从使用自动编码器和扩展器网络的步骤平均数据中估计步骤内代谢成本
Manal Mustafa1, Alex C Dzewaltowski2,3, Philippe Malcolm2
1Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
Frontiers in bioengineering and biotechnology
|July 7, 2025
概括
研究人员开发了新的神经网络方法来估计步行.
科学领域:
- 生物力学 生物力学
- 计算建模计算建模
- 机器学习是机器学习.
背景情况:
- 老龄化会使行走的氧气消耗增加30%以上,损害运动能力,导致身体退化.
- 在步行过程中代谢成本增加的根本原因尚未完全理解.
- 目前的运动捕捉系统无法直接测量步行周期内的代谢成本波动.
研究的目的:
- 使用神经网络方法从平均值计算代谢成本时间序列.
- 调查自动编码器 (AEs) 和扩展器对此任务的有效性.
- 为了应对表征阶段内代谢成本波动的挑战.
主要方法:
- 开发了两个神经网络模型,即自编码器 (AEs) 和扩展器,用于将平均代谢成本映射到时间序列.
- 网络使用计算机行走模型进行训练,模拟35次机器人干扰下的步态.
- 模型验证是使用未被扰乱的步态周期的估计代谢成本进行的.
主要成果:
- 没有绑定权重的扩展器和AE在非线性激活函数中表现最好.
- 有关权重的AE在线激活函数中表现最好.
- 扩展器在估计代谢成本时间序列方面出乎意料地表现优于AE.
结论:
- 神经网络方法,特别是扩展器,可以有效地从平均值计算代谢成本时间序列.
- 这种方法为估计阶段内代谢成本波动提供了潜在的解决方案.
- 未来的工作重点是克服对初始培训的时间序列的依赖.
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