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减轻电极转移对电子肌图应用中的分类性能的影响,使用滑动窗口规范化

Taichi Tanaka1, Isao Nambu2, Yasuhiro Wada2

  • 1Department of Science Technology of Innovation, Nagaoka University of Technology, Nagaoka 940-2188, Japan.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括
此摘要是机器生成的。

一种新的滑窗正常化 (SWN) 技术通过在通道上对齐振幅来提高电肌图 (EMG) 信号预测的准确性. 这种方法可以减轻电极转移造成的性能损失,而不需要额外的数据或再培训.

关键词:
DNN 分类 DNN 分类在EMGEMGEMGEMGEMGEMGEMGEMGEMGEM电极移位的电极移位是指电极移位的电极移位.电动肌谱学 电动肌谱学信号正常化信号正常化这是一个Z-score.

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科学领域:

  • 生物医学工程 生物医学工程
  • 信号处理 信号处理
  • 康复技术 康复技术 康复技术

背景情况:

  • 电肌图 (EMG) 信号对于假肢,辅助设备和康复非常重要.
  • 性能限制包括跨主题概括问题,电极移位和每日信号变化.
  • 现有的解决方案,如转移学习,需要额外的数据收集和再培训.

研究的目的:

  • 研究一种用于实时EMG预测的新型滑动窗正常化 (SWN) 技术.
  • 为了解决由电极移位和每日变化引起的性能下降.
  • 为了提高基于EMG的应用稳定性,而无需进行广泛的再培训.

主要方法:

  • 开发并验证了一个滑窗正常化 (SWN) 技术.
  • SWN将z-score规范化与滑动窗口处理合并在一起.
  • 来自右臂轨迹跟踪任务 (三种运动类别) 的实验数据用于验证.

主要成果:

  • 在没有再培训的情况下,SWN减轻了精度降低到-1.0%,比基线改善6.6%.
  • 该技术在通道中对齐EMG振幅,减少对电极位移的灵敏度.
  • 将SWN与多姿势训练相结合,超过了无转变条件的准确性2.4%.

结论:

  • SWN有效地减轻了由于电极移位和变化而导致的EMG性能下降.
  • 该方法可以通过单个电极位置的数据进行训练,简化实际应用.
  • SWN为基于EMG的实时人机接口提供了强大而高效的解决方案.