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Updated: May 28, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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人类下肢多关节连续运动估计基于表面电肌图.

Yonglin Han1, Qing Tao1, Xiaodong Zhang2

  • 1School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新型模型,用于使用表面电肌图 (sEMG) 信号来估计下肢关节角度,显著提高了康复和机器人的准确性. CB-TCN模型增强了特征提取,以更好地进行运动分析.

关键词:
这就是为什么CBAM是CBAM.TCN TCN 是一个数字.不同的运动模式,不同的运动模式.多关节角度估计估计sEMG 的意思是

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科学领域:

  • 生物力学和机器人技术
  • 生物医学工程 生物医学工程
  • 机器学习用于医疗保健

背景情况:

  • 精确估计多关节角度对于下肢康复,运动控制和外骨机器人技术至关重要.
  • 目前使用表面电肌图 (sEMG) 的方法在行走和坐等各种运动模式中面临挑战.
  • 从sEMG开发用于连续关节角度预测的强大模型是一个持续的研究需求.

研究的目的:

  • 提出并验证一种新型模型,即CB-TCN (时间卷积网络 + 卷积块注意模块 + 时间卷积网络),用于在下肢的连续多关节角度估计.
  • 通过将时间卷积网络 (TCN) 与卷积块注意模块 (CBAM) 集成来增强特征提取和预测准确性.
  • 通过数据增强技术,提高模型在不同运动模式的概括性.

主要方法:

  • 开发CB-TCN模型,将TCN用于时间特征提取和CBAM用于基于注意力的特征增强.
  • 实施移动窗口数据增强方法,以增加培训样本大小和模型适应性.
  • 实验验证涉及8名受试者执行四种不同的下肢运动:行走,跨越障碍物,腰和膝盖曲-延伸.

主要成果:

  • 与传统模型相比,CB-TCN模型在预测下肢关节角度方面表现出更高的准确性和稳定性.
  • 实现了高性能指标,包括R2值高达0.9718,根平均平方误差 (RMSE) 低至1.2648°,以及正常化的RMSE (NRMSE) 低至0.05234用于走路时的膝盖角度预测.
  • 该模型有效地捕捉了时间动态,并专注于突出特征以改善预测.

结论:

  • 拟议的CB-TCN模型整合了TCN和CBAM,显示了从sEMG信号预测下肢关节角度的显著优势.
  • 该方法为推进下肢康复,运动分析和开发智能机器人系统提供了一个有希望的解决方案.
  • 该模型的增强特征提取和概括能力解决了基于sEMG的运动估计中的关键挑战.