Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data

Rafael Wampfler, Severin Klingler, Barbara Solenthaler, Victor R Schinazi, Markus Gross

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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 system 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.
Original languageEnglish
Title of host publicationCHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages13
ISBN (Electronic)978-1-4503-6708-0
DOIs
Publication statusPublished - Apr 2020
Externally publishedYes

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Wampfler, R., Klingler, S., Solenthaler, B., Schinazi, V. R., & Gross, M. (2020). Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems [337] New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3313831.3376504