Generalizing Perceived Fatigue Estimation Across Diverse Upper Limb Tasks Using Minimal Wearable Sensors

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.