学术动态:Development of Shinai-Embedded IMU-Based Sensing System for Motion Analysis of Kendo Swings-星律科技

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学术动态:Development of Shinai-Embedded IMU-Based Sensing System for Motion Analysis of Kendo Swings

2026-05-26 18:00:27

论文标题:Development of Shinai-Embedded IMU-Based Sensing System for Motion Analysis of Kendo Swings

发布日期:2026-05-26

作者:Yuta Ogai, Masaomi Sanekata

DOI:10.3390/s26113356

论文摘要:In recent years, wearable sensing technologies have been widely used for motion analysis in sports; however, in kendo, motion evaluation still largely relies on subjective assessment, and quantitative approaches remain limited. This study proposes an embedded inertial measurement unit (IMU)-based sensing system integrated into a bamboo sword (shinai) for the motion analysis of kendo swings. The system incorporates a compact IMU and a microcontroller within the shinai, enabling unobtrusive measurement under realistic training conditions without affecting usability. Using the acquired sensor data, motion-related acceleration components were extracted with orientation estimation using the error-state Kalman filter (ESKF) based on six-axis IMU data, followed by gravity compensation and feature extraction based on the peak characteristics of the swing motion. The experimental results show that experienced practitioners exhibited significantly higher peak acceleration (p = 0.002) and smaller peak width (p = 0.022) than novice practitioners, indicating sharper and more efficient motion. No significant differences were observed in the secondary peak ratio. These results demonstrate that the proposed system can quantitatively capture the motion characteristics of kendo swings and distinguish practitioners of different proficiency levels, which highlights its potential for objective motion analysis and training support in kendo.

元数据:Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113356. Vol. 26, Issue 11. Authors: Yuta Ogai, Masaomi Sanekata.

开放许可:https://creativecommons.org/licenses/by/4.0/

原文链接:https://doi.org/10.3390/s26113356

PDF 链接:https://www.mdpi.com/1424-8220/26/11/3356/pdf


来源:MDPI Sensors via Crossref

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