Authors
Kwok Tai Chui, Kim Fung Tsang, Hao Ran Chi, Bingo Wing Kuen Ling, Chung Kit Wu
Publication date
2016/5/26
Journal
IEEE Transactions on Industrial Informatics
Volume
12
Issue
4
Pages
1438-1452
Publisher
IEEE
Description
Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel K xcorr,ij and the convolution kernel K conv,ij . K xcorr,ij , and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic …
Total citations
20172018201920202021202220232024518182826261413
Scholar articles
KT Chui, KF Tsang, HR Chi, BWK Ling, CK Wu - IEEE Transactions on Industrial Informatics, 2016