace accidents and the limited synthesis of existing research, the study aims to identify populations vulnerable to microsleep, characterize the resulting unsafe behaviors, and evaluate the effectiveness of detection methods. The authors conducted a systematic review following PRISMA 2020 guidelines, analyzing 22 peer-reviewed articles published between 2021 and 2026 from databases including Scopus, PubMed, and Google Scholar. The review addressed three primary research questions regarding vulnerable demographics, specific unsafe behaviors, and the comparative efficacy of subjective versus objective measurement tools. The findings identify several high-risk groups, including drivers, pilots, heavy industry workers, maritime personnel, healthcare professionals, and patients with neurological or sleep disorders such as obstructive sleep apnea and Parkinson’s disease. Vulnerability is driven by factors like prolonged work hours, fatigue, monotony, and high cognitive demands. The review establishes that microsleep directly triggers unsafe behaviors characterized by loss of focus, delayed reaction times, poor decision-making, reduced compliance with safety protocols, and increased accident risk. For instance, in transportation, microsleep leads to loss of vehicle control, while in industrial settings, it correlates with decreased safety participation and operational errors. The study also highlights that psychological distress and production pressure exacerbate these risks, particularly in environments with weak safety climates. Regarding detection methods, the review concludes that objective measures are significantly more effective than subjective self-reports. Objective techniques, including electroencephalography (EEG), electrooculography (EOG), pulse sensors, and computer vision systems integrated with machine learning algorithms, provide real-time, accurate physiological data. Specific studies cited demonstrated high accuracy in detecting microsleep using deep neural networks on EEG signals and sensor-based smart helmets. In contrast, subjective methods like questionnaires are prone to bias and underreporting. The authors conclude that integrating advanced fatigue risk management systems and strengthening organizational safety climates are essential strategies for mitigating the impact of microsleep on occupational safety.

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Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• microsleep
• drowsiness detection algorithms
• drowsiness
• shift work driving
• sleep deprivation
• vigilance

Information type

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• Empirical Findings: physiological data