TEXIVE: Detecting Drivers Using Personal Smart Phones by Leveraging Inertial Sensors.

In this work, we address a fundamental and critical task of detecting the behavior of driving and texting using smartphones carried by users. We propose, design, and implement TEXIVE that leverages various sensors integrated in the smartphone and realizes our goal of distinguishing drivers and passengers and detecting texting using rich user micro-movements and irregularities that can be detected by sensors in the phone before and during driving and texting. Without relying on external infrastructure, TEXIVE has an advantage of being readily implemented and adopted, while at the same time raising a number of challenges that need to be carefully addressed for achieving a successful detection with good sensitivity, specificity, accuracy, and precision. Our system distinguishes the driver and passengers by detecting whether a user is entering a vehicle or not, inferring which side of the vehicle s/he is entering, reasoning whether the user is siting in front or rear seats, and discovering if a user is texting by fusing multiple evidences collected from accelerometer, magnetometer, and gyroscope sensors. To validate our approach, we conduct extensive experiments with several users on various vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.