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A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram

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-war...

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Autores principales: Wu, Chung Kit, Tsang, Kim Fung, Chi, Hao Ran, Hung, Faan Hei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883350/
https://www.ncbi.nlm.nih.gov/pubmed/27171090
http://dx.doi.org/10.3390/s16050659
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author Wu, Chung Kit
Tsang, Kim Fung
Chi, Hao Ran
Hung, Faan Hei
author_facet Wu, Chung Kit
Tsang, Kim Fung
Chi, Hao Ran
Hung, Faan Hei
author_sort Wu, Chung Kit
collection PubMed
description 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.
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spelling pubmed-48833502016-05-27 A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram Wu, Chung Kit Tsang, Kim Fung Chi, Hao Ran Hung, Faan Hei Sensors (Basel) Letter 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. MDPI 2016-05-09 /pmc/articles/PMC4883350/ /pubmed/27171090 http://dx.doi.org/10.3390/s16050659 Text en © 2016 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/).
spellingShingle Letter
Wu, Chung Kit
Tsang, Kim Fung
Chi, Hao Ran
Hung, Faan Hei
A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title_full A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title_fullStr A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title_full_unstemmed A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title_short A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram
title_sort precise drunk driving detection using weighted kernel based on electrocardiogram
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883350/
https://www.ncbi.nlm.nih.gov/pubmed/27171090
http://dx.doi.org/10.3390/s16050659
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