Seminar: The Next Input Frontier: Implicit Hand Interactions
Nathan DeVrio
PhD Candidate
Human Computer Interaction Institute
Carnegie Mellon University
Friday, February 6, 2026
9:30 - 10:30 a.m.
1100 Torgersen Hall
Abstract
This talk examines how modern mobile devices have evolved to prioritize mobility and wearability at the expense of usable input. While advances in display and computation have increased the potential of these devices, limitations in input continue to constrain what users can actually do with them. Prior research has overly focused on improving explicit input techniques. In contrast, my work explores an alternative approach: implicit input which uses sensors worn on the body to detect behaviors that occur naturally around devices. I present three sensing systems that demonstrate how implicit input can expand interaction by enabling devices to understand what a user is touching, how they are moving, and which objects they are interacting with. Together, these systems illustrate how sensing everyday hand and body behaviors can meaningfully augment existing input. I argue that the rise of AI-driven mobile applications further exposes the shortcomings of current input frameworks, which provide limited user context and keep devices largely reactive. As a whole, my research agenda positions implicit sensing of hand interactions as a foundation for more proactive, context-aware devices that both better leverage advances in AI and help close the growing input gap in mobile computing.
Biography
Nathan DeVrio is a PhD candidate in the Human-Computer Interaction Institute at Carnegie Mellon University advised by Prof. Chris Harrison. In his research, Nathan explores how wearable devices can use novel sensing technologies to learn implicit information about their users and surroundings. This information helps provide context to increasingly powerful AI applications which can proactively help users during daily activities. Nathan publishes his work as full papers at top Human-Computer Interaction conferences including ACM CHI and UIST with recognition for best paper and honorable mention demo awards. His work has also received coverage from major outlets including NBC News and CNN, has led to multiple patents, and has been explored by companies including Apple, Meta, Google, and TDK.