Neural Networks Classification for Training of Five German Longsword Mastercuts - A Novel Application of Motion Capture: Analysis of Performance of Sword Fencing in the Historical European Martial Arts (HEMA) Domain

R. Klempous, Konrad Kluwak, Ito Atsushi, Tomasz Górski, J. Nikodem, Wojciech Bożejko, Z. Chaczko, Grzegorz Borowik, J. Rozenblit, Marek Kulbacki
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Abstract

This paper discusses an application of motion capture in longsword fencing, a discipline experiencing rising popularity since the 1990s. Historical European Martial Arts alliance focuses on re-enacting the Late Middle Ages and Renaissance fighting styles. To popularize this art, novel research to automatically distinguish selected sword cutting techniques has been conducted. The fencing knowledge required for conducting this research was based on publications and consultation with experts in the field, and recordings. For this research, different movements from Masterstrikes such as Zornhau (Strike of Wrath), Schielhau (Squinting Strike), Zwerchhau (Cross Strike), Krumphau (Crooked Strike), Scheitelhau (Crown Strike) were selected. Motions performed by an adept fencer (acting expert) were used as patterns of correct strikes and compared with the movements of fencing amateurs. The main goal of this research was to measure the precision of movement while performing five different fencing strokes. Each movement was recorded with 39 unique full-body plug-in gait configurations initially designed for medical applications. During the exercise, 16 EMG electrodes configuration was used for the measurement of muscle activity.
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五种德国长剑主刀训练的神经网络分类——动作捕捉的一种新应用:历史欧洲武术(HEMA)领域剑击的表现分析
本文讨论了动作捕捉技术在20世纪90年代兴起的长剑击剑运动中的应用。历史上的欧洲武术联盟专注于重新制定中世纪晚期和文艺复兴时期的战斗风格。为了普及这门艺术,人们进行了一项新的研究,以自动识别选定的剑切技术。开展这项研究所需的击剑知识是基于出版物和咨询该领域的专家以及录音。在这项研究中,选择了来自大师打击的不同动作,如愤怒之击、斜视打击、交叉打击、弯曲打击、皇冠打击等。用熟练击剑运动员(表演专家)的动作作为正确击球的模式,并与业余击剑运动员的动作进行比较。这项研究的主要目的是测量在进行五种不同击剑动作时的运动精度。每个动作都记录了39种独特的全身插入式步态配置,最初是为医疗应用而设计的。在运动过程中,16个肌电图电极配置用于测量肌肉活动。
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