Search Results

You are looking at 1 - 6 of 6 items for :

  • "statistical learning" x
  • Sport and Exercise Science/Kinesiology x
Clear All
Restricted access

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

Restricted access

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.

Restricted access

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 http://ucsf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV1LSwMxEB50i1Av6mrxVcjB65Z95LVHkRYvIoIi9VKabCKCbsFtD_57

Restricted access

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

Restricted access

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

Restricted access

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