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

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

doi:10.1145/2858036.2858093

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

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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

100 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery
Publication date	2016
Pages	2332-2342
ISBN (Print)	978-1-4503-3362-7
DOIs	https://doi.org/10.1145/2858036.2858093
Publication status	Published - 2016
Event	2016 CHI Conference on Human Factors in Computing Systems - San Jose, United States Duration: 7 May 2016 → 12 May 2016 http://chi2016.acm.org/wp/

Conference

Conference	2016 CHI Conference on Human Factors in Computing Systems
Country/Territory	United States
City	San Jose
Period	07/05/2016 → 12/05/2016
Internet address	http://chi2016.acm.org/wp/

Keywords

electromyography (emg), force sensitive resistors, hand gestures, practical wearable device design, pressure

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10.1145/2858036.2858093

http://doi.acm.org/10.1145/2858036.2858093

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@inproceedings{a665813be1ad4612bd1308ce8f58db5a,

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 L{\"o}chtefeld and Antonio Kr{\"u}ger",

year = "2016",

doi = "10.1145/2858036.2858093",

language = "English",

isbn = "978-1-4503-3362-7",

pages = "2332--2342",

booktitle = "Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery",

address = "United States",

note = "2016 CHI Conference on Human Factors in Computing Systems ; Conference date: 07-05-2016 Through 12-05-2016",

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McIntosh, J, McNeill, C, Fraser, M, Kerber, F, Löchtefeld, M & Krüger, A 2016, EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing. in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, pp. 2332-2342, 2016 CHI Conference on Human Factors in Computing Systems, San Jose, United States, 07/05/2016. https://doi.org/10.1145/2858036.2858093

EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing. / McIntosh, Jess; McNeill, Charlie; Fraser, Mike et al.
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery, 2016. p. 2332-2342.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T1 - EMPress

T2 - 2016 CHI Conference on Human Factors in Computing Systems

AU - McIntosh, Jess

AU - McNeill, Charlie

AU - Fraser, Mike

AU - Kerber, Frederic

AU - Löchtefeld, Markus

AU - Krüger, Antonio

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - electromyography (emg), force sensitive resistors, hand gestures, practical wearable device design, pressure

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DO - 10.1145/2858036.2858093

M3 - Article in proceeding

SN - 978-1-4503-3362-7

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BT - Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

CY - New York, NY, USA

Y2 - 7 May 2016 through 12 May 2016

ER -

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

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