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Predicting External Hand Forces During Overhead Work: An Approach Using EMG and Random Forest Regression

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dc.contributor.authorBehjati Ashtiani, Mohamaden
dc.contributor.authorFreidouny, Mohammadrezaen
dc.contributor.authorOjelade, Aanuoluwapoen
dc.contributor.authorKim, Sunwooken
dc.contributor.authorNussbaum, Maury A.en
dc.date.accessioned2024-09-19T19:38:41Zen
dc.date.available2024-09-19T19:38:41Zen
dc.date.issued2024-08-29en
dc.description.abstractWe developed a predictive model to estimate dynamic external hand forces during overhead tasks while wearing arm-support exoskeletons (ASEs). Despite the reported potential of ASEs to reduce muscle activation during overhead work, challenges in EMG sensor placement hinder comprehensive muscle monitoring. ASE effectiveness can be assessed by estimating shoulder forces through inverse dynamics, which requires external forces and body kinematics. Direct measurement of external forces can be quite challenging in practice. However, a predictive model could support estimating these forces without load cells. Participants completed task simulations using ASEs, while muscle activity and external forces were measured. Employing a random forest algorithm, EMG signals were mapped to force time series, accounting for participant characteristics and task parameters. Mean load cell values were 7.6 (SD 30.5) N, while predicted values were 7.6 (SD 22.7) N, affirming the potential of using EMG signals to estimate external hand forces while using ASEs.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1177/10711813241264257en
dc.identifier.eissn2169-5067en
dc.identifier.issn1071-1813en
dc.identifier.orcidKim, Sun Wook [0000-0003-3624-1781]en
dc.identifier.orcidNussbaum, Maury [0000-0002-1887-8431]en
dc.identifier.orcidOjelade, Aanuoluwapo [0000-0001-9715-3254]en
dc.identifier.urihttps://hdl.handle.net/10919/121165en
dc.language.isoenen
dc.publisherSageen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectExoskeletonsen
dc.titlePredicting External Hand Forces During Overhead Work: An Approach Using EMG and Random Forest Regressionen
dc.title.serialProceedings of the Human Factors and Ergonomics Society Annual Meetingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-groupVirginia Tech/Faculty of Health Sciencesen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-groupVirginia Tech/Graduate studentsen
pubs.organisational-groupVirginia Tech/Graduate students/Doctoral studentsen

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