Abstract

Recent work in muscle sensing has demonstrated the potential 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, electrodes 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 overcoming these challenges, and we demonstrate average classification accuracies as high as 86% for pinching with one of three fingers in a two-session, eight-person experiment.