A driver identification system using a 2D spectrogram of ECG improved identification performance compared to EEMD and MFCCs, while significantly reducing training time in a CNN network.
A 2D spectrogram-based ECG analysis improves driver identification accuracy and computational efficiency for intelligent vehicle security.
Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.
Choi et al. (Mon,) conducted a other in Driver identification for vehicle security. Driver identification system using a 2D spectrogram of ECG vs. Existing multidimensional feature extraction methods (EEMD and MFCCs) was evaluated on Identification performance and training time. A driver identification system using a 2D spectrogram of ECG improved identification performance compared to EEMD and MFCCs, while significantly reducing training time in a CNN network.