Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e., phenotypes) may exist. This study investigates the presence of gait fluctuation phenotypes. Whole-body kinematics were measured from young, healthy males and females (N = 51) while walking on a treadmill at their preferred speed. Motor fluctuation metrics (i.e., magnitude of variability, local dynamic stability, and regularity) were measured for 32 joint angles across the upper and lower body. These metrics were reduced to principal components (PCs) via principal component analysis and then grouped into clusters using the k-means method. One-way ANOVAs were conducted to test for cluster differences in motor fluctuation PCs. Three PCs were extracted, explaining 39.7 % of all 96 motor fluctuation metrics. Higher PC1 scores represent more fluctuation across all joints, higher PC2 scores represent greater upper limb fluctuations with fewer fluctuations in the lower limb, and PC3 scores represent less regularity in fluctuations. PC scores best grouped into four clusters in 54.0 % of iterations. Clusters 1-4 each had a significantly different PC1 score (p < 0.022), and Cluster 3 had a higher PC2 score than all other clusters (p < 0.022). Motor fluctuations in treadmill gait of young adults were characterised by four gait fluctuation phenotypes, interpreted as repeaters, replacers, moderate fluctuators, and mixed fluctuators (i.e. more upper limb but fewer lower limb fluctuations); extending the repeaters vs replacers hypothesis. The identified phenotypes add a new perspective that may help clarify the link between motor fluctuations and gait instability.
Keywords: Dynamic stability; Gait; K-means clustering; Kinematics; Machine learning; Motor variability.
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