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强大的肌电模式识别方法,以减少用户的校准负担:挑战和未来的挑战.

Xiang Wang1, Di Ao1, Le Li1,2

  • 1Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China.

Frontiers in bioengineering and biotechnology
|February 6, 2024
PubMed
概括
此摘要是机器生成的。

由于信号变化,用于假肢控制的肌电模式识别 (MPR) 面临着挑战. 本综述探讨了强大的MPR算法,以减少用户校准负担并提高设备的可用性.

关键词:
在高清-sEMGG.这是一个跨场景的场景.这是一个跨主题的跨主题.电极移位的电极移位是指电极移位的电极移位.电动肌图 (EMG) 是一种电动肌图.肌电模式识别 (MPR) 是一种强大的肌电控制.

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复工程 康复工程
  • 人与计算机的交互

背景情况:

  • 肌电模式识别 (MPR) 对于控制肌电接口 (MI) 设备,包括假肢和整形机器人至关重要.
  • 目前的MI提供先进的四肢控制,在消费电子产品中具有潜力.
  • 非静止的肌电信号会因电极移位和新用户等因素而导致性能降低,需要经常校准.

研究的目的:

  • 为了解决与传统肌电接口相关的大量用户校准负担.
  • 将导致校准负担的场景分类,重点关注数据分布转移和动态数据类别.
  • 审查和总结旨在减轻用户校准要求的强大的MPR算法.

主要方法:

  • 基于数据分布转移和动态数据类别的校准负载场景的分类.
  • 调查和总结了流行的强大的MPR算法.
  • 基于数据操纵,特征操纵和模型结构的算法分类.

主要成果:

  • 确定了导致肌电控制中的校准负担的常见场景.
  • 总结了适用于各种场景的强大的MPR算法.
  • 详细说明了每个分类算法的适用性和校准要求.

结论:

  • 强大的MPR技术在减少肌电界面的用户校准负担方面提供了显著的优势.
  • 剩余的挑战包括进一步提高算法的稳定性和适应性.
  • 未来的机遇在于开发更直观,更轻松的MPR系统,以增强用户体验和更广泛的应用.