Your browser doesn't support javascript.
loading
Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo.
Kato, Satoshi; Yamagiwa, Shinichi.
Afiliación
  • Kato S; Doctoral Program in Computer Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan.
  • Yamagiwa S; Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan.
Sensors (Basel) ; 21(17)2021 Sep 01.
Article en En | MEDLINE | ID: mdl-34502779
Many methods such as biomechanics and coaching have been proposed to help people learn a certain movement. There have been proposals for methods to discover characteristics of movement based on information obtained from videos and sensors. Especially in sports, it is expected that these methods can provide hints to improve movement skills. However, conventional methods focus on individual movements, and do not consider cases where external factors influence the movement, such as combat sports. In this paper, we propose a novel method called the Extraction for Successful Movement method (XSM method). Applying the method, this paper focuses on throwing techniques in judo to discover key factors that induce successful throwing from the postures right before initiating the throwing techniques. We define candidate factors by observing the video scenes where the throwing techniques are successfully performed. The method demonstrates the significance of the key factors according to the predominance of factors by χ2 test and residual analysis. Applying the XSM method to the dataset obtained from the videos of the Judo World Championships, we demonstrate the validity of the method with discussing the key factors related to the successful throwing techniques.
Asunto(s)
Palabras clave

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Artes Marciales Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Artes Marciales Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Japón