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使用深度学习对机器人假肢的模式统一意图估计.

Hanjun Kim1, Dawit Lee2, Jairo Y Maldonado-Contreras1,3

  • 1Hanjun Kim is with the Woodruff School of Mechanical Engineering, Georgia Tech, Atlanta, GA 30332-0405 USA.

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概括

这项研究引入了一种统一的方法,使机器人假肢能够通过不断估计地形斜率来识别用户的意图,从而提高了对转肢截肢者的传统离散模式分类器的准确性.

关键词:
假肢和外骨的使用深度学习是一种深度学习.意图识别识别的意图识别模式统一模式的统一斜坡估计 斜坡估计

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 生物力学 生物力学
  • 假肢手术 假肢手术是一门专业.

背景情况:

  • 传统的机器人假肢使用离散模式 (水平,坡道,楼梯) 进行行走,这对于连续的人类运动是不够的.
  • 现有的模式分类器因地形变化的持续性而难以准确地识别意图.

研究的目的:

  • 开发和验证一个模式统一的意图识别策略,用于转肢截肢.
  • 为了实现连续的斜率估计,以便在不同地形上更准确地控制假肢.

主要方法:

  • 使用深度卷积网络训练的运动数据从16个个体的腿骨转移截肢.
  • 开发了一种模式统一的斜率估计器,并将其性能与传统模式分类器进行比较,使用离开一个主体的验证.
  • 评估了系统复制健身膝关节运动的能力.

主要成果:

  • 模式统一的斜率估计器实现了较低的平均绝对误差 (MAE) 1.68 ± 0.60度,而模式分类器的1.94 ± 0.97度 (p<0.05).
  • 拟议的系统显著改善了膝盖动力学复制,膝盖清除的MAE为5.13 ± 2.00度,而爬楼梯时膝盖接触角度为6.74 ± 2.97度.
  • 这些结果明显优于传统分类器的MAE12.10±5.20度和13.80±3.28度 (p<0.01).

结论:

  • 一种模式统一的方法使机器人假肢能够在不依赖离散模式分类的情况下持续调整地形.
  • 这一策略增强了假肢的控制,并改善了转截肢患者的自然步行.
  • 这些发现表明,下肢假肢的控制系统更直观,更适应.