Estimating perceived fatigue using machine learning and biomechanical features from wearable sensors
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Date
2025-07-03
Authors
Qirtas, Malik Muhammad
Yasar, Merve Nur
Sica, Marco
Tedesco, Salvatore
Visentin, Andrea
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Physical 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.
Description
Keywords
Wearable sensors , Fatigue estimation , Biomechanical features , Inertial measurement unit , Electromyography , Machine learning
Citation
Qirtas, 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/SMARTCOMP65954.2025.00083