Detecting Distracted Driving Using a Wrist-Worn Wearable

In this paper, we attempt to detect instances of distracted driving using a wrist-worn wearable embedded with an accelerometer and a gyroscope. In our experimental study, 16 adult participants were asked to drive a driving simulator that is equipped with realistic driving conditions like brakes, accelerator, steering wheel and a large screen for road scene visualization. The simulator is also programmed for drivers to experience different environmental scenarios like day time, night time, fog and rain/ snow. While driving, participants engaged in a randomized sequence of calling, texting and reading from a phone while simultaneously driving. Throughout the experiment, each subject wore a wearable watch on the wrist which recorded the resulting acceleration and rotation of the wrist via an embedded accelerometer and gyroscope. Subsequently, we extracted a selected number of features from the sensory data, and designed machine learning techniques for detecting instances of distracted driving. Our performance evaluations reveal very good Precision, Recall, and F1-Scores. We believe that our paper introduces a new and potentially important application of wristworn wearables to enhance road safety.