Sensing + Interaction On and Around the Body

Wearables are a significant part of the new generation of computing. Compared with more
traditional computers (e.g., laptop, smartphones), wearable devices are more readily available
for immediate use, but significantly smaller in size, creating new opportunities and challenges
for on-body sensing and interaction. My holistic research approach (from problem
understanding to invention to implementation and evaluation) investigates how to effectively
exchange information between humans, their environment, and wearables. My Ph.D. thesis
focuses on novel wearable input using on-body sensing through various high-level interaction
gestures, low-level input events, and a redesign of the interaction. In this talk, I will highlight
three projects. The first is a wearable ring that allows the user to input over 40 unistroke
gestures (including text and numbers). It also shows how to overcome a limited training set size,
a common challenge in applying machine learning techniques to real systems, through an
understanding of the characteristics of data and algorithms. The second project demonstrates
how to combine a strong, yet incomplete, understanding of on-body signal propagation physics
with machine learning to create a novel yet practical sensing and interaction techniques. The
third project is an active acoustic sensing technique that enables a user to interact with
wearable devices in the surrounding 3D space through continuous high-resolution tracking of
finger's absolute 3D position. It demonstrates how to solve a technical interaction challenge
through a deep understanding of signal propagation. I will also share my vision on future
opportunities for on-body sensing and interaction, especially in high-impact areas, such as
health, activity recognition, AR/VR, and more futuristic interaction paradigms between humans
and the increasingly connected environment.
Cheng Zhang is a Ph.D. candidate in Computer Science in the School of Interactive Computing
(IC) at Georgia Institute of Technology, advised by Gregory Abowd (IC) and Omer Inan (ECE).
His research focuses on enabling the seamless exchange of information among humans,
computers, and the environment, with a particular emphasis on the interface between humans
and wearable technology. His Ph.D. thesis presents 10 different novel input techniques for
wearables, some leveraging commodity devices while others incorporate new hardware. His
work blends an understanding of signal propagation on and around the body with, when
necessary, appropriate machine learning techniques. His work has resulted in over a dozen
publications in top-tier conferences and journals in the field of Human-Computer Interaction and
Ubiquitous Computing (including two best paper awards), as well as over 6 pending U.S. and
international patents. His work has attracted the attention of various media outlets, including
ScienceDaily, DigitalTrends, ZDNet, New Scientist, RT, TechRadar, Phys.org, Yahoo News,
Business Insider, and MSN News. The work that leverages commodity devices has resulted in
significant commercial impact, including adoption of tap input on commodity smartphones by
Atlanta startup AgVoice, and licensing by Canadian startup ProximityHCI to improve the
smartwatch interaction experience.