Toward an Integrated Multi-Modal sEMG/MMG/NIRS Sensing System for Human-Machine Interface Robust to Muscular Fatigue
Toward an Integrated Multi-Modal sEMG/MMG/NIRS Sensing System for Human-Machine Interface Robust to Muscular Fatigue
复制标题
面向可抵抗肌肉疲劳的人机界面的集成多模式 sEMG/MMG/NIRS 传感系统
DOI：
10.1109/jsen.2020.3023742
复制
发表时间：
2021-02-01
影响因子：
4.3
通讯作者：
Zhu, Xiangyang
中科院分区：
文献类型：
--
作者：
Sheng, Xinjun;Ding, Xuecong;Zhu, Xiangyang
研究基于肌肉活动的人机界面对提高截肢患者的生活质量具有重要意义。然而，由于频繁的肌肉收缩，HMI性能受到肌肉疲劳的限制。为了克服这一缺点，本文提出了一种多模态传感系统，可以同时收集表面肌电（sEMG），近红外光谱（NIRS）和肌力（MMG）。为了评估多模态信号采集系统的性能，进行了增量等长随意收缩实验。实验结果表明，该系统能够从电生理、氧代谢和肌纤维低频振动的角度可靠地获取3种肌肉收缩信息。此外，肌肉疲劳引起的实验模拟人机界面的使用，它令人信服地证明了一个显着的（${p} < 0.01$）提高分类精度（CA），通过使用多模态特征。在肌肉疲劳时，CA补偿3.6% ~ 22.9%。这些结果表明，多模态感测可以提高HMI性能和鲁棒性。本研究的结果具有很大的潜力，以促进人机交互的生物医学和临床应用。
The research on muscular activity based human-machine interface (HMI) is of great significance, such as controlling prosthetic hand to improve the life quality of amputee patients. However, the HMI performance is limited by muscular fatigue due to frequent muscle contraction. To overcome the drawback, this paper presents a multi-modal sensing system that can collect surface electromyography (sEMG), near-infrared spectroscopy (NIRS) and mechanomyography (MMG) simultaneously. To evaluate the performance of the multi-modal signal acquisition system, incremental isometric voluntary contractions experiment is carried out. The experimental results show that the proposed system can reliably obtain three kinds of muscle contraction information from the perspective of electrophysiology, oxygen metabolism and low-frequency vibration of myofiber. Furthermore, muscle fatigue induced experiment imitating HMI usage is performed, and it convincingly demonstrates a significantly ( ${p} < 0.01$ ) improved classification accuracy (CA) by using multi-modal features. The CA is compensated by 3.6% ~ 22.9% in the presence of muscular fatigue. These results suggest that multi-modal sensing can improve the HMI performance and robustness. The outcomes of this study have great potential to promote the biomedical and clinical applications of human-machine interaction.