Abstract

Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an explosion in popularity. Their pervasiveness allows for effective data collections to quantify human behavior in natural settings, enriching traditional behavioral science research opportunities. In this paper, we focus on the problem of affect estimation from sensor-generated data, whereas ground truth is available to us in the form of daily self-reported affective states. First, our analysis shows that individuals’ self-reported affect labels exhibit low variance, with a few samples deviating significantly from average observations, which may relate to experiencing out-of-the-ordinary events. Second, we explore different machine learning models as well as label transformation techniques to directly infer the self-reported scores from available sensor data. Experimental results show that mixed effects model and label transformation are able to …

Date

June 10, 2019

Authors

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

Conference

2019 IEEE International Conference on Healthcare Informatics (ICHI)

Pages

1-9

Publisher

IEEE