Abstract
In the diverse landscape of process industries, operators frequently undertake solitary activities such as routine inspections, instrument calibration, emergency response, equipment maintenance, sampling and testing, and isolation procedures. The heightened focus on physical fatigue management in these operations has sparked a critical re-evaluation of current approaches. Currently, the prevalent method for assessing physical fatigue relies on subjective testing, primarily employing the Borg scale. However, the intrinsic limitations, characterized by low accuracy and a lack of real-time measurement, hinder its refinement and effectiveness. Motivated by the potential of objective measures, this research introduces an innovative approach to measuring physical fatigue. Specifically, a smartwatch equipped to collect electrodermal activity, skin temperature, pulse rate, and motion parameters is employed. Data is gathered in a fitness environment simulating industrial tasks, providing valuable insights into physical fatigue dynamics. The objective data collected are then subjected to Principal Component Analysis (PCA) to derive two principal components: one related to physiological aspects and the other associated with motion dimensions. Subsequent linear regression analyses utilize these subscales to establish a physical fatigue-weighted scale based solely on factual data. The derived physical fatigue scale reveals promising applications as a customized warning system, exclusively leveraging a non-intrusive smartwatch.
