We explore the feasibility of muscle-computer interfaces: 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 muscle-computer interface concept, we conducted an experiment to explore the potential of exploiting muscular sensing and processing technologies for muscle-computer interfaces. 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 muscle-computer interface designs.

Collaborators

Ravin Balakrishnan
James A. Landay
Dan Morris
T. Scott Saponas
Desney Tan

Publications

Enhancing Input On and Above the Interactive Surface with Muscle Sensing Enhancing Input On and Above the Interactive Surface with Muscle Sensing
Hrvoje Benko, T. Scott Saponas, Dan Morris and Desney Tan
ACM International Conference on Interactive Tabletops and Surfaces, 2009. Full Paper (PDF) [Video
Enabling Always-Available Input with Muscle-Computer Interfaces Enabling Always-Available Input with Muscle-Computer Interfaces
T. Scott Saponas, Desney Tan, Dan Morris, Ravin Balakrishnan, Jim Turner and James A. Landay
Symposium on User Interface Software and Technology, 2009. Full Paper (PDF) [Video
Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces
T. Scott Saponas, Desney Tan, Dan Morris and Ravin Balakrishnan
Conference on Human Factors in Computing Systems, 2008. Full Paper (PDF)