Key result
Novel ECG-based algorithm detects drunk driving with ~88% accuracy, outperforming baseline models.
Why the study?
Existing drunk driving detection systems do not provide accurate detection and pre-warning concurrently, and no prior research has investigated ECG-based classifiers for drunk driving detection.
Answers are grounded in this paper and cite their sources.
Should not yet inform roadside screening; leaves open validation of kernel-optimized ECG classifiers in real-world impairment detection.
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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Wu et al. (2016) conducted a letter in Drunk driving (n=50). Weighted third order polynomial kernel SVM vs. Unweighted (prime) third order polynomial kernel SVM was evaluated on Classification accuracy for drunk driving detection. An electrocardiogram-based drunk driving detection system using a weighted third-order polynomial kernel support vector machine achieved an accuracy of 87.52%, improving performance over the prime kernel baseline.
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