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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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基于sEMG和CNN-TL融合模型的下肢运动识别.

Zhiwei Zhou1, Qing Tao1, Na Su1,2

  • 1College of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括

结合卷积神经网络,变压器编码器和LSTM的新CNN-TL模型显著改善了基于表面电肌图 (sEMG) 的下肢运动分类准确性,用于康复设备.

关键词:
卷积神经网络是一种卷积神经网络.长期短期记忆 长期短期记忆下肢动作识别功能 下肢动作识别功能表面电动图信号的信号.变压器编码器编码器

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 康复技术 康复技术 康复技术

背景情况:

  • 对下肢运动的准确分类对于开发有效的康复和辅助设备至关重要.
  • 使用表面电肌图 (sEMG) 的现有方法在分类准确性上有局限性.

研究的目的:

  • 提出和评估一种新的融合识别模型,CNN-Transformer-LSTM (CNN-TL),用于使用sEMG数据增强下肢运动分类.

主要方法:

  • 收集了来自20名受试者的sEMG数据,他们进行了四种不同的下肢运动:上楼,下楼,在平面上行走,.
  • 预处理的sEMG数据和提取的时间和频率域特征.
  • 开发并将CNN-TL模型与CNN-LSTM,CNN和支持矢量机 (SVM) 模型进行比较.

主要成果:

  • 与其他模型相比,CNN-TL模型实现了更高的分类准确性.
  • 具体来说,CNN-TL的表现超过了CNN-LSTM的3.76%,CNN的5.92%和SVM的14.92%.

结论:

  • 拟议的CNN-TL模型在基于sEMG信号的下肢运动分类方面表现出卓越的性能.
  • 这种融合模型提供了一种有效的方法,用于在康复和辅助技术中推进运动功能.