Quantitative Analysis of Human Movement Synergies: Constructive Pattern Analysis for Gait

Abstract

To record three-dimensional coordinates of the joints from normal human subjects during locomotion, we used a digital motion analysis system (ELITE). Recordings were obtained under several different conditions, which included normal walking and stepping over obstacles. Principal component analysis was used to analyze coordinate data after conversion of the data to segmental angles. This technique gave a stable summary of the redundancy in gait kinematic data in the form of reduced variables (principal components). By modeling the shapes of the phase plots of reduced variables (distortion analysis) and using a limited number of model parameters, good resolution was obtained between subtly different conditions. Hence, it was possible to accurately resolve small distributed changes in gait patterns within subjects. These methods seem particularly suited to longitudinal studies in which relevant movement features are not known a priori. Assumptions and neurophysiological applications are discussed.

Contributions of altered sensation and feedback responses to changes in coordination of postural control due to aging

“…Moreover, covariance ellipses provide limited information about relative timing of joint motion and time delays in propagation and pro-cessing of neural signals. More information about timing could be obtained through more sophisticated methods of analyzing multijoint movement, such as cross-correlations [31], principal component analysis [32,33], and system identification. In addition, we have also neglected to measure motion of the knees and neck, and have assumed the trunk to be rigid.…”

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

“…In these applications, PCA is often applied to gait waveform data in one of two manners: (a) conduct one PCA for each subject using a joints/muscles/segments × time matrix (e.g., Bennett et al, 2010;Herr & Popovic, 2008;Mah et al, 1994;Raptopoulos et al, 2006); or (b) conduct one PCA for each joint/muscle/segment using a subjects × time matrix (e.g., Ivanenko et al, 2004;Laassel et al, 1992;Loslever et al, 1994;Sadeghi et al, 2001). In addition, some researchers (Troje, 2002) have used a two-stage PCA approach to model both within-and between-subject gait pattern variation.…”

Motion pattern analysis of gait in horseback riding by means of Principal Component Analysis

“…PCA is datadriven, implying that its results are inherently a function of the data set to which it is applied. Mah, Hulliger, and Lee (1994) used PCA for a constructive analysis of gait patterns.…”

“…The shapes of the phase portraits of these variables allowed gait discrimination under different neurophysiological conditions (Mah et al, 1994).…”