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dc.contributor.authorHe, Jiayuan
dc.contributor.authorSheng, Xinjun
dc.contributor.authorZhu, Xiangyang
dc.contributor.authorJiang, Chaozhe
dc.contributor.authorJiang, Ning 13:38:27 (GMT) 13:38:27 (GMT)
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.description.abstractAutomatic gesture recognition (AGR) is investigated as an effortless human-machine interaction method, potentially applied in many industrial sectors. When using surface electromyogram (sEMG) for AGR, i.e. myoelectric control, a minimum of four EMG channels are required. However, in practical applications, fewer number of electrodes is always preferred, particularly for mobile and wearable applications. No published research focused on how to improve the performance of a myoelectric system with only two sEMG channels. In this study, we presented a systematic investigation to fill this gap. Specifically, we demonstrated that through spatial filtering and electrode position optimization, the myoelectric control performance was significantly improved (p < 0.05) and similar to that with four electrodes. Further, we found a significant correlation between offline and online performance metrics in the two-channel system, indicating that offline performance was transferable to online performance, highly relevant for algorithm development for sEMG-based AGR applications.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada || (Discovery Grant 072169) National Natural Science Foundation of China || (Grant 51620105002 and 91748119) State Key Lab of Railway Control and Safety Open Topics Fund of China || (Grant RCS2017K008).en
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectAutomatic gesture recognitionen
dc.subjectelectromyogram (EMG)en
dc.subjectError analysisen
dc.subjectGesture recognitionen
dc.subjectmyoelectric controlen
dc.subjectpattern recognitionen
dc.titleSpatial Information Enhances Myoelectric Control Performance with Only Two Channelsen
dcterms.bibliographicCitationHe, J., Sheng, X., Zhu, X., Jiang, C., & Jiang, N. (2018). Spatial Information Enhances Myoelectric Control Performance with Only Two Channels. IEEE Transactions on Industrial Informatics, 1–1.
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Systems Design Engineeringen

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