Driver Distraction Identification using MEL Cepstrum Representation of Galvanic Skin Responses and Convolutional Neural Networks

Abstract

Driver distraction is the major cause of road accidents which can lead to severe physical injuries and deaths. Statistics indicate the need of a reliable driver distraction system which can monitor the driver's distraction in the real time and alert the driver before the mishap happens. Therefore, early detection of driver distraction can help decrease the costs of roadway disasters. Physiological signals such as galvanic skin response (GSR) analysis have been extensively used to monitor distraction at physiological level and develop system which alerts divers well in advance. In this paper, we introduce a driver distraction detection system based on MEL Cepstrum analysis of GSR signals and using convolutional neural networks (CNN). The proposed model operates by calculating and feeding two dimensional (2D) representation of GSR 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 Mel cepstrum space as features thus replacing the traditional ad hoc hand-crafted features when working with a high dimensional time-series dataset. The classification accuracy of the proposed prediction algorithm is evaluated based on a set of recorded GSR signals from 7 subjects. The subjects aged 24 to 45, actively participated in the naturalistic driving experiment during the GSR recordings.