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Tatsuya Daikoku, Yuji Takahashi, Nagayoshi Tarumoto and Hideki Yasuda

Statistical learning is thought to be a domain-general, implicit, and automatic learning process that lies at the heart of the adaptive behavioral repertoire of every complex organism ( Perruchet & Pacton, 2006 ; Saffran, Aslin, & Newport, 1996 ). Auditory statistical learning can be retained even

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Reed D. Gurchiek, Hasthika S. Rupasinghe Arachchige Don, Lasanthi C. R. Pelawa Watagoda, Ryan S. McGinnis, Herman van Werkhoven, Alan R. Needle, Jeffrey M. McBride and Alan T. Arnholt

Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v 0 and τ, which indicate a sprinter’s maximal theoretical velocity and the time it takes to approach v 0, respectively. This study aims to automate sprint assessment by estimating v 0 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 v 0, τ, and 30-m sprint time (t 30) 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 v 0, .14 to .17 seconds for τ, and .23 to .34 seconds for t 30. Model-derived sprint performance metrics from most regression models were significantly (P < .01) correlated with t 30. Comparison of the proposed method and a physics-based method suggests pursuit of a combined approach because their strengths appear to complement each other.

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ARTICLES Auditory Statistical Learning During Concurrent Physical Exercise and the Tolerance for Pitch, Tempo, and Rhythm Changes Tatsuya Daikoku * Yuji Takahashi * Nagayoshi Tarumoto * Hideki Yasuda * 22 3 233 244 10.1123/mc.2017-0006 mc.2017-0006 Improvements in Obstacle Clearance

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Eduardo Salazar, Mayank Gupta, Meynard Toledo, Qiao Wang, Pavan Turaga, James M. Parish and Matthew P. Buman

. , & Friedman , J.H. ( 2009 ). The elements of statistical learning: Data mining, inference, and prediction (Vol. 2 , 7th ed. ). New York, NY : Springer . Retrieved from

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Dan Weaving, Clive Beggs, Nicholas Dalton-Barron, Ben Jones and Grant Abt

Med . 2017 ; 51 : 209 – 210 . PubMed ID: 27650255 doi:10.1136/bjsports-2016-096589 10.1136/bjsports-2016-096589 27650255 31. James G , Witten D , Hastie T , Tibshirani R . An Introduction to Statistical Learning With Applications in R. Springer Texts in Statistics . New York, NY

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Denny Meyer, Madawa W. Jayawar, Samuel Muir, David Ho and Olivia Sackett

S . MMRM versus MI in dealing with missing data—a comparison based on 25 NDA data sets . J Biopharm Stat . 2011 ; 21 ( 3 ): 423 – 436 . PubMed ID: 21442517 10.1080/10543401003777995 21442517 22. Hastie T , Tishirani R , Friedman J . The Elements of Statistical Learning: Data Mining

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Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan and Lisa Mackay

expenditure in human physical activities . Med Sci Sports Exerc . 2012 ; 44 ( 11 ): 2138 – 2146 . PubMed ID: 22617402 doi:10.1249/MSS.0b013e31825e825a 10.1249/MSS.0b013e31825e825a 22617402 85. Friedman J , Hastie T , Tibshirani R . The Elements of Statistical Learning . Vol 1: Springer Series in