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Cited by 275

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Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces

Saponas, T Scott, Tan, Desney S., Morris, Dan, Balakrishnan, Ravin·2008·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
DOI 10.1145/1357054.1357138
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Abstract

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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Metadata

Authors
Saponas, T. S., Tan, D. S., Morris, D., & Balakrishnan, R.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2008-04-06
Pages
515-524
DOI
10.1145/1357054.1357138
OpenAlex
W2152528000
Cited by
275
References
0
FWCI
27.63
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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CoChat. (2026). Citation check: Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. https://app.cochat.ai/papers/10.1145/1357054.1357138
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Saponas, T. S., Tan, D. S., Morris, D., & Balakrishnan, R. (2008). Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1357054.1357138

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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