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IRDC-Net:一个具有残余模块和扩展卷积的初始网络,用于基于表面肌电图的手语识别.

Xiangrui Wang1, Lu Tang1, Qibin Zheng1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

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概括
此摘要是机器生成的。

这项研究引入了一种新的Inception架构,该架构具有残余模块和扩展卷积 (IRDC-net),用于使用表面电肌图 (sEMG) 信号进行手语识别 (SLR). IRDC网络显著提高了聋人通信辅助器的分类准确性.

关键词:
扩张的卷积扩张的卷积.开始网络的开始网络.剩余模块的残留模块可以使用.标志性语言识别 标志性语言识别表面电心图 (电心图) 是一种表面电心图.

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 聋人和听障人士面临的沟通挑战.
  • 基于表面电肌图 (sEMG) 的手语识别 (SLR) 为社会融合提供了一个有前途的解决方案.
  • 传统的卷积神经网络 (CNN) 结构在捕获复杂的sEMG信号特征方面存在局限性.

研究的目的:

  • 为增强的SLR提出一个新的IRDC-net架构.
  • 提高识别中国手语标志的准确性和效率.
  • 在公共数据集上验证拟议的方法,并将其与现有的CNN模型进行比较.

主要方法:

  • 用离散里埃转换将时间域sEMG信号转换为时间频域.
  • 开发和应用一个新的Inception架构与残余模块和扩展卷积 (IRDC-net) 的SLR.
  • 使用Ninapro DB1数据集对IRDC-net与VGG-net和ResNet-18进行比较分析.

主要成果:

  • 在时间频率转换后,IRDC网络在中国手语定制数据集上实现了91.70%的分类准确率.
  • 在公开的Ninapro DB1数据集上,IRDC网络在时间频率数据上达到了89.82%的分类准确度.
  • 拟议的IRDC网络在SLR任务中表现优于VGG网络和ResNet-18.

结论:

  • IRDC网络有效地捕获sEMG信号的复杂特征,以改进SLR.
  • 时频域转换提高了基于sEMG的单反相机系统的性能.
  • 这项研究有助于推进SLR技术,并帮助聋和听障人士.