KARATECH: A Practice Support System Using an Accelerometer to Reduce the Preliminary Actions of Karate

Author:

Kim Kwangyun1,Tsuchida Shuhei2ORCID,Terada Tsutomu1ORCID,Tsukamoto Masahiko1ORCID

Affiliation:

1. Graduate School of Engineering, Kobe University, 1-1 Rokkodai-Cho, Nada-Ku, Kobe 657-8501, Hyogo, Japan

2. Center for Interdisciplinary AI and Data Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-Ku, Tokyo 112-8610, Japan

Abstract

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as “pre-actions”), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite’s punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

Funder

JST CREST

JST SPRING

Publisher

MDPI AG

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