Acceleration-based Clustering Reveals Frequent Gait Switching in Sprint Sled Dogs.

Continuous video is difficult to obtain during field studies of sprint sled dogs, limiting analysis of stride-to-stride variation during load-pulling gallop. We developed an acceleration-based pipeline to identify recurrent stride states from harness-mounted tri-axial accelerometers without manual gait labels. Using multivariate dynamic time warping, manifold embedding, and density-based clustering, we analyzed more than 20,000 strides from a 10-dog team and identified recurrent, dog-specific stride states. In one previously annotated individual, acceleration-derived states were broadly consistent with manually labeled gallop patterns. Across dogs, transitions between stride states were frequent, with substantial interindividual variation and limited evidence of strong team-level coordination. A simple logistic model based on local tugline-force timing and magnitude had weak predictive power for transition events. These results suggest that sprint sled dog gallop occupies a variable set of nearby stride states and that local tugline-force fluctuations alone do not explain the observed switching.