Format

Send to

Choose Destination
Sensors (Basel). 2018 Mar 28;18(4). pii: E1007. doi: 10.3390/s18041007.

A Novel GMM-Based Behavioral Modeling Approach for Smartwatch-Based Driver Authentication.

Author information

1
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan, drliang@csie.ncu.edu.tw. yang.chinghan@gmail.com.
2
Software Research Center, National Central University, Taoyuan City 32001, Taiwan. yang.chinghan@gmail.com.
3
Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan, cvml@mail.ntou.edu.tw. cvml@mail.ntou.edu.tw.
4
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan, drliang@csie.ncu.edu.tw. drliang@csie.ncu.edu.tw.
5
Software Research Center, National Central University, Taoyuan City 32001, Taiwan. drliang@csie.ncu.edu.tw.

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center