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Support Vector Machine Classification of Drunk Driving Behaviour

Author

Listed:
  • Huiqin Chen

    (College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410012, China)

  • Lei Chen

    (College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

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.

Suggested Citation

  • Huiqin Chen & Lei Chen, 2017. "Support Vector Machine Classification of Drunk Driving Behaviour," IJERPH, MDPI, vol. 14(1), pages 1-14, January.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:1:p:108-:d:88599
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    Cited by:

    1. Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    2. Tao Huang & Shihao Zhou & Xinyi Chen & Zhengsong Lin & Feng Gan, 2022. "Colour Preference and Healing in Digital Roaming Landscape: A Case Study of Mental Subhealth Populations," IJERPH, MDPI, vol. 19(17), pages 1-19, September.

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