Abstract

Affective states are associated with people’s mental health status and have profound impact on daily life, thus unobtrusively understanding and estimating affects have been brought to the public attention. The pervasiveness of wearable sensors makes it possible to build automatic systems for affect tracking. However, constructing such systems is a challenging task due to the complexity of human behaviors. In this work, we focus on the problem of estimating daily self-reported affects from sensor-generated data. We first analyze the intra- and inter-subject differences of self-reported affect labels. Second, we explore different machine learning models as well as label transformation techniques to overcome the individual differences in self-reported responses estimation. We conceptualize three experimental settings including long-term and short-term estimation scenarios. Our experimental results show that …

Date

January 1, 1970

Authors

Shen Yan, Homa Hosseinmardi, Hsien-Te Kao, Shrikanth Narayanan, Kristina Lerman, Emilio Ferrara

Journal

Journal of Healthcare Informatics Research

Volume

4

Pages

261-294

Publisher

Springer International Publishing