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Ankle Joint01:10

Ankle Joint

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The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
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相关实验视频

Updated: Jul 15, 2025

Experimental Methods to Study Human Postural Control
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Experimental Methods to Study Human Postural Control

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使用深度学习模型来预测假肢脚扭矩.

Christopher Prasanna1,2, Jonathan Realmuto3, Anthony Anderson1,2

  • 1Center for Limb Loss and Mobility, Seattle, WA 98108, USA.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

深度学习模型使用最小的传感器数据准确地预测假肢脚扭矩. 这一进步使得动力假肢的响应性和预测性控制更强,改善了用户体验.

关键词:
生物力学 生物力学深度神经网络是一个神经网络.机器学习是机器学习.机器人脚假肢 机器人脚假肢

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相关实验视频

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科学领域:

  • 生物力学 生物力学
  • 机器学习 机器学习
  • 假肢是一种假肢.

背景情况:

  • 通过运动捕捉进行反向动力学是生物力学数据的标准,但是实验室和标记器密集的.
  • 需要一个实用的替代实时假肢控制系统.
  • 预测控制器需要快速,准确的生物机械信息.

研究的目的:

  • 开发深度学习模型来估计和预测假肢脚扭矩.
  • 用最小的输入信号来准确的动态系统建模.
  • 在高带宽假肢系统中实现预测控制.

主要方法:

  • 应用深度学习来创建动态系统模型.
  • 使用超参数优化协议进行自动模型选择.
  • 在六个输入信号上训练深度神经网络,以预测脚扭矩.

主要成果:

  • 深度神经网络预测了未来的脚扭矩,准确度很高 (2.9 ± 1.6% RMSE).
  • 与分析模型 (26.6 ± 40.9% RMSE) 相比,深度学习显示出更高的性能.
  • 未来的预测 (半步循环) 显示性能降低最小 (1.7%的RMSE增加).

结论:

  • 深度学习为准确的假肢脚扭矩预测提供了一种可行的方法.
  • 这种方法减少了对复杂的移动捕捉设置的依赖.
  • 允许开发动力假肢的先进预测控制.