filename     : Wam20a.pdf
entry        : inproceedings
conference   : SIGCHI Conference on Human Factors in Computing Systems (CHI '20), Honolulu, HI, 25-30 April, 2020
pages        :
year         : 2020
month        : 04
title        : Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data
subtitle     :
author       : Rafael Wampfler, Severin Klingler, Barbara Solenthaler, Victor R. Schinazi, Markus Gross
booktitle    : SIGCHI Conference on Human Factors in Computing Systems
ISSN/ISBN    : 978-1-4503-6708-0
editor       :
publisher    : ACM
publ.place   :
volume       :
issue        :
language     : English
keywords     : Classification; Affective Computing; Smartphone; Deep Learning
abstract     : Gaining awareness of the user's affective states enables smartphones to support enriched interactions that are sensitive to the user's context. To accomplish this on smartphones, we propose a system that analyzes the user's text typing behavior using a semi-supervised deep learning pipeline for predicting affective states measured by valence, arousal, and dominance. Using a data collection study with 70 participants on text conversations designed to trigger different affective responses, we developed a variational auto-encoder to learn efficient feature embeddings of two-dimensional heat maps generated from touch data while participants engaged in these conversations. Using the learned embedding in a cross-validated analysis, our nsystem predicted three levels (low, medium, high) of valence (AUC up to 0.84), arousal (AUC up to 0.82), and dominance (AUC up to 0.82). These results demonstrate the feasibility of our approach to accurately predict affective states based only on touch data.