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 noninvasive 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 elec-trography (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.