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基于LSTM的动力脚假肢控制算法开发使用非规范化的表面EMG和特征输入.

Ahmet Doğukan Keleş1,2, Ramazan Tarık Türksoy1,3, Can A Yucesoy1

  • 1Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye.

Frontiers in neuroscience
|July 19, 2023
PubMed
概括

这项研究开发了使用非规范化表面电肌图 (sEMG) 数据为动力脚假肢的新控制算法. 这些发现使截肢者能够更实用,更经济地进行假肢控制.

关键词:
特性提取 特性提取长期短期记忆 神经网络 神经网络下肢截肢是指截肢的情况.动力脚假肢 动力脚假肢表面电肌图 (sEMG) 是指表面电肌图.

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复工程 康复工程 康复工程
  • 神经修复品是一种神经修复品.

背景情况:

  • 电动脚假肢需要先进的控制算法来实现自然行走.
  • 目前的算法在传感器集成和自主适应方面存在局限性.
  • 表面电肌图 (sEMG) 为假肢控制提供了一个有前途,经济和实用的解决方案.

研究的目的:

  • 开发和评估使用非规范化的sEMG预测斜脚位置和时刻的算法.
  • 为了确定最佳的肌肉和特征组合,以实现经济和实际的假肢控制.
  • 确定在实时假肢控制中使用非规范化的sEMG数据的可行性.

主要方法:

  • 利用长期短期记忆 (LSTM) 神经网络架构与八个下肢肌肉的EMG数据.
  • 从非规范化的sEMG振幅中提取了五个特征 (IEMG,MAV,WAMP,RMS,WL).
  • 使用皮尔森相关系数和根-平方平均误差,排列肌肉和特征组合.

主要成果:

  • 综合EMG (IEMG) 和波形长度 (WL) 的组合显示出最好的特征性能.
  • 中部胃角 (MG),大腿直骨 (RF) 和大脑中部 (VM) 显示出位置和动量的最高预测成功率.
  • 长腿 (PL) 和最大腿 (GMax) +VM分别被确定为经济和实际的变化.

结论:

  • 非规范化的sEMG数据可以有效地用于开发电动脚假肢的控制算法.
  • 该研究提供了一种系统的方法来选择用于实际和经济的假肢应用的sEMG传感器.
  • 这项研究为更自主和自然感的假肢行走铺平了道路.