Wrist and Finger Gesture Recognition with Single-element A-mode Ultrasound signal: A Comparison with Single-channel Surface Electromyogram
| dc.contributor.author | He, Jiayuan | |
| dc.contributor.author | Luo, Henry | |
| dc.contributor.author | Jia, Jie | |
| dc.contributor.author | Yeow, John | |
| dc.contributor.author | Jiang, Ning | |
| dc.date.accessioned | 2018-10-11T14:36:55Z | |
| dc.date.available | 2018-10-11T14:36:55Z | |
| dc.date.issued | 2018-10-01 | |
| 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.abstract | With 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.sponsorship | Natural Sciences and Engineering Research Council of Canada [Discovery Grant 072169] | en |
| dc.identifier.uri | https://dx.doi.org/10.1109/TBME.2018.2872593 | |
| dc.identifier.uri | http://hdl.handle.net/10012/13994 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers | en |
| dc.subject | Wrist | en |
| dc.subject | Electrodes | en |
| dc.subject | Gesture recognition | en |
| dc.subject | pattern recognition | en |
| dc.subject | Human machine interface (HMI) | en |
| dc.subject | Probes | en |
| dc.subject | surface electromyogram (sEMG) | en |
| dc.subject | Thumb | en |
| dc.subject | Transducers | en |
| dc.subject | ultrasound signal | en |
| dc.title | Wrist and Finger Gesture Recognition with Single-element A-mode Ultrasound signal: A Comparison with Single-channel Surface Electromyogram | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | He, 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.2018.2872593 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Systems Design Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.typeOfResource | Text | en |
| uws.typeOfResource | Text | en |