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

EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing
Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Löchtefeld, Antonio Krüger

CHI '16: ACM Conference on Human Factors in Computing Systems
Session: In-Air Gesture

Abstract
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.

DOI:: http://dx.doi.org/10.1145/2858036.285...
WEB:: https://chi2016.acm.org/

Recorded at the 2016 CHI Conference on Human Factors in Computing Systems in San Jose, CA, United States, May 7-12, 2016