Generalizing perceived fatigue estimation across diverse upper limb tasks using minimal wearable sensors
Loading...
Date
2025-08-07
Authors
Qirtas, Malik Muhammad
Sica, Marco
Yasar, Merve Nur
O'Sullivan, Patricia
O'Flynn, Brendan
Tedesco, Salvatore
Menolotto, Matteo
Visentin, Andrea
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Accurately 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.
Description
Keywords
Mechanical sensors , EMG , Fatigue estimation , IMU , Machine learning , Sensors
Citation
Qirtas, 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.2025.3596719