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一种转移学习方法,用于识别脚行走,使用腿部肌肉表面肌电图.

Andrea Manni1, Gabriele Rescio1, Anna Maria Carluccio1

  • 1National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.

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PubMed
概括
此摘要是机器生成的。

早期检测脚步是预防健康问题的关键. 这项研究使用表面电肌学 (sEMG) 和转移学习 (TL) 来准确地从肌肉信号中识别脚走路模式.

关键词:
步态 步态 步态 步态sEMG 的意思是说.传感器 传感器 传感器脚行走 脚行走转移学习转移学习

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科学领域:

  • 生物医学工程 生物医学工程
  • 神经学 神经学
  • 运动医学 运动医学

背景情况:

  • 步态监测对于早期检测诸如脚行走等运动异常至关重要.
  • 持续的脚行走会导致严重的肌肉骨问题,平衡问题和生活质量的降低.
  • 表面肌电图 (sEMG) 通过捕捉运动前的肌肉电活动,提供了早期检测的潜力.

研究的目的:

  • 提出一种新的方法,用下肢的sEMG信号来检测脚行走.
  • 为了解决sEMG数据固有的复杂性和噪声,以便进行可靠的分类.
  • 利用转移学习 (TL) 来提高不同sEMG设备的模型准确性和通用性.

主要方法:

  • 使用表面电肌图 (sEMG) 传感器来记录下肢肌肉的电活动.
  • 应用连续波纹转换 (CWT) 将1秒的sEMG信号窗口转换为2D尺度图像.
  • 雇员转移学习 (TL) 使用预训练的神经网络架构来分类脚走路模式.

主要成果:

  • 在使用InceptionResNetV2架构的公共数据集上实现了大约95%的分类准确性.
  • 证明了拟议的sEMG和TL方法在识别脚行走方面的有效性.
  • 突出了CWT生成的头图的潜力,用于从噪音严重的sEMG数据中进行强大的特征提取.

结论:

  • 开发的基于sEMG的方法,增强了转移学习和CWT,在检测脚行走方面显示出高准确性.
  • 这种方法为早期诊断和监测步行异常提供了一个有希望的非侵入性工具.
  • 进一步的研究可以探索更广泛的应用在临床环境和不同人群的步态分析.