@article{ref1,
title="A novel GMM-based behavioral modeling approach for smartwatch-based driver authentication",
journal="Sensors (Basel)",
year="2018",
author="Yang, Ching-Han and Chang, Chin-Chun and Liang, Deron",
volume="18",
number="4",
pages="s18041007-s18041007",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s18041007",
url="http://dx.doi.org/10.3390/s18041007"
}