Detecting differences in gait initiation between older adult fallers and non-fallers through time-series principal component analysis (PCA)
Date
2022-01-04
Authors
Yoshida, Kaya
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Gait initiation (GI) is an important locomotor transition task that includes anticipatory postural adjustments and the joint propulsion necessary for the first step of walking. Metrics associated with this task are known to change across the lifespan and may provide valuable information for fall risk indication, as falls often occur during transitional tasks. Assessments of discrete variables between fallers and non-fallers at GI have provided insight into differences between groups. However, more complex approaches such as time-series principal component analysis (PCA) may allow the examination of changes in magnitude, pattern, and timing not detectable using discrete comparisons alone. Therefore, this thesis aims to characterize differences between fallers and non-fallers by examining the kinematics and kinetics of gait initiation using time-series PCA. A sample of 56 community-dwelling older adults was recruited for this study and completed five walking trials where GI was measured by two force platforms. PCA of centre of pressure kinematics and kinetics time-series data were used to identify the critical features of the signal, and multivariate analysis of covariance was used to compare the individual loading scores of each principal component for each phase between groups. It was revealed that fallers demonstrated differences in the range of mediolateral movement during weight transfer and forward progression, a greater range of anteroposterior movement in forward progression, and a more gradual rise in vertical forces in the first step, associated with a shorter first step length. These findings point to a tendency for fallers to prioritize stability over forward progression performance, and differences in postural control strategies, compared to non-fallers. Further, the use of time-series PCA helped to highlight differences not detectable using discrete analysis alone.
Description
Keywords
Gait initiation, Biomechanics, Principal Component Analysis, Fall risk