Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

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 surfac...

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Published inIEEE transactions on human-machine systems Vol. 47; no. 4; pp. 564 - 575
Main Authors Guo, Weichao, Sheng, Xinjun, Liu, Honghai, Zhu, Xiangyang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN2168-2291
2168-2305
DOI10.1109/THMS.2016.2641389

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Summary: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 realtime 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.
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ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2016.2641389