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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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手术仪器信号手势识别使用表面肌电图信号

Melissa La Banca Freitas1, José Jair Alves Mendes2, Thiago Simões Dias2

  • 1Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology-Paraná (UTFPR), Ponta Grossa 84017-220, PR, Brazil.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种可穿戴系统,用于使用表面电肌图 (sEMG) 信号进行外科仪器信号 (SIS). 该系统在识别14种不同的手势方面实现了88%的准确性,增强了外科沟通.

关键词:
自动细分自动细分自动细分整体分类 整体分类 整体分类模式识别 模式识别 模式识别机器人手术是机器人手术的一种方式.信号处理是指信号处理的过程.表面电力学图 (surface electromyography) 是一种表面电力学图.远程外科手术是一种远程外科手术.

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科学领域:

  • 生物医学工程 生物医学工程
  • 人与计算机的交互
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 手术仪器信号 (SIS) 依赖于手势来进行外科医生-仪器仪器通信,这对于防止错误至关重要.
  • 现有的SIS系统经常使用有限的手势,可能会阻碍细微的沟通.
  • 可穿戴技术为实时,非侵入性手术通信系统提供了一个有前途的途径.

研究的目的:

  • 评估SIS使用表面电肌图 (sEMG) 信号的手势识别系统的可行性.
  • 开发和评估可穿戴SIS应用程序的处理程序,特别是用于远程手术和机器人手术.
  • 创建一个包含10名志愿者的14个SIS手势的综合数据库,以进行强大的系统培训和测试.

主要方法:

  • 在10名志愿者执行14个不同的SIS手势时,从Myo腕带获取sEMG信号.
  • 实现一个涉及自动细分,特征提取 (13组) 和特征选择的处理程序.
  • 使用6种不同的分类器,包括支持矢量机 (SVM) 和2种组合技术,对sEMG信号进行分类.

主要成果:

  • 在所有记录的数据库中使用SVM分类器实现了76%的准确性.
  • 单个志愿者分析的SVM分类器的准确度更高,达到88%.
  • 开发的系统证明了其适用于可穿戴应用程序中的实时SIS手势识别.

结论:

  • 表面电肌图 (sEMG) 信号对于识别多种不同的手术仪器信号 (SIS) 手势是有效的.
  • 拟议的处理程序和分类方法适用于可穿戴的SIS应用程序,增强外科通信.
  • 这项研究为将先进的手势识别集成到远程手术和机器人手术系统中提供了基础.