Modeling emotion unobtrusively in interactive digital systems

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Although office environments are the provenance of computer usage, interactive systems have recently woven themselves into our homes, our leisure activities, and our relationships. In these activities, user experience is governed in large part by the user's affective experience; however, current computer systems still do not understand or adapt to the emotional state of the user. Although progress has been made on modeling emotion, current approaches (e.g., instrumenting a user with physiological sensors) are often too obtrusive and expensive for use outside of laboratories, and thus lack utility for many affective computing applications. Rather than instrument a user, the primary objective proposed by this research is to invent novel methods for detecting and modeling emotion that are unobtrusive to the user, thus improving emotion detection in natural settings. We will accomplish this by mining existing traces left behind as users interact with their devices and their environment. First, we will use traces from computer interaction (keystroke dynamics and cursor kinematics), second we will use biometric traces (pupil dilation, posture sensing), and third, we will use linguistic traces from social media updates and text messages. We will also improve individual model performance through synthesis of the multiple projects. These three themes represent foundational research and will advance the fields of human-computer interaction, affective computing, and emotion modeling.