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...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2016-05, Vol.16 (5), p.659-659
Main Authors: Wu, Chung Kit, Tsang, Kim Fung, Chi, Hao Ran, Hung, Faan Hei
Format: Article
Language:English
Subjects:
Accidents, Traffic
Alcoholic Intoxication
Automobile Driving
Breath tests
Classifiers
Driving
Driving Under the Influence
Drunk driving
drunk driving detection
Drunkenness
electrocardiogram
Electrocardiography
Electroencephalography
feature extraction
Humans
Kernel functions
Kernels
Letter
Mathematical analysis
Support vector machines
Traffic accidents
Vectors (mathematics)
weighted kernel
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s16050659