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, , 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.

Recommended citation: Lamichhane, B., Großekathöfer, U., Schiavone, G., & Casale, P. (2017). Towards stress detection in real-life scenarios using wearable sensors: normalization factor to reduce variability in stress physiology. In eHealth 360°: International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers (pp. 259-270). Springer International Publishing.
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