Dynamical analysis of sawing motion tracks muscle fatigue evolution
Date of Original Version
Both for civilian and military applications, tracking and identifying muscle fatigue-usually caused by continuous, repetitive motion over a finite period of time-is of great importance. The muscle fatigue process is very difficult to track due to its hidden nature. Invasive procedures are often needed to measure fatigue. Here, easily obtainable nonin-vasive kinematic measurements are used to extract muscle fatigue related trends associated with a sawing motion. The methodology is derived from dynamical systems based fatigue identification in engineered systems. Ten right-handed subjects perform sawing motion until voluntary exhaustion. Three sets of joint kinematic angles are measured from the elbow, wrist, and shoulder. Fatigue is identified in two steps: (1) phase space warping based feature vectors are estimated from kinematic time series; and (2) smooth orthogonal decomposition (SOD) is used to extract fatigue related trends from these features. SOD-based trends are compared against independently obtained fatigue markers estimated from the mean and median frequencies of electrography (EMG) signals of individual muscles. SOD-based trends from elbow and shoulder kinematics adequately capture fatigue in the triceps muscle estimated from the EMG measurements. These same kinematic angles show little fatigue information in the flexor/extensor carpi radialis (not directly engaged in sawing motion). The methodology used here shows great potential in tracking individual muscle fatigue evolution using only motion kinematics data. © 2009 by ASME.
Proceedings of the ASME Design Engineering Technical Conference
PARTS A, B AND C
Segala, David B., David Chelidze, Deanna Gates, and Jonathan Dingwell. "Dynamical analysis of sawing motion tracks muscle fatigue evolution." Proceedings of the ASME Design Engineering Technical Conference 4, PARTS A, B AND C (2009): 1593-1599. doi:10.1115/DETC2009-87823.