Estimating Perceived Fatigue Using Machine Learning and Biomechanical Features from Wearable Sensors

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.