EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and
Pressure Sensing
In-Air Gesture
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McIntosh, Jess
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McNeill, Charlie
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Fraser, Mike
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Kerber, Frederic
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Löchtefeld, Markus
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Krüger, Antonio
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.2332-2342
© Copyright 2016 ACM
Summary: Practical wearable gesture tracking requires that sensors align with
existing ergonomic device forms. We show that combining EMG and pressure data
sensed only at the wrist can support accurate classification of hand gestures.
A pilot study with unintended EMG electrode pressure variability led to
exploration of the approach in greater depth. The EMPress technique senses both
finger movements and rotations around the wrist and forearm, covering a wide
range of gestures, with an overall 10-fold cross validation classification
accuracy of 96%. We show that EMG is especially suited to sensing finger
movements, that pressure is suited to sensing wrist and forearm rotations, and
their combination is significantly more accurate for a range of gestures than
either technique alone. The technique is well suited to existing wearable
device forms such as smart watches that are already mounted on the wrist.