Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
