Why the study?
Driver drowsiness impairs judgment and decision making, resulting in fatal crashes and road traffic injuries.
A smartphone-based drowsiness detection application using facial analysis, heart rate, and speeding data achieved a performance accuracy of 93.3%.
May support smartphone drowsiness detection development; leaves open prospective validation before clinical use.
Absence of forbearance among drivers, fatigue and irresponsible behaviour among drivers result in countless fatal crashes and road traffic injuries. Driver drowsiness is a highly problematic issue which impairs judgment and decision making among drivers resulting in fatal motor crashes. This paper describes a simple drowsiness detection approach for a smartphone with Android application using Android Studio 3.6.1 and Mobile Vision API for drowsiness detection before and while driving. Physiological analysis and a quick facial analysis were performed to check drowsiness before the driver starts driving. The smartphone camera was used for analysing the heart rate by tracking colour changes due to blood flow on the fingertip. Facial analysis was undertaken by Google Vision API which determined the head position, blinking duration and yawning frequency through the eye opening and mouth opening probabilities. The heart rate, blinking duration, yawning frequency and speeding were used as indicators for drowsiness. The facial analysis was repeated with speeding data while driving with results analysed each one minute. A performance accuracy of the combined results with speeding detection proved to be around 93.3%.
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Rajkumarsingh et al. (2021) studied this question.
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