一个轻量级的冷多卷积双分支网络,用于高效的基于sEMG的手势识别
Shengbiao Wu1,2, Zhezhe Lv1, Yuehong Li1
1School of Electronics and Electrical Engineering, East China University of Technology, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究介绍了一种新的冷多卷积双分支网络 (FMC-DBNet),用于高效的表面电肌图 (sEMG) 手势识别. 该模型显著减少了培训时间和计算成本,为资源有限的设备提供了强大的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 康复技术 康复技术 康复技术
背景情况:
- 表面电肌图 (sEMG) 信号的非静止性给手势识别带来了挑战.
- 传统的深度学习模型用于sEMG分析,在计算上是昂贵的,训练时间很长.
- 资源有限的设备限制了复杂的深度学习模型在康复和假肢中的应用.
研究的目的:
- 为sEMG手势识别开发一种高效且计算成本低廉的深度学习模型.
- 解决传统可训练模型在训练时间和计算开销方面的局限性.
- 为了在低功耗设备上实现强大的手势识别,用于诸如康复辅助和智能假肢控制等应用.
主要方法:
- 提出了一个冷多卷积双分支网络 (FMC-DBNet),使用随机初始化和固定的卷积内核进行无训练的特征提取.
- 实现了双分支架构来处理原始sEMG信号和从变化模式分解 (VMD) 衍生的内在模式函数 (IMF).
- 整合了正比 (PPV) 和全球平均整合 (GAP) 统计数据,以增强多重解决方案的代表性.
主要成果:
- 在Ninapro DB1数据集上的27名受试者中,获得了96.4%±1.9%的平均准确性.
- 与传统可训练的卷积神经网络 (CNN) 基线相比,训练时间缩短了约90%.
- 证明了冷随机卷积结构的有效性,以实现高效和强大的sEMG手势识别.
结论:
- 冷的随机卷积结构为sEMG手势识别提供了完全训练的深度网络的高效和强大的替代方案.
- FMC-DBNet为现实应用中低功耗和计算效率高的手势识别提供了一个有前途的解决方案.
- 拟议的方法显著降低了计算开销,使先进的sEMG分析在资源有限的平台上可行.
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