Monitoring people's stress levels has become an essential part of behavioural studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively to an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this paper, we present a stress monitoring system that provides objective daily stress measurements in everyday settings based on three physiological signals: electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) using Shimmer3 ECG, Shimmer3 GSR+, and Empatica E4 wearable sensors. We perform controlled stress assessment experiments on 17 participants in which we successfully detect stress with a 94.55% accuracy for 10-fold cross-validation and an 85.71% accuracy for subject-wise cross-validation. In everyday settings, the system assesses stress with an 81.82% accuracy. We also examine whether motion artefacts affect stress assessment and filter the low-confidence readings to minimise false alarms.
Keywords: Stress monitoring; bio-signal processing; internet-of-things; physiological signals; wearable sensors.