Toward an Enhanced Human-Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals
Toward an Enhanced Human-Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals
复制标题
利用 EMG 和 NIRS 信号组合实现上肢假肢控制的增强型人机界面
DOI：
10.1109/thms.2016.2641389
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发表时间：
2017-08-01
影响因子：
3.6
通讯作者：
Zhu, Xiangyang
中科院分区：
文献类型：
--
作者：
Guo, Weichao;Sheng, Xinjun;Zhu, Xiangyang
目前先进的肌电假手由于缺乏足够的截肢残肌信号源和实时控制性能不足而受到限制。本文提出了一种新的人机界面假肢操作，结合表面肌电图（EMG）和近红外光谱（NIRS）的优点，克服肌电控制的局限性。实验包括13个健全的和截肢的3个科目进行评估的离线分类准确率（CA）和在线性能的前臂运动识别系统的基础上，三种类型的传感器（肌电，NIRS，只有，和混合肌电-NIRS）。实验结果表明，肌电信号和近红外光谱的结合，无论是离线CA和控制的虚拟假手的实时性能显着提高（p < 0.05）。这些研究结果表明，肌电信号和近红外光谱的融合是可行的，以改善上肢假肢的控制，而不增加传感器节点的数量或信号处理的复杂性。本研究的结果对桡动脉截肢者灵巧假手的开发具有很大的促进作用。
Advanced myoelectric prosthetic hands are currently limited due to the lack of sufficient signal sources on amputation residual muscles and inadequate real-time control performance. This paper presents a novel human-machine interface for prosthetic manipulation that combines the advantages of surface electromyography (EMG) and near-infrared spectroscopy (NIRS) to overcome the limitations of myoelectric control. Experiments including 13 able-bodied and three amputee subjects were carried out to evaluate both offline classification accuracy (CA) and online performance of the forearm motion recognition system based on three types of sensors (EMG-only, NIRS-only, and hybrid EMG-NIRS). The experimental results showed that both the offline CA and real-time performance for controlling a virtual prosthetic hand were significantly (p < 0.05) improved by combining EMG and NIRS. These findings suggest that fusion of EMG and NIRS is feasible to improve the control of upper-limb prostheses, without increasing the number of sensor nodes or complexity of signal processing. The outcomes of this study have great potential to promote the development of dexterous prosthetic hands for transradial amputees.