Sprint Assessment Using Machine Learning And A Wearable Accelerometer
- ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
- Alan Needle Ph.D., Assistant Professor (Creator)
- Institution
- Appalachian State University (ASU )
- Web Site: https://library.appstate.edu/
Abstract: Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v0 and t, 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 t 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, t, 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 t, 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.
Sprint Assessment Using Machine Learning And A Wearable Accelerometer
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Created on 10/29/2019
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Additional Information
- Publication
- Gurchiek, R. D., Rupasinghe Arachchige Don, H. S., Pelawa Watagoda, L. R., McGinnis, R. S., van Werkhoven, H., Needle, A. R., McBride, J. M., & Arnholt, A. T. (2019). Sprint Assessment Using Machine Learning and a Wearable Accelerometer, Journal of Applied Biomechanics, 35(2), 164-169. DOI: https://doi.org/10.1123/jab.2018-0107. Publisher version of record available at: https://journals.humankinetics.com/view/journals/jab/35/2/article-p164.xml
- Language: English
- Date: 2019
- Keywords
- wearable sensor, inertial sensor, sprint assessment, statistical learning