@inproceedings{82c079f273734aafb37755bf7bfd8010,
title = "EMPress: Practical hand gesture classification with wrist-mounted EMG and pressure sensing",
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.",
keywords = "Electromyography (EMG), Force sensitive resistors, Hand gestures, Practical wearable device design, Pressure",
author = "Jess McIntosh and Charlie McNeill and Mike Fraser and Frederic Kerber and Markus Lochtefeld and Antonio Kr{\"u}ger",
year = "2016",
month = may,
day = "31",
doi = "10.1145/2858036.2858093",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "2332--2342",
booktitle = "CHI '16: Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems",
address = "USA United States",
note = "34th Annual Conference on Human Factors in Computing Systems, CHI 2016 ; Conference date: 07-05-2016 Through 12-05-2016",
}