Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.
Keywords: Biomechanics; Clustering; Kinematics; Principal component analysis; Running gait.
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