Convolutional Neural Networks Using Scalograms for Stress Recognition in Drivers

In this paper we present a system which allows the detection of stress in drivers by analyzing a two-dimensional representation of their electrodermal activity Skin Potential Response (SPR) signal, and their electrocardiogram signal. Signals were logged during a simulated drive, in an experiment carried out in a company using a professional car driving simulator. Subjects had to overcome some stress-inducing events located at specific positions during the drive. The acquired SPR and heart rate signals are analyzed with scalogram plots, in order to obtain a time-frequency representation of the signals. The 2D scalogram representation is segmented into images, associated to short time segments, which are classified using a Convolutional Neural Network architecture. We show that the use of scalograms can allow the system to perform well in distinguishing among stress and non-stress situations, achieving a 91.78% accuracy. The same system was tested on real driving data available from a public dataset, achieving a 99.24% accuracy.