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相关实验视频

Updated: Sep 11, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

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Published on: January 24, 2025

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立即识别下肢连续运动的方法,基于启动窗口表面电肌图数据.

Xiaohui Li1,2,3, Hao Zhou1,3, Xueyan Lyu1,2,3

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.

Journal of neural engineering
|August 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用表面电肌图 (sEMG) 信号识别下肢运动的新方法,大大减少了延迟,提高了人机协作康复的准确性. 这种方法可以更快,更精确地控制机器人设备.

关键词:
按等级平衡的方法.即时识别即时识别即时识别下肢连续运动连续运动启动窗口中的sEMG数据.

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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:

  • 在下肢康复中的人机协作需要高精度和实时响应,以识别运动意图.
  • 表面电肌图 (sEMG) 对于精确的下肢运动识别至关重要.
  • 在连续运动中实现低延迟识别,同时保持准确性,是机器人应用的重大挑战.

研究的目的:

  • 介绍下肢连续运动的创新识别方法.
  • 研究即时识别网络 (IRN) 和持续识别 (CR) 模型,以改善人机同步.
  • 为了应对低延迟,高精度的动作识别在下肢康复机器人的挑战.

主要方法:

  • 开发了一个即时识别网络 (IRN) 和一个持续识别 (CR) 模型.
  • 将开始窗口表面电肌图 (sEMG) 数据长度优化为 210.
  • 实施了一种类别平衡的方法,以提高运动识别的准确性.
  • 在每天连续移动的七个不同场景中验证了CR模型.

主要成果:

  • IRN将时间延迟从300-350毫秒减少到60毫秒.
  • 阶级平衡方法在开始窗口内提高了4.83%的运动识别准确性.
  • 在七种情景中,CR模型实现了96.31%的平均准确性.

结论:

  • 拟议的即时识别方法显著提高了下肢连续运动识别的性能.
  • 这项研究提供了一种创新的方法,用于改善康复中的人机同步.
  • 这些发现为更有效地部署和广泛应用康复机器人铺平了道路.