通过骑手佩戴的加速度计检测马的步态
Jorn Schampheleer1, Anniek Eerdekens1, Wout Joseph1
1WAVES, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Animals : an open access journal from MDPI
|April 26, 2025
概括
这项研究使用骑手佩戴的加速度计来分类马的步态,避免直接安装传感器. 一个ConvLSTM2D模型实现了89.72%的准确性来分类四个步态,优化传感器放置和数据参数.
科学领域:
- 马类科学 马类科学
- 生物机械工程 生物机械工程
- 机器学习在体育科学中的应用
背景情况:
- 直接安装传感器用于马的步态分析,会带来不适和纠等风险.
- 以骑手为中心的加速度计为马的运动分类提供了一个非侵入性的替代方案.
研究的目的:
- 为了评估传感器位置,采样频率和窗口大小对马的步态分类准确性的同时影响,使用骑手佩戴的加速度计.
- 为了确定最优的参数和模型,准确的,非侵入性的马步态分析.
主要方法:
- 收集了来自四个骑手位置 (膝盖,脊椎,胸部,手臂) 的加速度计数据,跨越五名骑手和七匹马进行三种步态 (步行,,).
- 比较了8个机器学习模型,包括一个ConvLSTM2D网络,分析不同采样频率和四秒钟窗口大小的数据.
- 使用Leave One Subject Out交叉验证 (LOSOCV) 验证了模型性能.
主要成果:
- ConvLSTM2D模型实现了最高的平均准确率89.72%,用于分类四种马的步态 (停止,步行,,).
- 在四秒间隔宽度和25 Hz采样频率下观察到最佳性能.
- 获得了86.18%的F1得分,证明了强大的分类能力.
结论:
- 骑手佩戴的加速度计提供了一种有效的,非侵入性的方法,用于马的自动步态分类.
- 具有优化的参数的ConvLSTM2D模型为实时马匹运动分析提供了高精度.
- 这种方法减轻了与直接传感器连接相关的风险,提高了马的福利和训练洞察力.
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