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dc.contributor.authorYeo, Hui Shyong
dc.contributor.authorWu, Erwin
dc.contributor.authorLee, Juyoung
dc.contributor.authorQuigley, Aaron John
dc.contributor.authorKoike, Hideki
dc.date.accessioned2019-10-21T10:30:06Z
dc.date.available2019-10-21T10:30:06Z
dc.date.issued2019-10-17
dc.identifier.citationYeo , H S , Wu , E , Lee , J , Quigley , A J & Koike , H 2019 , Opisthenar : hand poses and finger tapping recognition by observing back of hand using embedded wrist camera . in Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST 2019) . ACM , New York , pp. 963-971 , 32nd ACM User Interface Software and Technology Symposium (UIST 2019) , New Orleans , Louisiana , United States , 20/10/19 . https://doi.org/10.1145/3332165.3347867en
dc.identifier.citationconferenceen
dc.identifier.isbn9781450368162
dc.identifier.otherPURE: 260975557
dc.identifier.otherPURE UUID: 58d3cbdb-7dd4-42c9-9e01-a619ef28d4d5
dc.identifier.otherORCID: /0000-0002-5274-6889/work/63716823
dc.identifier.otherScopus: 85074839520
dc.identifier.otherWOS: 000518189200076
dc.identifier.urihttps://hdl.handle.net/10023/18715
dc.description.abstractWe introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.
dc.format.extent9
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofProceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST 2019)en
dc.rightsCopyright © 2019 Association for Computing Machinery. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3332165.3347867en
dc.subjectBack of the handen
dc.subjectOpisthenaren
dc.subjectHand poseen
dc.subjectFinger tappingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.titleOpisthenar : hand poses and finger tapping recognition by observing back of hand using embedded wrist cameraen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1145/3332165.3347867
dc.date.embargoedUntil2019-10-17


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