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A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition
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, t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631507/ https://www.ncbi.nlm.nih.gov/pubmed/31238529 http://dx.doi.org/10.3390/s19122811 |
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author | Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laviolette, François Gosselin, Benoit |
author_facet | Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laviolette, François Gosselin, Benoit |
author_sort | Côté-Allard, Ulysse |
collection | PubMed |
description | 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 ([Formula: see text]) 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. |
format | Online Article Text |
id | pubmed-6631507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66315072019-08-19 A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laviolette, François Gosselin, Benoit Sensors (Basel) Article 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 ([Formula: see text]) 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. MDPI 2019-06-24 /pmc/articles/PMC6631507/ /pubmed/31238529 http://dx.doi.org/10.3390/s19122811 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laviolette, François Gosselin, Benoit A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title | A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title_full | A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title_fullStr | A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title_full_unstemmed | A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title_short | A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition |
title_sort | low-cost, wireless, 3-d-printed custom armband for semg hand gesture recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631507/ https://www.ncbi.nlm.nih.gov/pubmed/31238529 http://dx.doi.org/10.3390/s19122811 |
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