开源,开放科学:开发OpenLESS作为自动化着陆错误评分系统
Jeffrey A Turner1,2, Elaine T Reiche1,2, Matthew T Hartshorne1,2,3
11Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, NC, USA.
Journal of athletic training
|April 16, 2025
概括
一种用于跳跃着陆分析的自动化工具OpenLESS在健康和ACLR后种群中显示出良好的有效性. 这种无标记运动捕捉系统为临床和运动环境提供高效和可靠的下肢运动评估.
科学领域:
- 生物力学 生物力学
- 运动分析 运动分析
- 运动科学 运动科学 运动科学
背景情况:
- 对运动质量的临床评估是耗时的.
- 需要自动化工具来提高监测患者结果的效率.
- 登陆错误评分系统 (LESS) 需要客观和快速的评分方法.
研究的目的:
- 开发和验证开放着陆错误评分系统 (OpenLESS),这是一个用于跳跃着陆分析的自动化工具.
- 在健康和临床人群中评估OpenLESS与专家LESS得分的有效性.
- 在运动员中评估OpenLESS的插座可靠性.
主要方法:
- OpenLESS使用无标记运动捕捉和3D动力学来解释运动质量.
- 通过将OpenLESS分数与健康和ACLR后队列中的专家LESS分数进行比较来评估有效性.
- 在运动员队列中,使用类内相关系数 (ICC),SEM和MDC来确定可靠性.
主要成果:
- 在健康 (ICC2,k=0.79) 和ACLR后 (ICC2,k=0.88) 队列中,OpenLESS与专家的LESS得分达成良好一致.
- 自动评分显著减少了分析时间,在25分钟内处理353个试验,而手动评分需要35个小时.
- 在实地测试中观察到卓越的交会可靠性 (ICC2,k>0.89),SEM的误差为0.98和MDC的误差为2.72.
结论:
- OpenLESS是自动跳着降落运动质量评估的有效和高效工具.
- 该系统在健康人群和ACLR后人群中显示出良好的有效性.
- OpenLESS为基于现场的评估提供了卓越的可靠性,解决了客观运动分析的需求.
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