Citation

Yang CH, Chang CC, Liang D. Sensors (Basel) 2018; 18(4): s18041007.

Affiliation

Software Research Center, National Central University, Taoyuan City 32001, Taiwan. drliang@csie.ncu.edu.tw.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18041007

PMID

29597285

Abstract

All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created an experimental system that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication-an equal error rate (EER) of 4.62% in the simulated environment and an EER of 7.86% in the real-traffic environment-confirm the feasibility of this approach.

Language: en

Keywords

Gaussian mixture models; accelerometer sensor; driver authentication; orientation sensor; smartwatch