As computing proliferates into everyday life, systems that understand people's context of use are of paramount importance. Regardless of whether the platform is a mobile device, a wearable, or embedded in the environment, context offers an implicit dimension that will become highly important if we are to power more human-centric experiences. Context-driven sensing will become a foundational element for many high-impact applications, from specific domains such as elder care, health monitoring, and empowering people with disabilities, to much broader areas such as smart infrastructures, robotics, and novel interactive experiences for consumers.

In this dissertation, I discuss the construction and evaluation of sensing technologies that can be practically deployed and yet still greatly enhance contextual awareness, primarily drawing upon machine learning to unlock a wide range of applications. I attack this problem area on two fronts: 1) supporting sensing expressiveness via context-sensitive wearable devices, and 2) achieving general-purpose sensing through sparse environment instrumentation. Finally, I explore how such sensing schemes can become more practical, by reducing user burden through data-driven approaches. I discuss algorithms and pipelines that extract meaningful signals and patterns from sensor data to enable high-level abstraction and interaction. I also discuss system and human-centric challenges, and I conclude with a vision of how rich contextual awareness can enable more powerful experiences across broader domains.

237 pages

Thesis Committee:
Chris Harrison (Chair)
Scott Hudson
Niki Kittur
Shwetak Patel (University of Washington)

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

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