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Sensors 2017, 17(2), 333; doi:10.3390/s17020333

Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference Syste

1
Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), 333 Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea
2
Research Institute of Engineering & Technology, Hanyang University, 55 Hanyangdaehak-ro, Ansan, Gyeonggi-do 15588, Korea
3
Department of Interdisciplinary Engineering System, Hanyang University, 55 Hanyangdaehak-ro, Ansan, Gyeonggi-do 15588, Korea
4
Department of Robotics Engineering, Hanyang University, 55 Hanyangdaehak-ro, Ansan, Gyeonggi-do 15588, Korea
*
Author to whom correspondence should be addressed.
Received: 31 December 2016 / Revised: 29 January 2017 / Accepted: 6 February 2017 / Published: 9 February 2017
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Abstract

Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrancebyleveragingbuilt-insmartphonesensorsonly. Theresultsofourcomprehensiveevaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8 %accuracy regardless of smartphone positions and vehicle types. View Full-Text
Keywords: driver identification; fuzzy inference system; vehicle-boarding actions; inertial sensors; driving while distracted driver identification; fuzzy inference system; vehicle-boarding actions; inertial sensors; driving while distracted
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Ahn, D.; Park, H.; Hwang, S.; Park, T. Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference Syste. Sensors 2017, 17, 333.

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