Cub Cadet i1042 Operator's Manual page 10

CONCLUSION
Our work demonstrates that muscle-sensing can be used to
accurately classify a useful variety of finger gestures, even
when the hands are under load. It also shows that classifica-
tion can be done in real-time, thus making forearm muscle-
sensing viable for human-computer interaction, in contrast
to previous work that relied on off-line analysis. Further-
more, it highlights the tradeoff between speed and accuracy
that results from providing users with immediate visual
feedback. Finally, it introduces a novel bimanual technique
for accurate engagement/disengagement of the recognizer, a
crucial aspect of making muscle sensing usable for interac-
tive tasks. In addition to the formal experimentation and
results, we have demonstrated more holistic interaction via
our portable music player application and a prototype game.
ACKNOWLEDGEMENTS
We thank Jonathan Lester and Carl Ringler for assistance
with the wireless device, Meredith Skeels for fabricating
some armbands, as well as John Platt and Jay Stokes for
machine learning advice and tools.
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