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Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v0 and τ, which indicate a sprinter’s maximal theoretical velocity and the time it takes to approach v0, respectively. This study aims to automate sprint assessment by estimating v0 and τ using machine learning and accelerometer data. To this end, photocells recorded 10-m split times of 28 subjects for three 40-m sprints while wearing an accelerometer around the waist. Features extracted from the accelerometer data were used to train a classifier to identify the sprint start and regression models to estimate the sprint model parameters. Estimates of v0, τ, and 30-m sprint time (t30) were compared between the proposed method and a photocell method using root mean square error and Bland–Altman analysis. The root mean square error of the sprint start estimate was .22 seconds and ranged from .52 to .93 m/s for v0, .14 to .17 seconds for τ, and .23 to .34 seconds for t30. Model-derived sprint performance metrics from most regression models were significantly (P < .01) correlated with t30. Comparison of the proposed method and a physics-based method suggests pursuit of a combined approach because their strengths appear to complement each other.

Gurchiek, van Werkhoven, Needle, and McBride are with the Department of Health & Exercise Science, Appalachian State University, Boone, NC, USA. Rupasinghe Arachchige Don, Pelawa Watagoda, and Arnholt are with the Department of Mathematical Sciences, Appalachian State University, Boone, NC, USA. Gurchiek and McGinnis are with the Department of Electrical and Biomedical Engineering, The University of Vermont, Burlington, VT, USA.

Gurchiek (reed.gurchiek@uvm.edu) is corresponding author.
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