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Toward an enhanced human-machine interface for upper-limb prosthesis control with combined EMG and NIRS signals

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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. © 2017 IEEE.
Original languageEnglish
Article number7805220
Pages (from-to)564-575
Number of pages12
Journal IEEE Transactions on Human-Machine Systems
Volume47
Issue number4
Early online date4 Jan 2017
DOIs
Publication statusPublished - Aug 2017

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