EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing

Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Löchtefeld, Antonio Krüger

In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM International Conference on Human Factors in Computing Systems (CHI-16) May 9-12 San Jose CA United States Seiten 2332-2342 ISBN 978-1-4503-3362-7 ACM 2016.


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.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence