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Int. J. Environ. Res. Public Health 2017, 14(1), 108; doi:10.3390/ijerph14010108

Support Vector Machine Classification of Drunk Driving Behaviour

1,2,* and 1
1
College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410012, China
*
Author to whom correspondence should be addressed.
Academic Editors: Amy O’Donnell, Eileen Kaner and Peter Anderson
Received: 10 November 2016 / Revised: 11 January 2017 / Accepted: 13 January 2017 / Published: 23 January 2017
(This article belongs to the Special Issue Alcohol and Health)
View Full-Text   |   Download PDF [838 KB, uploaded 23 January 2017]   |  

Abstract

Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN), the root mean square value of the difference of the adjacent R–R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety. View Full-Text
Keywords: drunk driving; support vector machine; principal component analysis; driving performance; physiological measurement drunk driving; support vector machine; principal component analysis; driving performance; physiological measurement
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, H.; Chen, L. Support Vector Machine Classification of Drunk Driving Behaviour. Int. J. Environ. Res. Public Health 2017, 14, 108.

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