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L-SHADE优化了sEMG手势识别学习框架.

Naveen Gehlot1,2, Ankit Vijayvargiya3, Ashutosh Jena4

  • 1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. naveen.gehlot@manipal.edu.

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概括

这项研究优化了手势识别 (HGR) 使用额外树 (ET) 分类器与线性人口大小减少成功-历史适应差异进化 (L-SHADE). 优化了L-SHADE的ET框架显著提高了准确性,并减少了实时手势识别的计算时间.

关键词:
电肌图学信号 电肌图学信号手的手势识别手势识别人机交互的人机交互这是一个超参数.机器学习模型的机器学习模型优化技术的优化技术

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科学领域:

  • * 人与计算机的交互
  • * 机器学习 * 机器学习
  • * * 信号处理 信号处理

背景情况:

  • *实时手势识别 (HGR) 依赖于机器学习分类器 (MLC).
  • *MLC的性能高度依赖于与实时数据的超参数调整.
  • * 现有的方法需要优化,以提高准确性和效率.

研究的目的:

  • *为HGR开发一个优化的额外树 (ET) MLC框架.
  • * 为了提高实时手势识别的准确性和减少计算负载.
  • * 为了评估线性人口大小减小成功史适应差异演变 (L-SHADE) 对超参数优化的有效性.

主要方法:

  • *利用来自两个前臂肌肉的实时表面电肌图 (sEMG) 信号.
  • *为了分类,捕获了六种不同的手势动作.
  • *采用十个MLC,包括一个额外树 (ET) 分类器,并使用L-SHADE优化它.

主要成果:

  • * L-SHADE优化的ET框架在获取数据上实现了87.89%的平均准确率,比84.14%的平均准确率有所改善.
  • *平均计算时间从8.62毫秒减少到3.16毫秒.
  • * 在公开的15个手势数据集上显示了>3.0%的平均准确度改善.

结论:

  • * L-SHADE优化显著提高了HGR的ET分类器的性能.
  • * 拟议的框架为实时手势识别提供了强大而高效的解决方案.
  • *这种方法为人机交互和可穿戴技术领域做出了宝贵的贡献.