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

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          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.

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            Speaker-independent isolated word recognition using dynamic features of speech spectrum

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                28 March 2018
                April 2018
                : 18
                : 4
                : 1007
                Affiliations
                [1 ]Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan, drliang@ 123456csie.ncu.edu.tw
                [2 ]Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan, cvml@ 123456mail.ntou.edu.tw
                [3 ]Software Research Center, National Central University, Taoyuan City 32001, Taiwan
                Author notes
                [* ]Correspondence: yang.chinghan@ 123456gmail.com ; Tel.: +886-3-422-7151 (ext. 35236)
                Author information
                https://orcid.org/0000-0002-1209-6295
                Article
                sensors-18-01007
                10.3390/s18041007
                5948624
                29597285
                eead3828-d40e-4232-8873-d996edeef371
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 January 2018
                : 01 March 2018
                Categories
                Article

                Biomedical engineering
                accelerometer sensor,driver authentication,gaussian mixture models,orientation sensor,smartwatch

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