Deep Augmentation for Electrode Shift Compensation in Transient High-density sEMG: Towards Application in Neurorobotics
Deep Augmentation for Electrode Shift Compensation in Transient High-density sEMG: Towards Application in Neurorobotics
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DOI：
10.1109/iros47612.2022.9981786
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发表时间：
2022-07
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影响因子：
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通讯作者：
Tianyun Sun;Jacqueline Libby;J. Rizzo;S. F. Atashzar
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作者：
Tianyun Sun;Jacqueline Libby;J. Rizzo;S. F. Atashzar
超越了传统的稀疏多通道外围人机接口，已被广泛应用于神经机器人，高密度表面肌电图（HD-sEMG）解码上肢运动控制显示出显着的潜力。我们最近提出了在深度神经网络架构中对大量手势（>60）进行LSTM的异构时间膨胀，以确保空间分辨率和快速收敛。然而，几个基本问题仍然没有答案。本文明确针对的一个问题是“电极移位”问题，这可能专门发生在高密度系统和传感器网格的落纱和穿戴过程中。另一个现实世界的问题是瞬态与高原分类的问题，这与神经接口的时间分辨率和无缝控制有关。在本文中，我们第一次实现手势预测的瞬态阶段的HD-sEMG数据，同时鲁棒的人机接口解码器电极移位。为此，我们提出了瞬态HD-sEMG的深度数据增强的概念。我们表明，如果不使用建议的增强，10毫米的轻微移位可能会降低解码器的性能低至20%。将提出的数据增强与3D卷积神经网络（CNN）相结合，我们将性能恢复到84.6%，同时确保高时空分辨率，增强电极移位，并更接近最终用户的大规模采用，增强弹性。
Going beyond the traditional sparse multichannel peripheral human-machine interface that has been used widely in neurorobotics, high-density surface electromyography (HD-sEMG) has shown significant potential for decoding upper-limb motor control. We have recently proposed heterogeneous temporal dilation of LSTM in a deep neural network architecture for a large number of gestures (>60), securing spatial resolution and fast convergence. However, several fundamental questions remain unanswered. One problem targeted explicitly in this paper is the issue of “electrode shift,” which can happen specifically for high-density systems and during doffing and donning the sensor grid. Another real-world problem is the question of transient versus plateau classification, which connects to the temporal resolution of neural interfaces and seamless control. In this paper, for the first time, we implement gesture prediction on the transient phase of HD-sEMG data while robustifying the human-machine interface decoder to electrode shift. For this, we propose the concept of deep data augmentation for transient HD-sEMG. We show that without using the proposed augmentation, a slight shift of 10mm may drop the decoder’s performance to as low as 20%. Combining the proposed data augmentation with a 3D Convolutional Neural Network (CNN), we recovered the performance to 84.6% while securing a high spatiotemporal resolution, robustifying to the electrode shift, and getting closer to large-scale adoption by the end-users, enhancing resiliency.