A Principal Components Analysis Approach to Quantifying Foot Clearance and Foot Clearance Variability.

Area of Science:

• Biomechanics
• Gait Analysis
• Gerontology
• Neurology

Background:

• Low foot clearance and high stride-to-stride variability are associated with increased fall risk in various populations.
• Traditional measures of foot clearance, like the minimum toe clearance, may not capture all gait cycles, as not every stride exhibits a distinct local minimum.

Purpose of the Study:

• To develop and validate a nondiscrete measure of foot clearance applicable to all walking strides.
• To compare the efficacy of discrete and nondiscrete measures in assessing individual foot clearance and variability.
• To investigate the association between a novel principal components analysis (PCA) approach and traditional discrete measures.

Main Methods:

• Recruited 35 participants: young adults, older fallers, older non-fallers, and stroke survivors.
• Recorded 3D lower extremity kinematics during overground walking.
• Applied Principal Components Analysis (PCA) to the toe height waveform to represent foot clearance.
• Used Spearman's rank order correlation to compare PCA-derived measures with discrete foot clearance variables.

Main Results:

• PCA demonstrated significant moderate to strong associations with discrete measures of foot clearance and variability (P < .05).
• A discrete local minimum approximation showed weak correlations with PCA and other discrete foot clearance measures.
• PCA effectively quantifies the behavioral components of toe height closest to the ground across all strides.

Conclusions:

• A PCA-based method provides a robust, nondiscrete measure of foot clearance during walking.
• This approach is superior to discrete measures for characterizing foot clearance and variability, especially in strides lacking a local minimum.
• The findings offer a more comprehensive understanding of gait parameters relevant to fall risk assessment.