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dc.contributor.authorHe, Jiayuan
dc.contributor.authorLuo, Henry
dc.contributor.authorJia, Jie
dc.contributor.authorYeow, John T. W.
dc.contributor.authorJiang, Ning
dc.date.accessioned2018-10-11 14:36:55 (GMT)
dc.date.available2018-10-11 14:36:55 (GMT)
dc.date.issued2018-10-01
dc.identifier.urihttps://dx.doi.org/10.1109/TBME.2018.2872593
dc.identifier.urihttp://hdl.handle.net/10012/13994
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.abstractWith the ability to detect volumetric changes of contracting muscles, ultrasound (US) was a potential technique in the field of human-machine interface (HMI). Compared to the US imaging (B-mode US), the signal from a static single-element US transducer, A-mode US, was a more cost-effective and convenient way towards the real-world application, particularly the wearables. This study compared the performance of the single-channel A-mode US with single-channel surface electromyogram (sEMG) signals, one of the most popular signal modalities for wrist and finger gesture recognition. We demonstrated that A-mode US outperformed sEMG in six out of nine gestures recognition, while sEMG was superior to A-mode US on the detection of the Rest state. We also demonstrated that, through feature space analysis, the advantage of A-mode US over sEMG for gesture recognition was due to its superior ability in detecting information from deep musculature. This study presented the clear complementary advantages between A-mode US and sEMG, indicating the possibility of fusing two signal modalities for the gesture recognition applications.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada [Discovery Grant 072169]en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectWristen
dc.subjectElectrodesen
dc.subjectGesture recognitionen
dc.subjectpattern recognitionen
dc.subjectHuman machine interface (HMI)en
dc.subjectProbesen
dc.subjectsurface electromyogram (sEMG)en
dc.subjectThumben
dc.subjectTransducersen
dc.subjectultrasound signalen
dc.titleWrist and Finger Gesture Recognition with Single-element A-mode Ultrasound signal: A Comparison with Single-channel Surface Electromyogramen
dc.typeArticleen
dcterms.bibliographicCitationHe, J., Luo, H., Jia, J., Yeow, J. T. W., & Jiang, N. (2018). Wrist and Finger Gesture Recognition with Single-element A-mode Ultrasound signal: A Comparison with Single-channel Surface Electromyogram. IEEE Transactions on Biomedical Engineering, 1–1. https://doi.org/10.1109/TBME.2018.2872593en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Systems Design Engineeringen
uws.typeOfResourceTexten
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen


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