Eigenposes: Using principal components to describe body configuration for analysis of postural control dynamics

Abstract

Many studies of human postural control use data from video-captured discrete marker locations to analyze via complex inverse kinematic reconstruction the postural responses to a perturbation. We propose here that Principal Component Analysis of this marker data provides a simpler way to get an overview of postural perturbation responses. Using short (1, 4, and 16 mm) anterior platform step translations that are on the order of a young adult's normal sway path length, we find that the low order eigenmodes (which we call eigenposes) of the time-series marker data correspond dominantly to a simple anterior-posterior pendular motion about the ankle, and secondarily (and with less energy) to hip flexion and extension. A third much weaker mode is occasionally seen that is represented by knee flexion. © 2010 IEEE.