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Published on: August 8, 2019
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Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: A systematic
Christopher N Watling1, Md Mahmudul Hasan1, Grégoire S Larue1
1Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia.
Accident; Analysis and Prevention
|December 7, 2020
Summary
Driver sleepiness detection systems show varied performance. A multi-signal approach appears more consistent than single signals, but overall, significant improvements are needed for on-road deployment.
Area of Science:
- Physiological monitoring
- Road safety engineering
- Machine learning applications
Background:
- Driver sleepiness is a significant cause of road accidents.
- Monitoring driver arousal levels can potentially reduce sleep-related crashes.
- Current driver sleepiness detection systems require evaluation for effectiveness.
Purpose of the Study:
- To systematically review the sensitivity and specificity of driver sleepiness detection systems.
- To assess the performance variations based on different system approaches (mono-signal vs. poly-signal).
- To identify limitations and areas for improvement in current physiological-based systems.
Main Methods:
- Systematic review of 21 studies meeting inclusion criteria.
- Analysis of sensitivity and specificity outcomes from reviewed studies.
- Comparison of performance metrics between mono-signal and poly-signal approaches.
Main Results:
- Sensitivity outcomes ranged from 39.0% to 98.8%; specificity outcomes ranged from 73.0% to 98.9%.
- Poly-signal approaches demonstrated greater consistency and higher sensitivity/specificity compared to mono-signal approaches.
- Increased features did not consistently improve system sensitivity or specificity; only six studies exceeded 90% in both metrics.
Conclusions:
- Considerable variability exists in current driver sleepiness detection systems regarding ground truth, features, and machine learning methods.
- Physiological-based driver sleepiness detection systems require substantial advancement and revalidation before safe on-road deployment.
- Multi-signal approaches show promise but further research and standardization are essential.
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