一种新的双阶段驱动深度神经网络方法减轻电极移位对肌电模式识别系统的影响

Frank Kulwa, O. W. Samuel, M. G. Asogbon, Tolulope Tofunmi Oyemakinde, O. Obe, Guanglin Li
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引用次数: 0

摘要

基于模式识别(PR)的肌电假肢商业化的主要障碍是对电极移动等混杂因素缺乏鲁棒性,这一问题已经困扰多年。为了克服这一挑战,提出了一种新的双阶段卷积神经网络(DS-CNN)。DS-CNN由两个级联阶段组成,其中第一阶段破译特定类型移位的发生,在第二阶段触发必要的CNN模型以准确解码个体运动意图,这对于启动假肢的鲁棒控制是必要的。该方案将原始肌电信号作为输入,减少了传统基于机器学习的PR方案所需的预处理时间,并使用相同的网络架构有效地减轻了横向和纵向偏移。该方法在4种不同的电极移位条件(移位范围为7.50mm-10.05mm)下进行了验证,该数据集来自18名身体健全的受试者,他们执行了8类目标手势。实验结果表明,所提出的双阶段驱动深度神经网络模型可以充分解决电极移位的影响,并且在无移位场景附近(移位缓解与无移位场景差异< 1.70%)具有分类精度。这些结果表明,我们的方法可以为适应电极移位提供一种实用的解决方案,从而提高肌电模式识别系统在临床和商业环境中的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems
A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.
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