Physiological-based Driver Monitoring Systems: A Scoping Review
DOI: 10.28991/cej-2022-08-12-020
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Summary
This scoping review addresses the growing need for accurate driver monitoring systems (DMS) to mitigate traffic accidents caused by impaired driver states, such as fatigue, drowsiness, and distraction. While traditional DMS rely on vehicle-based or behavioral data, this paper focuses on physiological-based systems that utilize biosensors to assess a driver’s physical and emotional condition. The study aims to map research published between 2018 and 2022, identifying key physiological indicators, system classifications, and existing research gaps. The motivation stems from the increasing integration of automated driving features, which require continuous monitoring of driver readiness to re-engage with vehicle control. The authors employed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) framework to systematically analyze literature from ScienceDirect and Scopus. Initial searches yielded 2,939 articles, which were screened for eligibility based on language, publication date, and relevance to physiological driver monitoring. After removing duplicates and irrelevant studies, 93 articles met the inclusion criteria. The selected papers were categorized by physiological signals—electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), electromyography (EMG), galvanic skin response (GSR), and photoplethysmography (PPG)—and by application type, including driver identification, alertness, fatigue, and drunk driving detection. Keyword co-occurrence analysis was performed to visualize research themes. The findings highlight distinct applications for various physiological signals. For driver identification, ECG and EMG signals processed through convolutional neural networks achieved high accuracy rates, with combined ECG-EMG models reaching 98.9%. In alertness monitoring, studies demonstrated that electrodermal activity (EDA) and heart rate are reliable predictors of distraction, with extreme gradient boosting (XGB) algorithms showing superior performance in classification tasks. Regarding fatigue, EEG analysis revealed that monotonous driving and noise exposure increase alpha wave activity, indicating declining alertness. EOG signals were noted for their strong signal-to-noise ratio in detecting sleep phases, while EMG served as a gold standard for muscle fatigue, though its intrusive nature limits real-time practicality. The review also identified challenges, including signal noise, the invasiveness of certain sensors like EEG helmets, and the difficulty of distinguishing between similar emotional states using GSR. The significance of this review lies in its comprehensive mapping of physiological DMS advancements over the last five years. It underscores the potential of multi-sensor fusion to enhance detection accuracy and reliability. However, the authors conclude that significant challenges remain in deploying these systems in real-world vehicular contexts, particularly regarding sensor comfort, signal robustness against environmental noise, and the computational complexity of real-time data processing. The study provides a foundation for future research to develop less intrusive, more robust physiological monitoring solutions that can effectively integrate with advanced driver assistance systems.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- drowsiness detection algorithms
- dms validation
- drowsiness
- workload measurement
- distraction detection algorithms
- dms regulation
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics, tool software