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. 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. 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.
UPDATE: EM-Sense wins Fast Company's Innovation by Design 2016 Award (Students Category)!
Gierad Laput, Chouchang Yang, Robert Xiao, Alanson Sample, and Chris Harrison. 2015. EM-Sense: Touch Recognition of Uninstrumented, Electrical and Electromechanical Objects. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15). ACM, New York, NY, USA, 157-166. DOI=http://dx.doi.org/10.1145/2807442.2807481