We explore the feasibility of muscle-computer interfaces (muCIs): an interaction methodology that directly senses and decodes human muscular activity rather than relying on physical device actuation or user actions that are externally visible or audible. As a first step towards realizing the mu-CI concept, we conducted an experiment to explore the potential of exploiting muscular sensing and processing technologies for muCIs. We present results demonstrating accurate gesture classification with an off-the-shelf electromyography (EMG) device. Specifically, using 10 sensors worn in a narrow band around the upper forearm, we were able to differentiate position and pressure of finger presses, as well as classify tapping and lifting gestures across all five fingers. We conclude with discussion of the implications of our results for future muCI designs.

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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.