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Updated: Sep 9, 2025

Functional Isolation of Single Motor Units of Rat Medial Gastrocnemius Muscle
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通过融合sEMG和MMG信号进行手动提升的肌肉疲劳分类研究

Zheng Wang1, Xiaorong Guan1,2, Dingzhe Li1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

这项研究引入了一种新的方法,通过融合表面电肌图 (sEMG) 和机械肌图 (MMG) 信号来分析举重过程中的肌肉疲劳. 从变压器 (BP + BERT) 算法获得的反向传播神经网络和双向编码器表示在分类肌肉疲劳方面达到98.10%的准确性.

关键词:
反向传播神经网络和BERT深度学习机械肌图肌肉疲劳表面电肌图

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