使用压力内的监督机器学习和深度学习方法之间的地面反应力组件估计精度的比较
Amal Kammoun1,2, Philippe Ravier1, Olivier Buttelli1,3
1PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, France.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
监督机器学习 (SML) 方法,特别是随机森林 (RF),使用内传感器准确估计地面反应力 (GRF) 组件,在静态活动中优于深度学习 (DL) 方法.
科学领域:
- 生物力学 生物力学
- 传感器技术 传感器技术
- 机器学习 机器学习
背景情况:
- 估计地面反应力 (GRF) 组件对于生物力学分析至关重要.
- 压力内底传感器为GRF估计提供了一种便携式方法.
- 对比各种机器学习算法用于GRF估计对于实际应用至关重要.
研究的目的:
- 估计GRF组件 (Fx,Fy,Fz) 使用压力内底传感器在六个活动中,包括新的静态和手动物料处理场景.
- 为了比较六种不同的方法的准确性,三种深度学习 (DL) 和三种监督机器学习 (SML) 用于GRF组件估计.
- 确定不同活动中GRF估计的最准确方法.
主要方法:
- 评估了六种方法:人工神经网络,长期短期记忆,卷积神经网络 (DL) 和最小平方,支持向量回归,随机森林 (SML).
- 从9名从事6种不同的活动的受试者收集了数据.
- 根平均平方误差 (RMSE) 用于量化对力板数据的估计准确性.
主要成果:
- 随机森林 (RF) 方法在估计静态活动的GRF组件方面表现出最高的准确性.
- 对于静态情况,RF实现的RMSE平均值明显低于参考测量值.
- 监督机器学习方法,特别是射频,在GRF估计准确度方面超过了经过测试的深度学习方法.
结论:
- 压力内底传感器与监督机器学习相结合,特别是随机森林,提供准确的GRF组件估计.
- 该研究将GRF估计扩展到新的活动中,为生物力学和人体工程学提供了宝贵的见解.
- 在静态和潜在的其他活动中,RF为GRF估计提供了Deep Learning方法的优越替代方案.
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