Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint

Bibliographic Details
Title:	Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint
Authors:	Handdeut Chang, Seulki Kyeong, Youngjin Na, Yeongjin Kim, Jung Kim
Source:	IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2654-2664 (2023)
Publisher Information:	IEEE, 2023.
Publication Year:	2023
Collection:	LCC:Medical technology LCC:Therapeutics. Pharmacology
Subject Terms:	Surface electromyography, decomposition, elbow, torque estimation, rescaling method, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
More Details:	In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	1558-0210
Relation:	https://ieeexplore.ieee.org/document/10146313/; https://doaj.org/toc/1558-0210
DOI:	10.1109/TNSRE.2023.3281410
Access URL:	https://doaj.org/article/cc05f4f0493e424da7fe0486007d063a
Accession Number:	edsdoj.05f4f0493e424da7fe0486007d063a
Database:	Directory of Open Access Journals

FullText	Links: – Type: other Url: https://resolver.ebsco.com:443/public/rma-ftfapi/ejs/direct?AccessToken=423297014DA6771387FE&Show=Object Text: Availability: 0 CustomLinks: – Url: https://login.libproxy.scu.edu/login?url=http://ieeexplore.ieee.org/search/searchresult.jsp?action=search&newsearch=true&queryText=%22DOI%22:10.1109/TNSRE.2023.3281410 Name: EDS - IEEE (s8985755) Category: fullText Text: Check IEEE Xplore for full text MouseOverText: Check IEEE Xplore for full text. A new window will open. – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=15580210&ISBN=&volume=31&issue=&date=20230101&spage=2654&pages=2654-2664&title=IEEE Transactions on Neural Systems and Rehabilitation Engineering&atitle=Development%20of%20a%20High-SNR%20Stochastic%20sEMG%20Processor%20in%20a%20Multiple%20Muscle%20Elbow%20Joint&aulast=Handdeut%20Chang&id=DOI:10.1109/TNSRE.2023.3281410 Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries – Url: https://doaj.org/article/cc05f4f0493e424da7fe0486007d063a Name: EDS - DOAJ (s8985755) Category: fullText Text: View record from DOAJ MouseOverText: View record from DOAJ
Header	DbId: edsdoj DbLabel: Directory of Open Access Journals An: edsdoj.05f4f0493e424da7fe0486007d063a RelevancyScore: 980 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 979.677185058594
IllustrationInfo
Items	– Name: Title Label: Title Group: Ti Data: Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Handdeut+Chang%22">Handdeut Chang</searchLink><br /><searchLink fieldCode="AR" term="%22Seulki+Kyeong%22">Seulki Kyeong</searchLink><br /><searchLink fieldCode="AR" term="%22Youngjin+Na%22">Youngjin Na</searchLink><br /><searchLink fieldCode="AR" term="%22Yeongjin+Kim%22">Yeongjin Kim</searchLink><br /><searchLink fieldCode="AR" term="%22Jung+Kim%22">Jung Kim</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2654-2664 (2023) – Name: Publisher Label: Publisher Information Group: PubInfo Data: IEEE, 2023. – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: LCC:Medical technology<br />LCC:Therapeutics. Pharmacology – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Surface+electromyography%22">Surface electromyography</searchLink><br /><searchLink fieldCode="DE" term="%22decomposition%22">decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22elbow%22">elbow</searchLink><br /><searchLink fieldCode="DE" term="%22torque+estimation%22">torque estimation</searchLink><br /><searchLink fieldCode="DE" term="%22rescaling+method%22">rescaling method</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+technology%22">Medical technology</searchLink><br /><searchLink fieldCode="DE" term="%22R855-855%2E5%22">R855-855.5</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%2E+Pharmacology%22">Therapeutics. Pharmacology</searchLink><br /><searchLink fieldCode="DE" term="%22RM1-950%22">RM1-950</searchLink> – Name: Abstract Label: Description Group: Ab Data: In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article – Name: Format Label: File Description Group: SrcInfo Data: electronic resource – Name: Language Label: Language Group: Lang Data: English – Name: ISSN Label: ISSN Group: ISSN Data: 1558-0210 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://ieeexplore.ieee.org/document/10146313/; https://doaj.org/toc/1558-0210 – Name: DOI Label: DOI Group: ID Data: 10.1109/TNSRE.2023.3281410 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/cc05f4f0493e424da7fe0486007d063a" linkWindow="_blank">https://doaj.org/article/cc05f4f0493e424da7fe0486007d063a</link> – Name: AN Label: Accession Number Group: ID Data: edsdoj.05f4f0493e424da7fe0486007d063a
PLink	https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.05f4f0493e424da7fe0486007d063a
RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TNSRE.2023.3281410 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 2654 Subjects: – SubjectFull: Surface electromyography Type: general – SubjectFull: decomposition Type: general – SubjectFull: elbow Type: general – SubjectFull: torque estimation Type: general – SubjectFull: rescaling method Type: general – SubjectFull: Medical technology Type: general – SubjectFull: R855-855.5 Type: general – SubjectFull: Therapeutics. Pharmacology Type: general – SubjectFull: RM1-950 Type: general Titles: – TitleFull: Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Handdeut Chang – PersonEntity: Name: NameFull: Seulki Kyeong – PersonEntity: Name: NameFull: Youngjin Na – PersonEntity: Name: NameFull: Yeongjin Kim – PersonEntity: Name: NameFull: Jung Kim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 15580210 Numbering: – Type: volume Value: 31 Titles: – TitleFull: IEEE Transactions on Neural Systems and Rehabilitation Engineering Type: main
ResultId	1