ECG-based driver inattention identification during naturalistic driving using Mel-frequency cepstrum 2-D transform and convolutional neural networks

Driver distraction is a major cause of road accidents which can lead to severe physical injuries and death. Statistics indicate the need of a reliable driver distraction system which can monitor the driver׳s distraction in real time and alert the driver before a mishap happens. Early detection of driver distraction can help decrease the costs of roadway disasters. There has been large research efforts towards analyzing driving behavior or monitoring driver distraction using camera-based techniques. However, camera-based systems pose challenges such as privacy and latency in the signs of distraction. Physiological signals such as Electrocardiogram (ECG) and heart rate related measures have been extensively used to monitor human state at a physiological level and developed systems which alert divers well in advance. In this paper, we introduce an ECG based driver distraction detection system using Mel-frequency cepstrum representation and convolutional neural networks (CNN). The proposed model operates by feeding a two dimensional Mel-frequency cepstrum representation of ECG data as input to deep convolutional neural networks. We present a recipe to extract Mel frequency filter bank coefficients in time and frequency domains. The deep CNN is structured to automatically learn reliable discriminative patterns in the 2D spectro-temporal space as features thus replacing the traditional ad hoc hand-crafted features when working with the recorded time-series dataset. The classification accuracy of the proposed prediction algorithm was evaluated for ECG signals recorded from 10 subjects. The subjects aged 24 to 45, actively participated in the naturalistic driving experiment during the ECG recordings. The experimental results demonstrate that the proposed algorithm achieves a significant classification accuracy for across the subjects.