基于加速度计传感器的技的识别
Zeting Liu1, Mengyuan Yang1, Kaihang Li1
1School of Sports Engineering, Beijing Sport University, China.
Heliyon
|July 8, 2024
概括
这项研究开发了一种机器学习方法,用于识别使用加速度计的泰拳道 (TKD) . 带有腰部穿戴传感器的支持矢量机 (SVM) 模型实现了高精度,使其成为 TKD 技术的最佳分类.
科学领域:
- 运动科学 运动科学 运动科学
- 生物机械工程 生物机械工程
- 机器学习 机器学习
背景情况:
- 太拳道 (TKD) 是一个全球流行的运动和体育活动.
- 在自由生活环境中测量TKD是具有挑战性的,与实验室研究不同.
- 使用加速度计的机器学习已经应用于其他武术,但不太适用于TKD.
研究的目的:
- 开发和评估一种方法来识别不同泰拳道技巧.
- 评估放置在腰部和脚上的加速度计的有效性,以进行击分类.
- 为了比较TKD击识别的机器学习算法 (SVM和决策树).
主要方法:
- 20名参与者进行了四种类型的TKD (前方,圆房,侧面,后方) 多次.
- 三个加速度计被戴在腰部,右脚和左脚上.
- 使用支持矢量机 (SVM) 和决策树算法来分类动作.
主要成果:
- 在SVM模型中,使用腰部和两条脚上的加速度计,腰部和右脚上的加速度计,或仅使用腰部的加速度计,SVM模型实现了高分类精度 (≥0.96).
- 决策树模型达到0.8.8左右的准确度.
- 在SVM模型中,仅使用腰部穿戴的加速度计数据实现了最佳性能.
结论:
- 支持矢量机 (SVM) 模型在识别 TaeKwonDo 中表现出高精度.
- 仅在腰部使用加速度计提供了高精度和最小传感器要求的最佳平衡.
- 这种方法为在现实环境中分析TKD技术提供了实用方法.
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