Machine learning physical fatigue estimation approach based on IMU and EMG wearable sensors

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dc.contributor.authorNair, Suraj P.en
dc.contributor.authorSica, Marcoen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-22T13:42:52Z
dc.date.available2025-01-22T13:42:52Z
dc.date.issued2024en
dc.description.abstractPhysical fatigue refers to a state of exhaustion or reduced capacity for physical performance due to prolonged exertion, repetitive movements, or lack of rest. It is a multifaceted condition that can severely impact performance, especially in activities requiring sustained effort, precision, or concentration. In physical tasks, fatigue manifests as a decrease in muscle strength, coordination, and endurance, leading to diminished performance and an increased risk of injury. Detecting physical fatigue is crucial in a variety of domains: professional sports, collaborative robotics, construction, and more. This research introduces a novel framework for predicting fatigue during shoulder movements using data collected from wearable inertial measurement units and electromyography sensors. By integrating the Borg Scale, a subjective measure of perceived exertion, our approach uniquely combines objective sensor data with user-reported fatigue levels, creating a more holistic fatigue assessment model. The primary aim of this study is to develop a predictive model capable of accurately estimating fatigue, as measured by the Borg Scale. An investigation of the best machine learning algorithm for this task ensures that the chosen method provides the most reliable predictions. Furthermore, by systematically reducing the number of sensors and analyzing the impact on model performance, it is possible to find a minimal sensor configuration that maintains the model’s predictive power while reducing complexity and cost. The Ridge Regression model, after hyperparameter tuning, outperformed other models, achieving a mean absolute error of 2.417 in predicting fatigue. This preliminary study shows the potential of integrating data from different inertial and electromyography sensors for fatigue prediction in shoulder movements, with potential applications in occupational safety.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNair, S. P., Sica, M., Tedesco, S. and Visentin, A. (2024) 'Machine Learning Physical Fatigue Estimation Approach Based on IMU and EMG Wearable Sensors', 32nd Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, December 9-10, 2024.en
dc.identifier.endpage12en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16876
dc.language.isoenen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Maternity/Adoptive Leave Allowance/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HE::HORIZON-IA/101092989/EU/DATA Monetization, Interoperability, Trading & Exchange/DATAMITEen
dc.rights© 2022, the Authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectFatigue estimationen
dc.subjectWearable sensorsen
dc.subjectMachine learningen
dc.subjectFeature selectionen
dc.titleMachine learning physical fatigue estimation approach based on IMU and EMG wearable sensorsen
dc.typeConference itemen
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