Automobile Driver Recognition under different physiological conditions using the electrocardiogram

Author

Sidek, Khairul Azami ; Khalil, Ibrahim

Author_Institution

Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, VIC, Australia

fYear

2011

fDate

18-21 Sept. 2011

Firstpage

753

Lastpage

756

Abstract

This paper presents a person identification mechanism of automobile drivers under different physiological conditions. A total of 16 subjects were used in this study from the Stress Recognition in Automobile Driver database (DRIVEDB). Discrete Wavelet Transform was applied to reveal useful hidden information in the ECG signal which is not readily available in a time domain representation. Features are extracted based on coefficients produced due to the wavelet decomposition process. These features sets were then used in Radial Basis Function (RBF) for classification purposes. Our experimentation suggests that person identification is possible by obtaining identification accuracy of 95% as compared to 91% without wavelet analysis. This also indicates the robustness of ECG biometric implemented under different physiological conditions.

Keywords

biometrics (access control); discrete wavelet transforms; driver information systems; electrocardiography; feature extraction; medical signal processing; pattern classification; physiology; radial basis function networks; DRIVEDB; ECG biometric; ECG signal; automobile driver database; automobile driver recognition; discrete wavelet transform; electrocardiogram; feature extraction; person identification mechanism; physiological conditions; radial basis function; stress recognition; wavelet decomposition process; Discrete wavelet transforms; Electrocardiography; Feature extraction; Physiology; Vehicles; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing in Cardiology, 2011

Conference_Location

Hangzhou

ISSN

0276-6547

Print_ISBN

978-1-4577-0612-7

Type

conf

Filename

6164675