基于高效相似性评估的动态高尔夫球摆分析框架
Seung-Su Lee1, Jun-Hyuk Choi1, Jeongeun Byun2
1Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一个新的动态运动相似度测量 (DMSM) 框架通过评估七个阶段的联合轨迹来增强高尔夫球摆分析,比传统方法提高准确性和生物力学洞察力.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 计算机视觉 计算机视觉
背景情况:
- 基于图像的姿势估计提供了具有成本效益的运动分析.
- 传统的方法与高尔夫球摆的时间对齐和感知准确性作斗争.
- 在高尔夫球摇摆运动捕捉中需要动态的,相位特定的分析.
研究的目的:
- 引入一个动态运动相似度测量 (DMSM) 框架用于高尔夫球摇摆分析.
- 提高运动捕获数据的感知一致性和生物机械解释性.
- 开发基于AI的高尔夫球摇摆分析和实时反的实用基础.
主要方法:
- 将高尔夫球摆分为七个规范阶段.
- 评估每个阶段内共同关键点的动态轨迹.
- 集成连续运动,规范化坐标,插曲数据,并使用数值集成用于路径差异.
主要成果:
- DMSM显示相同和不同玩家摇摆对之间的歧视更强烈.
- 在脊柱角度轨迹 (Δ = 38.68,p < 0.05) 中取得了显著的生物力学区别.
- 教练的评估证实了与新框架的感知对齐.
结论:
- DMSM提供了更可靠的相位反和生物机械可解释的运动分析.
- 该框架在计算上是可行的,只需轻微的开销 (≈169 ms).
- 提供了人工智能驱动的体育训练和实时反系统的实际基础.
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