Authors
Yasunori Takemura1, *, Kazuya Oda2, Michiyoshi Ono1
1
Department of Engineering, Nishinippon Institute of Technology, Miyako,
Fukuoka 800-0344, Japan
2
Department of Life Science and System Engineering, Kyushu Institute of
Technology, Kitakyushu, Fukuoka 808-0896, Japan
*
Corresponding author. Email: [email protected]
Corresponding Author
Yasunori Takemura
Received 9 April 2018, Accepted 15 November 2018, Available Online 1 December
2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.3.12
Keywords
Sports science; SOM; machine learning; clustering
Abstract
In Japan, sports efforts are actively being carried out to host the 2020
Olympic Games. Especially in the field of sports science, researches on
ergonomics, development of sports equipment and pattern recognition technology
using artificial intelligence are actively researched. In previous research,
we developed a clustering algorithm for positioning adaptation and relationships
in team sports using self-organizing maps in university rugby players.
However, I have not yet confirmed whether the developed algorithm can be
applied to other team sports. For this reason, we applied the same algorithm
to a university volleyball player. Then, as an algorithm, we verify whether
it can be generally used for team sports.
Copyright
© 2018 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).