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Processing of real-time surface electromyography signals during knee movements of rehabilitation participants

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 14 No. 6 (2024)

DOI
10.11591/ijece.v14i6.pp6526-6537
ISSN
2722-2578, 2088-8708
Published
2024-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.

Collaboration network of the authors

9 authors · 20 co-authorship links · 4 across different organisations

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  • Prince of Songkla University
  • University of Aizu
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Vasin ChaobowornPrince of Songkla University1
Yoschanin SasiwatPrince of Songkla University1
Dujdow BuranapanichkitPrince of Songkla University1
Hiroshi SaitoUniversity of Aizu1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Processing of real-time surface electromyography signals during knee movements of rehabilitation participants</dc:title><dc:creator>Sengchuai, Kiattisak</dc:creator><dc:creator>Sittiruk, Thantip</dc:creator><dc:creator>Jindapetch, Nattha</dc:creator><dc:creator>Phukpattaranont, Pornchai</dc:creator><dc:creator>Booranawong, Apidet</dc:creator><dc:description>&lt;jats:p&gt;In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2024-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v14i6.pp6526-6537</dc:identifier><dc:identifier>10.11591/ijece.v14i6.pp6526-6537</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 14 No 6 (2024); 6526</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/36637/17808</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>