Generalizing perceived fatigue estimation across diverse upper limb tasks using minimal wearable sensors

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dc.contributor.authorQirtas, Malik Muhammaden
dc.contributor.authorSica, Marcoen
dc.contributor.authorYasar, Merve Nuren
dc.contributor.authorO'Sullivan, Patriciaen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.authorMenolotto, Matteoen
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderResearch Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-10-16T09:00:23Z
dc.date.available2025-10-16T09:00:23Z
dc.date.issued2025-08-07en
dc.description.abstractAccurately estimating perceived fatigue from wearable sensor data is a challenge, especially across diverse tasks. This letter presents a generalized framework to predict estimated fatigue scores (measured using the Borg scale) using combined electromyography and inertial measurement units data collected from two independent upper limb datasets. Our best model achieved a mean absolute error of 2.35 and a mean absolute percentage error of 18.60% using only five strategically placed sensors. A broad set of biomechanical features was extracted to capture both kinematic and neuromuscular indicators of fatigue. Vertical acceleration of the upper arm and shoulder, along with spectral features from deltoid EMG, emerged as the most consistent predictors across tasks. These findings support interpretable and generalizable fatigue detection and provide a foundation for real-time monitoring systems in sports, rehabilitation, and occupational health.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQirtas, M.M., Sica, M., Yasar, M.N., O'Sullivan, P., O’Flynn, B., Tedesco, S., Menolotto, M. and Visentin, A. (2025) 'Generalizing perceived fatigue estimation across diverse upper limb tasks using minimal wearable sensors', IEEE Sensors Letters, 9(9), pp. 1–4. https://doi.org/10.1109/LSENS.2025.3596719en
dc.identifier.doi10.1109/LSENS.2025.3596719en
dc.identifier.eissn2475-1472en
dc.identifier.endpage4en
dc.identifier.issued9en
dc.identifier.journaltitleIEEE Sensors Lettersen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18044
dc.identifier.volume9en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/21/RC/10303_P2/IE/VistaMilk Phase II/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2025, the Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectMechanical sensorsen
dc.subjectEMGen
dc.subjectFatigue estimationen
dc.subjectIMUen
dc.subjectMachine learningen
dc.subjectSensorsen
dc.titleGeneralizing perceived fatigue estimation across diverse upper limb tasks using minimal wearable sensorsen
dc.typeArticle (peer-reviewed)en
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