The integrated strategy for detecting driver drowsiness described in this work makes use of the driver's physical, physiological, and optical cues. A machine learning image processing algorithm that contributes to the visual behaviour analysis is used to combine facial and eye analysis to assess the driver's level of exhaustion. As part of the physical behaviour method, the steering grip of the driver is measured using a human antenna effect-based touch sensing technology. Driver heart rate data is collected using a sensor and evaluated to detect tiredness based on the threshold value.

Keywords : Internet of Things, drowsiness, image processing, heart rate, touch sensing, buzzer.