Towards Stress Detection in Real-Life Scenarios Using Wearable Sensors - Normalization Factor to Reduce Variability in Stress Physiology.

Wearable physiological sensors offer possibilities for the development of continuous stress detection models. Such models need to address the inter-individual and intra-individual differences in stress physiology. In this paper we propose and evaluate a normalization factor, StressResponse Factor (SRF), to address such differences. SRF is computed using physiological features and the corresponding stress level at a reference point. The proposed normalization factor is evaluated in a dataset obtained from a free-living study with 10 participants, where each participant was monitored for 5 days during their working hours using different physiological sensors. We obtain an average reduction of mean squared error by up to 32% in models with SRF compared to the models without SRF.