A novel GMM-based behavioral modeling approach for smartwatch-based driver authentication

Ching Han Yang, Chin Chun Chang, Deron Liang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Article number1007
JournalSensors (Switzerland)
Issue number4
StatePublished - Apr 2018


  • Accelerometer sensor
  • Driver authentication
  • Gaussian mixture models
  • Orientation sensor
  • Smartwatch


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