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Sensors 2016, 16(5), 659; doi:10.3390/s16050659

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram

Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 22 December 2015 / Revised: 20 April 2016 / Accepted: 27 April 2016 / Published: 9 May 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [943 KB, uploaded 9 May 2016]   |  

Abstract

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods. View Full-Text
Keywords: drunk driving detection; electrocardiogram; weighted kernel; feature extraction drunk driving detection; electrocardiogram; weighted kernel; feature extraction
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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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MDPI and ACS Style

Wu, C.K.; Tsang, K.F.; Chi, H.R.; Hung, F.H. A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram. Sensors 2016, 16, 659.

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