Tracking Physiological Fatigue in Prolonged Load Carriage Walking Using Phase Space Warping and Smooth Orthogonal Decomposition

Abstract

The ability to track and predict the onset of physiologic fatigue using easily measurable variables is of great importance to both civilian and military activities. In this paper, biomechanical gait variables are used to reconstruct fatigue evolution in subjects walking with a 40 kg load on a level treadmill for two hours. Fatigue is reconstructed in two steps: (1) phase space warping based feature vectors are estimated from gait variable time series; and (2) smooth orthogonal decomposition is used to extract fatigue related trends from these features. These results are verified using independently obtained measures of fatigue from breath-by-breath oxygen consumption (V˙O2) and surface electromyography (EMG) from a set of leg muscles. V˙O2 based measures for some subjects show no discernable trends. However, for a subject showing monotonically increasing oxygen consumption, the reconstructed dominant fatigue variable closely track V˙O2 measure reflecting global systemic fatigue. For the muscles showing variation in EMG-based fatigue measures, the reconstructed fatigue variables also closely track these local muscle trends. The results show that kinematic angles, which are easier quantities to measure in the field, can be used to track and predict the onset of fatigue.