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sEMG-Based Hand-Gesture Classification Using a Generative Flow Model
Open AccessArticle

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

1
Department of Computer and Electrical Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec, QC G1V 0A6, Canada
2
Department of Computer Science and Software Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec, QC G1V 0A6, Canada
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(12), 2811; https://doi.org/10.3390/s19122811
Received: 16 May 2019 / Revised: 15 June 2019 / Accepted: 20 June 2019 / Published: 24 June 2019
(This article belongs to the Special Issue EMG Sensors and Applications)
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Abstract

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( p < 0.05 ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository. View Full-Text
Keywords: acquisition system; surface electromyogram; sEMG; wearable sensors; gesture recognition acquisition system; surface electromyogram; sEMG; wearable sensors; gesture recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Côté-Allard, U.; Gagnon-Turcotte, G.; Laviolette, F.; Gosselin, B. A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition. Sensors 2019, 19, 2811.

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