Emotions on the Go: Mobile Emotion Assessment in Real-Time using Facial Expressions

Exploiting emotions for user interface evaluation became an increasingly important research objective in Human-Computer Interaction. Emotions are usually assessed through surveys that do not allow information to be collected in real-time. In our work, we suggest the use of smartphones for mobile emotion assessment. We use the front-facing smartphone camera as a tool for emotion detection based on facial expressions. Such information can be used to reflect on emotional states or provide emotion-aware user interface adaptation. We collected facial expressions along with app usage data in a two-week field study consisting of a one-week training phase and a one-week testing phase. We built and evaluated a person-dependent classifier, yielding an average classification improvement of 33% compared to classifying facial expressions only. Furthermore, we correlate the estimated emotions with concurrent app usage to draw insights into changes in mood. Our work is complemented by a discussion of the feasibility of probing emotions on-the-go and potential use cases for future emotion-aware applications.