Psychological stress level detection based on electrodermal activity

This paper presents a method for detecting psychological stress levels. It aims to explore the feasibility of using a single physiological signal to create a more practical alternative for detecting stress in people than current multiple physiological signals approaches involve. In particular, the approach explored uses linear discriminant analysis (LDA) based on the electrodermal activity (EDA) signal which aims at discriminating between three stress levels: low, medium and high. We used the MIT Media lab 'stress database' from which we selected eleven 'foot' based EDA data sets for our experiments. Using this eighteen EDA features were extracted from (sixty-six) five-minutes data segments equating to three driving conditions: at rest, on the open road (highway) and city driving. After that, Fisher projection and Linear discriminant analysis (LDA) were used to classify the stress levels with feature vectors, that included both leaving one out and test cross-validation strategy. The results showed that these methods achieved recognition rate of 81.82% which we argue, while less that multiple signal systems, may be a better balance between recognition performance and computational load, that could be a promising line of research for the development of practical personal stress monitors.