Estimating perceived fatigue using machine learning and biomechanical features from wearable sensors

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dc.contributor.authorQirtas, Malik Muhammaden
dc.contributor.authorYasar, Merve Nuren
dc.contributor.authorSica, Marcoen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderHORIZON EUROPE Digital, Industry and Spaceen
dc.date.accessioned2025-11-12T11:58:01Z
dc.date.available2025-11-12T11:58:01Z
dc.date.issued2025-07-03en
dc.description.abstractPhysical fatigue is a state of reduced physical ability caused by prolonged activity or repetitive tasks. It can affect performance in tasks that require effort, focus or precision and can lead to reduced strength and increased risk of injuries. Detecting physical fatigue is important for timely interventions and enhancing safety, efficiency and overall well-being in workplaces, sports and rehabilitation. In this study, we propose a robust and generalizable framework for fatigue detection using wearable sensor data, specifically using Inertial Measurement Unit and Electromyography sensors. A comprehensive set of biomechanical features was extracted from raw sensor data to capture both kinematic and neuromuscular aspects of fatigue progression. These features were evaluated across shoulder internal rotation and external rotation movements under different resistance levels. We trained and compared multiple regression models for fatigue estimation using subjective fatigue ratings based on the Borg Rating of Perceived Exertion scale and performed feature importance analysis to get model interpretability. The extracted feature set showed strong generalizability specifically for IR movements, as proved by leave one task out cross-validation, where models maintained robust performance across unseen movement-resistance task settings. This work highlights the potential of combining IMU and EMG data, along with biomechanical features extracted from these two sensor modalities for accurate and interpretable fatigue estimation. It opens the way for real-world applications in dynamic and diverse environments for effective fatigue estimation.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2289- P2-INSIGHT2; 13/RC/2077-CONNECT; 21/RC/10303 P2- VISTAMILK2; 16/RC/3918); Horizon Digital, Industry and Space (101092989- DATAMITE)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQirtas, M. M. , Yasar, M. N., Sica, M., Tedesco, S. and Visentin, A. (2025) 'Estimating perceived fatigue using machine learning and biomechanical features from wearable sensors', 2025 IEEE International Conference on Smart Computing (SMARTCOMP), Cork, Ireland, 16-19 June 2025, pp. 342-347. https://doi.org/10.1109/SMARTCOMP65954.2025.00083en
dc.identifier.doi10.1109/smartcomp65954.2025.00083en
dc.identifier.eissn2693-8340en
dc.identifier.endpage347en
dc.identifier.isbn979-8-3315-8646-1en
dc.identifier.isbn979-8-3315-8647-8en
dc.identifier.issn2693-8332en
dc.identifier.startpage342en
dc.identifier.urihttps://hdl.handle.net/10468/18195
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2025 IEEE International Conference on Smart Computing (SMARTCOMP), Cork, Ireland, 16-19 June 2025en
dc.rights© 2025, IEEE. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectWearable sensorsen
dc.subjectFatigue estimationen
dc.subjectBiomechanical featuresen
dc.subjectInertial measurement uniten
dc.subjectElectromyographyen
dc.subjectMachine learningen
dc.subject~Tyndall National Institute - Conference Items~en
dc.titleEstimating perceived fatigue using machine learning and biomechanical features from wearable sensorsen
dc.typeConference itemen
dc.typeproceedings-articleen
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