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相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
04:49

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Published on: September 6, 2024

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基于ML的个性化可穿戴机器人控制可以改善受损的手臂功能.

James Arnold1, Prabhat Pathak1, Yichu Jin1

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Nature communications
|August 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了可穿戴机器人的个性化控制系统,可以准确地检测用户的意图,并改善残疾人的上肢运动. 这种新的方法增强了手臂功能,可移动性,并减少了补偿运动.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术

背景情况:

  • 可穿戴机器人提供了上肢辅助的潜力,但由于运动变化和支持透明度的权衡,在个性化控制方面面临挑战.
  • 有效的控制对于残疾人辅助机器人的现实应用至关重要.

研究的目的:

  • 为软肩机器人开发和评估一种新的实时控制器,该控制器集成了个性化的机器学习意图检测和基于物理的歇斯底里模型.
  • 通过使用自适应式机器人辅助系统,提高中风后和肌缩侧面硬化症 (ALS) 患者的上肢功能和运动质量.

主要方法:

  • 开发了一个个性化的机器学习 (ML) 意图检测模型,使用IMU和压缩传感器来解码用户的运动意图.
  • 实现基于物理的hysteresis模型,以提高控制透明度,适应实际现实世界的使用.
  • 在实时控制器中集成ML意图检测和歇斯底里模型,以根据用户意图和动力状态调节辅助.
  • 使用软肩机器人对9名参与者进行了为期几天的评估 (5人中风后,4人患有ALS).

主要成果:

  • 控制器在从最小的角度变化中识别肩膀运动时达到94.2%的准确性.
  • 与基线控制器相比,手臂降低力减少了31.9%.
  • 在运动质量上观察到显著的改善,包括增加肩膀 (17.5°),肘部 (10.6°) 和手腕 (7.6°) 运动范围 (ROM).
  • 干补偿减少了高达25.4%,手行道效率提高了高达53.8%.

结论:

  • 综合控制策略有效地提高了中风后和ALS患者的上肢功能.
  • 个性化意图检测和自适应控制是提高可穿戴辅助机器人的性能和可用性的关键.
  • 这种方法证明了机器人协助上肢残疾人的有希望的进步,导致更自然和更有效的运动.