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Article

Gesture Recognition with a 3-D Accelerometer

Authors:

Shijian LiAuthors Info & Claims

UIC '09: Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing

Pages 25 - 38

https://doi.org/10.1007/978-3-642-02830-4_4

Published: 06 July 2009 Publication History

Abstract

Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called <em>FDSVM (</em> Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.

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Gesture Recognition with a 3-D Accelerometer
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Information & Contributors

Information

Published In

cover image Guide Proceedings

UIC '09: Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing

July 2009

388 pages

ISBN:9783642028298

Editors:
Daqing Zhang
Institut TELECOM & Management SudParis, Telecommunication Network and Services Department, HANDICOM Lab.,, Evry Cedex, France 91011
,
Marius Portmann
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia QLD 4072
,
Ah-Hwee Tan
School of Computer Engineering, Division of Information Systems, Nanyang Technological University, Singapore 639798
,
Jadwiga Indulska
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia QLD 4072

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 06 July 2009

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https://dl.acm.org/doi/10.1145/3654777.3676421
Zhang Y(2022)Application of Knowledge Model in Dance Teaching Based on Wearable Device Based on Deep LearningMobile Information Systems10.1155/2022/32995922022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3299592
Lin SGamage NHerath KWithana A(2022)MyoSpring: 3D Printing Mechanomyographic Sensors for Subtle Finger Gesture RecognitionProceedings of the Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3490149.3501321(1-13)Online publication date: 13-Feb-2022
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Vanderdonckt JRoselli PPérez-Medina JD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)!FTL, an Articulation-Invariant Stroke Gesture Recognizer with Controllable Position, Scale, and Rotation InvariancesProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3243032(125-134)Online publication date: 2-Oct-2018
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