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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.<\/jats:p>","DOI":"10.1093\/icb\/icag110","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T11:28:29Z","timestamp":1783337309000},"source":"Crossref","is-referenced-by-count":0,"title":["Acceleration-based Clustering Reveals Frequent Gait Switching in Sprint Sled Dogs"],"prefix":"10.1093","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5023-1972","authenticated-orcid":false,"given":"Benjamin","family":"Seleb","sequence":"first","affiliation":[{"name":"Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology , Atlanta, Georgia 30332 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