Adaptive fuzzy classifier to detect driving events from the inertial sensors of a smartphone

In the last years there has been a rising interest in monitoring driver behaviors by using smartphones, due to their increasing market penetration. Inertial sensors embedded in these devices are key to carry out this task. Most of the state-of-the-art apps use fix thresholds to detect driving events from the inertial sensors. However, sensors output values can differ depending on many parameters. In this paper we present an Adaptive Fuzzy Classifier to identify sudden driving events (acceleration, steering, braking) and road bumps from the inertial and GPS sensors. An on-line calibration method is proposed to adjust the decision thresholds of the Membership Functions (MFs) to the specific phone pose and vehicle dynamics. To validate our method, we use the UAH-Driveset database [1], which includes more than 500 minutes of naturalistic driving, and we compare results with our previous DriveSafe [2] app version, based on fix thresholds. Results show a notable improvement in the events detection regarding our previous version.