Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72071
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dc.titleTrajectory modeling in gesture recognition using cybergloves® and magnetic trackers
dc.contributor.authorKevin, N.Y.Y.
dc.contributor.authorRanganath, S.
dc.contributor.authorGhosh, D.
dc.date.accessioned2014-06-19T03:31:06Z
dc.date.available2014-06-19T03:31:06Z
dc.date.issued2004
dc.identifier.citationKevin, N.Y.Y.,Ranganath, S.,Ghosh, D. (2004). Trajectory modeling in gesture recognition using cybergloves® and magnetic trackers. IEEE Region 10 Annual International Conference, Proceedings/TENCON A : A571-A574. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72071
dc.description.abstractThe recognition of human gestures is important for several human-computer interaction applications. In this paper, we develop a gesture recognition system that uses the condensation-based trajectory matching/recognition algorithm. The gesture data are collected using a pair of CybtrGloves® measuring hand-joint angles and three magnetic trackers that determine 3-D hand positions. The multi-dimensional gesture data are subsequently recognized by matching against trajectory models using probability measures. In our experiments, we evaluate the efficiency of our proposed gesture recognition system using three different gesture sets, viz., directional movements, static hand-shapes and American Sign Language (ASL) gestures. Experimental results show high recognition rate and signer-independence but less robustness to co-articulation effects. © 2004IEEE.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleIEEE Region 10 Annual International Conference, Proceedings/TENCON
dc.description.volumeA
dc.description.pageA571-A574
dc.description.coden85QXA
dc.identifier.isiutNOT_IN_WOS
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