We are living in a world where social technologies connect more people than ever before. However, barriers inherent to these platforms, such as minimal social cues and inauthentic interactions, can limit our ability to meaningfully connect. To address these issues, I introduce expressive biosignals as a novel intervention to foster social connection over technology. Expressive biosignals are sensed physiological data presented as a new type of social cue to help people gain a deeper understanding of each other's underlying psychological states. As sensing and sharing these data become increasingly possible in our everyday lives, we must address the following questions: how are biosignals shared and perceived, how do they influence communication, and how can they be designed most effectively to facilitate positive interactions?

In my thesis, I present a series of studies that address these questions through the design, development, and deployment of expressive biosignals systems. I investigate the social dynamics involved in sending and receiving heart rate and brain activity, including people's motivations for sharing these personal data with others, the effects of biosignal sharing on interpersonal judgments, and the everyday interaction patterns that they afford in dyadic communication. In the final stage of my thesis work, I illustrate the value of integrating biosignals into communication through a one-month field study that compared a biosignals sensing OFF and ON version of HedgeHugs, an Apple Watch and iPhone application that enabled romantic partners to share biosignals-based animations with each other. Taken together, these works show that expressive biosignals have the potential to facilitate various components of communication and consequently, foster mutual feelings of connection. Specifically, biosignals can support senders' emotional expressions and receivers' perceptions of and responses to those expressions. My thesis makes three main contributions: (1) an articulation of the design space for expressive biosignals, (2) theoretical models for their influences on communication and connection among interaction partners, and (3) novel interventions for improving social connection through clarifying and conveying our internal experiences.

311 pages

Thesis Committee:
Laura Dabbish (Co-Chair)
Geoff Kaufman (Co-Chair)
Mayank Goel
John Tang (Microsoft Research)

Jodi Forlizzi, Head, Human-Computer Interaction Institute
Martial Hebert, Dean, School of Computer Science

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