Making muscle-computer interfaces more practical

Authors:
T. Scott Saponas;Desney S. Tan;Dan Morris;Jim Turner;James A. Landay
Affiliations:
University of Washington, Seattle, WA, USA;Microsoft Research, Redmond, WA, USA;Microsoft Research, Redmond, WA, USA;Microsoft Corporation, Redmond, WA, USA;University of Washington, Seattle, WA, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2010

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Abstract

Recent work in muscle sensing has demonstrated the poten-tial of human-computer interfaces based on finger gestures sensed from electrodes on the upper forearm. While this approach holds much potential, previous work has given little attention to sensing finger gestures in the context of three important real-world requirements: sensing hardware suitable for mobile and off-desktop environments, elec-trodes that can be put on quickly without adhesives or gel, and gesture recognition techniques that require no new training or calibration after re-donning a muscle-sensing armband. In this note, we describe our approach to over-coming these challenges, and we demonstrate average clas-sification accuracies as high as 86% for pinching with one of three fingers in a two-session, eight-person experiment.