Abstract
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, \(Stress\,Response\,Factor~(SRF)\), 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.
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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: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_34
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