EMSense: Recognizing Handled, Uninstrumented, Electro-Mechanical Objects Using Software-Defined Radio

Authors

Gierad Laput (Carnegie Mellon University)
Chouchang (Jack) Yang (Disney Research Pittsburgh)
Robert Xiao (Carnegie Mellon University)
Alanson Sample (Disney Research Pittsburgh)
Chris Harrison (Carnegie Mellon University)

ACM Symposium on User Interface Software and Technology (ACM UIST) 2015

November 8, 2015

EMSense- Recognizing Handled, Uninstrumented, Electro-Mechanical Objects Using Software-Defined Radio-Image

Most everyday electrical and electromechanical objects emit small amounts of electromagnetic (EM) noise during regular operation. When a user makes physical contact with such an object, this EM signal propagates through the user, owing to the conductivity of the human body. By modifying a small, low-cost, software-defined radio, we can detect and classify these signals in real-time, enabling robust on-touch object detection. Unlike prior work, our approach requires no instrumentation of objects or the environment; our sensor is self-contained and can be worn unobtrusively on the body. We call our technique EM-Sense and built a proof-of-concept smartwatch implementation. Our studies show that discrimination between dozens of objects is feasible, independent of wearer, time and local environment.

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