Gait & posture · 2026
Exploring running classifications using foot inclination angle and ground reaction force clustering methods: Do traditional foot strike categories capture meaningful patterns in ground reactions forces?
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Vannatta C Nathan, Tara Vivek Kumar, Kernozek Thomas W
Аннотация
BACKGROUND: Footstrike pattern has been a factor considered in various aspects of running injury and performance. Different features of the ground reaction forces are thought to vary across footstrike patterns. However, comprehensive analyses of ground reaction forces across these patterns have been limited and novel analysis techniques may now allow for additional insights into underlying biomechanical features not captured with traditional methods.
RESEARCH QUESTION: Do kinematic footstrike classifications capture underlying ground reaction force patterns detected with clustering techniques?
METHODS: 103 (47 male) collegiate cross-country runners completed overground running trials at their self-selected training pace. Ground reaction forces were collected over five trials of right foot contacts. 3D motion capture was utilized to assess foot inclination angle and classified as rearfoot strike (RFS), mid-foot strike (MFS), or forefoot strike (FFS). Principle components analysis and hierarchical agglomerative clustering (HAC) were used to identify clusters derived from ground reaction force features (i.e., loading rate and impact peak). Agreement between classification schemes (foot inclination angle vs. ground reaction force clusters) were then assessed.
RESULTS: MFS was the most prevalent (n = 58, 56.3%), followed by RFS (n = 28, 27.2%), then FFS (n = 17, 16.5%). Five clusters were derived from HAC. RFS, MFS, and FFS patterns were distributed across each of the clusters derived from HAC. RFS and FFS patterns tended to cluster together whereas MFS patterns were distributed primarily across two clusters. Ground reaction force time series were distinct for each cluster with differences more apparent in the high frequency domain.
SIGNIFICANCE: Ground reaction force patterns may have more intricate differences than kinematic classifications may be able to detect.
RESEARCH QUESTION: Do kinematic footstrike classifications capture underlying ground reaction force patterns detected with clustering techniques?
METHODS: 103 (47 male) collegiate cross-country runners completed overground running trials at their self-selected training pace. Ground reaction forces were collected over five trials of right foot contacts. 3D motion capture was utilized to assess foot inclination angle and classified as rearfoot strike (RFS), mid-foot strike (MFS), or forefoot strike (FFS). Principle components analysis and hierarchical agglomerative clustering (HAC) were used to identify clusters derived from ground reaction force features (i.e., loading rate and impact peak). Agreement between classification schemes (foot inclination angle vs. ground reaction force clusters) were then assessed.
RESULTS: MFS was the most prevalent (n = 58, 56.3%), followed by RFS (n = 28, 27.2%), then FFS (n = 17, 16.5%). Five clusters were derived from HAC. RFS, MFS, and FFS patterns were distributed across each of the clusters derived from HAC. RFS and FFS patterns tended to cluster together whereas MFS patterns were distributed primarily across two clusters. Ground reaction force time series were distinct for each cluster with differences more apparent in the high frequency domain.
SIGNIFICANCE: Ground reaction force patterns may have more intricate differences than kinematic classifications may be able to detect.