To test the stress detection performance of a wearable sensor system, the signals related to heart activity and electrodermal activity of the Empatica E4 wristband have been compared to stationary electrocardiogram and finger skin conductivity electrodes of high sampling rates during the classical laboratory stress protocol Trier Social Stress Test. The comparison has been done on both signal level and in terms of features for stress detection on a total of seven subjects. The main results point out that the Empatica E4 wristband had a significant loss in terms of detected interbeat intervals, but that time-domain features such as the mean heart rate and standard deviation of the heart rate were still well estimated, with good stress discrimination power. Furthermore, the skin conductivity signals measured at different locations (wrist versus finger) show no visual resemblance and it appears that the signal from the Empatica E4 wristband yielded higher stress discrimination power than the signal measured at the fingers.

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.