Driver Identification System Using 2D ECG and EMG Based on Multistream CNN for Intelligent Vehicle

Abstract

As autonomous driving technologies develop, biometrics technology using driver's bio-signals provides various driver-customized infotainment services. Many studies on driver identification are underway to improve the feature extraction and classification steps to identify drivers with high accuracy. The existing identification systems have used a driver's single biological signal to identify the driver without considering the driver's driving state. The identification error rate was high because electromyography (EMG) is included according to motion artifacts while driving in the electrocardiogram (ECG) acquired by the driver's behavioral characteristics. In this letter, EMG acquired by the driver's motion and ECG acquired by the behavioral characteristics are used simultaneously to improve the identification accuracy. The proposed identification system converts ECG and EMG into 2D constant Q transform (CQT) images and classifies drivers by multistream convolutional neural network (CNN). Our extensive experiments show that a single ECG can achieve 98.1% of the identification accuracy, a single EMG can achieve 84.4% of the identification accuracy, and a multisignal can achieve 98.9% of the identification accuracy with 2D CQT.