Wearable biosensor monitoring of driver fatigue in intelligent transport systems: A systematic review and IoT framework
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Vol. 11 No. 1 (2026)
Keywords:
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driver fatigue detection, wearable biosensors, electroencephalography (EEG), heart rate variability (HRV), IoT intelligent transport systems, machine learning physiological monitoring, obstructive sleep apnea
Abstract
Purpose. Road traffic fatalities claim approximately 1.19 million lives annually, with driver fatigue implicated in up to 50% of severe collisions in Europe, yet no published framework simultaneously integrates clinical physiological threshold derivation, wearable biosensor specification, machine learning architecture selection, and Internet of Things deployment within a unified, regulatory-compliant monitoring system. This study addresses that integrative gap by proposing the Physiologically-Grounded Driver Monitoring (PGDM) conceptual IoT framework. Methodology. A systematic literature review adhering to PRISMA 2020 guidelines was conducted across five databases, IEEE Xplore, ScienceDirect, PubMed/PMC, Web of Science, and arXiv, covering 2019 to May 2026. Of 2,847 initial records, 43 peer-reviewed studies met inclusion criteria following quality assessment using the adapted Mixed Methods Appraisal Tool (MMAT v.2018). Four open datasets provided empirical benchmarks: OpenDriver (81 drivers, ~4,600 hours, open-road ECG/IMU), WACHSens (n=62), DD-Database (n=10, EEG/EOG/ECG), and AdVitam (n=346, ECG/EDA/respiration). Results. EEG frontal theta power (4–8 Hz) anticipates behavioural drowsiness by 2–7 minutes; RMSSD below 20 ms and LF/HF above 2.0 constitute validated HRV impairment thresholds; PERCLOS exceeding 70% corresponds to severe drowsiness with 91% sensitivity. Obstructive sleep apnea, affecting 15–78% of professional drivers, is identified as the dominant uncontrolled physiological confound in existing detection algorithms. A hybrid CNN-LSTM-Attention architecture achieves 97.3% accuracy at 15–18 ms edge inference latency; Transformer-based multimodal fusion achieves the lowest cross-subject degradation (5.2 percentage points). Theoretical contribution. The PGDM framework constitutes the first published driver monitoring architecture that derives alert levels directly from validated clinical thresholds and achieves simultaneous compliance with EU GSR 2019/2144, EU AI Act 2024/1689, MDR 2017/745, and GDPR 2016/679, bridging the fragmented engineering, clinical, and regulatory research streams. Practical implications. The PGDM specification provides transport engineers, fleet operators, and regulatory bodies with an actionable, standards-compliant design blueprint for real-time wearable driver monitoring deployable across commercial vehicle fleets. Seven priority research gaps are identified, foremost a prospective cohort study stratifying fatigue algorithms by polysomnography-confirmed OSA status.
Sustainable Development Goals (SDGs): SDG 3: Good Health and Well-Being; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities
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References
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Authors
Vovk, Y., Vovk, O., Holotenko, O., Vovk, Y., & Pientak, M. (2026). Wearable biosensor monitoring of driver fatigue in intelligent transport systems: A systematic review and IoT framework. Journal of Sustainable Development of Transport and Logistics, 11(1), 43–82. https://doi.org/10.14254/jsdtl.2026.11-1.03
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