Driver body condition monitoring system based on human-computer interaction
DOI: 10.54254/2755-2721/40/20230628
archive: archived pipeline: cataloged
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Summary
This paper addresses the critical safety issue of driver fatigue, a leading cause of traffic accidents resulting in significant economic and human losses. The study aims to review existing driver fatigue monitoring technologies and propose an innovative multi-modal monitoring system that integrates multiple biosensors to improve detection accuracy. The motivation stems from the limitations of current single-mode systems, which often suffer from invasiveness, discomfort, or insufficient comprehensiveness in assessing driver physiological and cognitive states. The methodology involves a comprehensive review of domestic and international research on driver monitoring. The authors analyze various techniques, including electrocardiogram (ECG), electroencephalogram (EEG), heart rate variability (HRV), eye tracking, facial state analysis, and deep learning approaches such as Convolutional Neural Networks (CNN). Specific systems reviewed include EEG-based software that categorizes brain activity into four stages, HRV-based models using neural networks for in-vehicle alerts, and non-invasive EM-CNN systems that track eye and mouth features. The review also covers Chinese implementations, such as the BYD BAWS system, Volvo’s DAC technology, and 5G-enabled Internet of Vehicles (IoV) systems that use infrared cameras and CCD sensors. Additionally, a deep learning-based facial feature detection system is examined, which utilizes Open-CV and Dlib models to calculate eye and mouth aspect ratios and head Euler angles, achieving up to 97% accuracy in experimental settings. The findings indicate that while individual methods like EEG and eye tracking offer high accuracy, they have distinct limitations. EEG and ECG methods are accurate but invasive, requiring physical contact that may cause driver distress. Eye tracking systems are non-invasive and reliable but face challenges with sampling rates and algorithmic interference. Neural network-based facial analysis is effective but requires large datasets for validation. Current in-vehicle systems in China can monitor physiological signals but lack the capability to effectively intervene in driving behavior unless paired with expensive Level 2 driver assistance systems. The paper concludes that single-mode systems are insufficient for comprehensive monitoring. The significance of this work lies in the proposal of a multi-mode monitoring system that integrates heart rate monitors, skin conductance sensors, eye trackers, and EEG sensors. This integrated approach aims to provide a holistic assessment of driver pressure, cognitive load, fatigue, and emotional state, thereby enhancing safety and personalized assistance. The authors emphasize that while this multi-sensor fusion offers superior accuracy, it faces challenges regarding the complexity of algorithms, individual physiological differences, and the need for rigorous real-world validation. Future research should focus on implementing real-time AI analytics, addressing ethical issues, and developing robust systems that can reliably intervene in driving operations to mitigate fatigue-related accidents.
Provenance
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| 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 | — | — | 4 | 2026-08-23 |
| 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 | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- drowsiness detection algorithms
- dms validation
- distraction detection algorithms
- drowsiness
- gaze based attention detection
- 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: tool software, validation psychometrics