Validation of Driver Drowsiness Detection Based on Humantenna Effect Using Facial Features

Abstract

Along with the continued use of vehicles such as cars, trains, motorbikes, planes and other transportation methods came the need to research automotive safety to avoid accidents that may be caused by reckless, sleepy or drowsy drivers. By utilizing current technological advancements, it could be possible to rapidly improve and create new safety measures and features in these vehicles. This research presents validated results for a proposed driver drowsiness detection system based on the humantenna touch sensor that is simple, cost efficient, accurate and has a lot of room for improvement. The use of this said is further validated by collecting simultaneous data obtained from processing the images capturing the facial features of the driver through facial landmark detection and eye aspect ratio calculation. The results obtained were based on the strong correlation between driver drowsiness and both phenomena, the steering wheel grip pattern and eye closure. The superposed data showed the different states possible for a driver in terms of humantenna effect and eye aspect ratio (EAR) calculations. These results also showed the potential for more research and improvement of the humantenna touch sensor.