Population
Drivers in real-world driving conditions
Design
Other
Key result
A methodology combining physiological signals, video features, and driving environment parameters achieved high classification accuracy for driver fatigue (88%) and stress (86%).
Authors
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Supports multimodal driver monitoring; leaves open prospective validation before clinical or safety adoption.
A multimodal approach combining physiological, video, and environmental data can accurately detect driver stress and fatigue in real-world conditions.
Rigas et al. (2011) studied Driver stress and fatigue. Combination of physiological signals, video features, and driving environment parameters was evaluated on Classification accuracy for fatigue and stress states. A methodology combining physiological signals, video features, and driving environment parameters achieved high classification accuracy for driver fatigue (88%) and stress (86%).
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