通过生物关节时刻估计进行任务无关的外骨控制
Dean D Molinaro1,2,3, Keaton L Scherpereel4,5,6, Ethan B Schonhaut4
1George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. molinarodean@gmail.com.
Nature
|November 13, 2024
概括
这项研究引入了一种用于下肢外骨架的新任务无关控制器,使用深度神经网络来估计关节时刻. 这使得人类的各种活动能够得到协调的帮助,从而提高了外骨的生存能力.
科学领域:
- 机器人技术
- 生物力学
- 人工智能
背景情况:
- 目前的下肢外骨控制器难以处理各种人体运动, 限制了它们的实际应用.
- 现有的系统往往需要特定任务的调整,这阻碍了活动之间的无过渡.
研究的目的:
- 开发和评估用于下肢外骨架的无任务控制器,以帮助用户进行广泛的活动.
- 通过使用深度神经网络来估计生物关节时刻, 实现自主多关节辅助.
主要方法:
- 一个深度神经网络被训练在实时估计关节和膝关节的时刻.
- 控制器被集成到服装集成的外骨架中,并在28个不同的活动中进行了测试.
- 通过将估计的时刻与地面真相进行比较和评估用户的能量来评估性能.
主要成果:
- 控制器准确地估计了28项活动的部和膝盖时刻 (平均R2为0.83),从循环运动到非结构化任务.
- 基于任务的控制器显著优于基于任务分类器的方法.
- 在没有控制器重新校准的情况下,使用者的能量减少了5. 319. 7%.
结论:
- 使用基于深度神经网络的时刻估计的任务无关控制器可以有效地协调下肢外骨架的辅助.
- 这种方法显著提高了外骨在广泛的人类行为中的适应性和效率.
- 开发的控制器是对辅助外骨的现实可行性的关键一步.
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