A machine learning approach of predicting high potential archers by means of physical fitness indicators-Reference-Cited by-同舟云学术

A machine learning approach of predicting high potential archers by means of physical fitness indicators

Published:2019-01-03 Issue:1 Volume:14 Page:e0209638
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Muazu Musa Rabiu^ORCID,P. P. Abdul Majeed Anwar,Taha Zahari,Chang Siow Wee,Ab. Nasir Ahmad Fakhri,Abdullah Mohamad Razali

Funder

National Sports Institute of Malaysia

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference30 articles.

1. The Application of Principal Components Analysis to Recognize Essential Physical Fitness Components among Youth Development Archers of Terengganu, Malaysia;RM Musa;Indian J Sci Technol. Indian Journal of Science and Technology,2016

2. Electromyographic analysis of bow string release in highly skilled archers;PE Martin;J Sports Sci,1990

3. Activation patterns in forearm muscles during archery shooting;H Ertan;Hum Mov Sci,2003

4. Archery performance level and repeatability of event-related EMG;AR Soylu;Hum Mov Sci,2006

5. Time–motion analysis of elite field hockey, with special reference to repeated-sprint activity;M Spencer;J Sports Sci,2004

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