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基于深度学习的框架,用于使用组合生物信号的实时上肢运动意图分类.

A Usama Syed1,2, Neelum Y Sattar2, Ismaila Ganiyu3

  • 1Department of Industrial Engineering, University of Trento, Trento, Italy.

Frontiers in neurorobotics
|August 14, 2023
PubMed
概括

这项研究引入了一种使用表面电肌图 (sEMG) 和功能近红外光谱 (fNIRS) 控制假肢手臂的新脑计算机接口. 该系统实现了94.5%的准确性解码上肢意图的跨关节截肢.

关键词:
辅助机器人技术可以帮助机器人身体残疾就是残疾.智能系统 智能系统是智能系统.机器学习是机器学习.假肢是一种假肢.在 sEMG 和 fNIRS 中.跨骨截肢是指一个截肢.

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 康复工程 康复工程

背景情况:

  • 先进的假肢需要直观的控制接口.
  • 解读人类对上肢运动的意图对于假肢功能至关重要.
  • 整合多个生物信号可以提高意图解码的准确性.

研究的目的:

  • 开发一个实时的神经机器接口来控制假肢.
  • 用组合的sEMG和fNIRS信号来解码人类对上肢运动的意图.
  • 评估框架在跨骨截肢患者中的表现.

主要方法:

  • 一个新的框架,整合了表面电肌图 (sEMG) 和功能近红外光谱 (fNIRS) 生物信号.
  • 卷积神经网络 (CNN) 用于信号训练和意图解码.
  • 来自运动皮层的fNIRS信号和双腿肌肉的sEMG同时记录了八个上肢运动.
  • 特性提取包括特定移动窗口内的峰值,最小值和平均 ΔHbO 和 ΔHbR 值 (fNIRS) 和波长,峰值和平均值 (sEMG).

主要成果:

  • 该框架成功解码了八种不同的上肢运动.
  • 在分类预期的动作方面,获得了94.5%的平均精度.
  • 从sEMG和fNIRS中选择的功能证明了对意图解码的有效性.

结论:

  • 拟议的框架显示了对假肢手臂实时控制的巨大潜力.
  • 集成sEMG和fNIRS为神经机器接口提供了一个强大的方法.
  • 这项研究证实了改善假肢手臂功能和用户控制的有希望的方法.