E4 Bytes: Detecting Moments of Stress in Real-World Settings

Our research clients at University of Salzburg, University Hospital Zurich, Harvard University, University of Groningen, and the University of Birmingham, describe how they used our E4 smartband to develop an algorithm that identifies moments of stress in real-world settings.

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small amount of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. Our research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS).

We propose a rule-based algorithm based on galvanic skin response and skin temperature, which we measured with the Empatica E4 wristband. The new algorithm combines empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from a first-person video.

Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The Empatica E4 wristband has proven to deliver high-quality and continuous measurement data although it was applied in a highly mobile and non-standardised real-world environment. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.


[1] Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F. H., Osborne, T., & Pykett, J. (2019). Detecting Moments of Stress from Measurements of Wearable Physiological Sensors. Sensors, 19(17), 3805.