Sensing Technologies and Physiological Parameters for Real-Time Driver Drowsiness Detection: A Comprehensive Review.

Area of Science:

• Road Safety
• Biomedical Engineering
• Sensor Technology

Background:

• Driver drowsiness is a major cause of road accidents.
• Effective real-time monitoring systems are crucial for preventing fatigue-related incidents.

Purpose of the Study:

• To review and categorize sensing technologies for driver drowsiness detection.
• To analyze and compare physiological and mechanical sensing methods.

Main Methods:

• Surveyed physiological sensing methods: electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG).
• Surveyed mechanical sensing methods: respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force.
• Analyzed methods based on signal acquisition, deployment constraints, real-time capability, cost, and user movement restrictions.

Main Results:

• Mechanical sensing is non-invasive and cost-effective but susceptible to noise and variability.
• Physiological sensing provides direct, early fatigue indicators but requires complex, contact-based sensors.
• Multimodal sensor fusion enhances robustness and reliability in real-world driving.

Conclusions:

• Both physiological and mechanical sensing methods have distinct advantages and limitations for driver drowsiness detection.
• Sensor fusion is key to developing reliable, real-time driver monitoring systems.
• Considerations for system design include efficiency, real-time capability, and user constraints.