Kim Kwangyun, Tsuchida Shuhei, Terada Tsutomu, Tsukamoto Masahiko
Graduate School of Engineering, Kobe University, 1-1 Rokkodai-Cho, Nada-Ku, Kobe 657-8501, Hyogo, Japan.
Center for Interdisciplinary AI and Data Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-Ku, Tokyo 112-8610, Japan.
Sensors (Basel). 2025 Jul 2;25(13):4134. doi: 10.3390/s25134134.
Kumite is a karate sparring competition in which two players fight each other using various techniques. In kumite matches, it is essential to reduce a preliminary action (hereinafter referred to as "pre-action"), such as pulling the arms and lowering the shoulders just before performing an attack technique. This is because pre-actions reveal the timing of the attack to the opponent. However, players often find it difficult to recognize their own pre-actions, and accurately estimating their presence or absence is challenging with conventional motion analysis methods, as pre-actions are subtle compared to major techniques like punching or kicking. Previously, we proposed a method for detecting pre-actions during single punches performed in a static state using inertial sensors. While this method was effective in controlled situations, it failed to detect pre-actions in punches during actual kumite matches. The main reason is that players generally perform footwork during matches, and this footwork is often misrecognized as pre-action via conventional detection methods. To address misrecognition caused by footwork, we propose a new method that combines preprocessing designed to detect and smooth footwork segments in the inertial data with the conventional pre-action detection method, thereby enabling pre-action detection during kumite matches. In the preprocessing, we apply an autocorrelation function to assess the constancy of footwork and accurately separate the footwork segment from the kumite technique segment. Only the footwork segment is then smoothed to suppress its influence on the detection process. Our experimental results show that the proposed method can estimate the presence or absence of pre-action in the punch of an actual kumite match with an accuracy of 0.875.
组手是一种空手道对打比赛,两名选手运用各种技巧相互对抗。在组手比赛中,减少诸如在执行攻击技术前伸展手臂和压低肩膀等预备动作(以下简称“预动作”)至关重要。这是因为预动作会向对手暴露攻击时机。然而,选手们往往难以识别自己的预动作,而且使用传统的动作分析方法准确估计其有无具有挑战性,因为与诸如 punching 或 kicking 等主要技术相比,预动作很细微。此前,我们提出了一种使用惯性传感器检测静态单拳动作中预动作的方法。虽然该方法在受控情况下有效,但未能检测到实际组手比赛中拳法的预动作。主要原因是选手在比赛中通常会进行脚步移动,而这种脚步移动通过传统检测方法常常被误识别为预动作。为了解决由脚步移动导致的误识别问题,我们提出一种新方法,该方法将旨在检测和平滑惯性数据中脚步移动片段的预处理与传统的预动作检测方法相结合,从而能够在组手比赛中检测预动作。在预处理中,我们应用自相关函数来评估脚步移动的稳定性,并准确地将脚步移动片段与组手技术片段分离。然后仅对脚步移动片段进行平滑处理,以抑制其对检测过程的影响。我们的实验结果表明,所提出的方法能够以 0.875 的准确率估计实际组手比赛中拳法预动作的有无。