IFIANet:基于sEMG的动作意图识别中的时间频率频率注意网络
Gang Zheng1, Jiankai Lin1, Jiawei Zhang1
1College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了IFIANet,这是一个深度学习框架,用于使用表面电肌图 (sEMG) 信号识别运动意图. IFIANet通过准确预测用户移动来增强外骨控制,达到超过82%的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 外骨系统需要精确的运动意图识别来实现自然的人机交互.
- 表面电肌图 (sEMG) 信号提供了运动前的神经激活数据,对于意图识别至关重要.
研究的目的:
- 开发一个先进的深度学习框架,IFIANet,以改进基于sEMG的运动意图识别.
- 为了提高sEMG信号处理的准确性和稳定性,用于外骨控制.
主要方法:
- 开发了一个基于CNN-TCN的网络,以从sEMG信号中有效地学习时空特征.
- 一个创新的频率信息集成注意 (IFIA) 模块被设计为整合全球频率信息,提高特征可区分性.
主要成果:
- IFIANet框架在不同的预测时间表中表现稳定.
- 在MyPredict1数据集的9名参与者中,平均识别准确度超过了82%.
- 该模型有效地将局部时间频率特征与全球频率特征融合在一起.
结论:
- IFIANet提供了一种高效可靠的方法,用于基于sEMG的运动意图识别.
- 拟议的框架显著推进了下肢外骨架系统的智能控制.
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