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 inter-individual 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.