PCA预规范化方法对地面反应力估计精度的影响.
Amal Kammoun1,2, Philippe Ravier1, Olivier Buttelli1,3
1PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, France.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
选择正确的预规范化方法对于使用机器学习 (PCA-ML) 主成分分析准确估计地面反应力 (GRF) 是至关重要的. 最好的方法完全取决于所采用的特定机器学习技术.
科学领域:
- 生物力学 生物力学
- 运动科学 运动科学 运动科学
- 生物医学工程 生物医学工程
背景情况:
- 鞋底压力传感器可以估计地面反应力 (GRF) 组件.
- 主要组件分析与机器学习 (PCA-ML) 结合在一起是此估计的常见方法.
- PCA需要预规范化,其对GRF估计准确性的影响尚未完全理解.
研究的目的:
- 评估12种预规范化方法对GRF组件估计准确性的影响.
- 为了比较三个PCA-ML方法 (PCA-ANN,PCA-LS,PCA-SVR) 与不同规范化的性能.
- 在GRF估计中确定特定PCA-ML技术的最佳规范化策略.
主要方法:
- 评估了12种用于内底压力传感器数据的预规范化方法.
- 使用了三种PCA-ML方法:人工神经网络 (ANN),最小平方 (LS) 和支持向量回归 (SVR).
- 评估准确性与九个受试者在行走时的金标准力板测量.
主要成果:
- 在不同的ML技术中,规范化方法的性能差异很大.
- 体重正常化对于PCA-ANN来说是最佳的,但对于PCA-SVR来说是最差的.
- 建议对PCA-ANN和PCA-LS进行矢量标准化,而对于PCA-SVR则建议使用平均方法.
结论:
- 预规范化方法的选择严重取决于所选择的机器学习算法.
- 选择独立于ML技术的规范化方法可能会导致低于最佳或不准确的GRF估计.
- 为PCA-ANN,PCA-LS和PCA-SVR提供了具体的规范化建议,以提高GRF估计的准确性.
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