基于对一致的随机森林的战略,用于sEMG手势识别系统,该系统对受污染的数据具有强大可靠性
Gabriela Winkler Favieiro1, Maurício Cagliari Tosin2, Alexandre Balbinot1
1Graduate Program of Electrical Engineering (PPGEE), Laboratory of Electro-Electronic Instrumentation (IEE), Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, 206-D, Porto Alegre, RS, Brazil.
Computers in biology and medicine
|June 21, 2025
概括
本研究介绍了用于使用表面电肌图 (sEMG) 信号进行强大的运动识别的Paraconsistent Random Forest方法. 它有效地处理噪音数据,在信号退化时优于传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 由于不受控制的采集环境,对物理信号的机器学习具有挑战性.
- 表面电肌图 (sEMG) 信号因噪音和文物而降解,使运动识别复杂化.
- 现有的模式识别算法与改变的肌电信号特征作斗争.
研究的目的:
- 介绍对一致的随机森林 (PRF) 方法,以增强基于sEMG的运动识别.
- 为了评估PRF对常见的sEMG信号污染物的强度.
- 在退化数据场景中证明PRF在传统方法上的优势.
主要方法:
- 开发了一个混合分类器,将Random Forest的噪声弹性与Paraconsistent Logic处理非理想数据的能力相结合.
- 利用实验程序,以测试该方法的性能与运动工件,热噪声和电极-皮肤接触损失.
- 统计验证了所有实验结果.
主要成果:
- 超一致的随机森林方法显示,随着数据的退化,运动预测准确度下降不到20%.
- 传统的方法在预测准确度下降了高达90%,经常变得无效.
- 在存在典型的sEMG污染物的情况下,PRF表现出显著的稳定性和可行性.
结论:
- 偏一致的随机森林方法是机器学习应用程序的有希望的方法,涉及退化的物理信号.
- 在现实世界,不受控制的条件下,PRF显著提高了从sEMG数据中识别运动的可靠性.
- 随机森林和对一致逻辑的混合增强了对模糊或矛盾数据的决策树表示能力.
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