Design and Validation of a Sensor Fault-Tolerant Module for Real-Time High-Density EMG Pattern Recognition
Design and Validation of a Sensor Fault-Tolerant Module for Real-Time High-Density EMG Pattern Recognition
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DOI：
10.1109/embc46164.2021.9629541
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
D. J. Reynolds;Aashin Shazar;Xiaorong Zhang
中科院分区：
文献类型：
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作者：
D. J. Reynolds;Aashin Shazar;Xiaorong Zhang
随着电子技术的进步，高密度 (HD) EMG 传感系统已经可用，并已对其在神经机器接口 (NMI) 应用中的可行性和性能进行了研究。与传统的基于单通道的目标肌肉传感方法相比，HD EMG 传感在更大的表面积上对电活动进行采样，并有望：1）从一个时间和两个空间维度提供更丰富的神经信息，2）在现实生活中易于佩戴，无需放置解剖学上的目标电极。为了在实时 NMI 应用中使用高清肌电图，需要解决包括高计算负担和肌电图记录随时间的不可靠性等挑战。本文提出了一种基于 HD EMG PR 的 NMI，它将 HD EMG PR 与传感器容错模块 (SFTM) 无缝集成，旨在实时提供强大的 PR。实验结果表明，SFTM 能够将接触伪影和接触松动等干扰的 PR 精度恢复 6%-22%。开发了所提出的 HD EMG SFTM 的基于 Python 的实现，并证明其在实时性能方面具有计算效率。这些结果证明了基于实时 HD EMG PR 的 NMI 的可行性。
With the advancements in electronics technology, high-density (HD) EMG sensing systems have become available and have been investigated for their feasibility and performance in neural-machine interface (NMI) applications. Comparing to the traditional single channel-based targeted muscle sensing method, HD EMG sensing performs a sampling of the electrical activity over a larger surface area and has the promise of 1) providing richer neural information from one temporal and two spatial dimensions and 2) ease of wear in real life without the need of anatomically targeted electrode placement. To use HD EMG in real-time NMI applications, challenges including high computational burden and unreliability of EMG recordings over time need to be addressed. This paper presented an HD EMG PR based NMI which seamlessly integrates HD EMG PR with a Sensor Fault-Tolerant Module (SFTM) which aimed to provide robust PR in real time. Experimental results showed that the SFTM was able to recover the PR accuracies by 6%-22% from disturbances including contact artifacts and loose contacts. A Python-based implementation of the proposed HD EMG SFTM was developed and was demonstrated to be computationally efficient for real-time performance. These results have demonstrated the feasibility of a robust real-time HD EMG PR-based NMI.