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The $3 recognizer: simple 3D gesture recognition on mobile devices

Authors:

Sven Kratz,

Michael RohsAuthors Info & Claims

IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces

Pages 419 - 420

https://doi.org/10.1145/1719970.1720051

Published: 07 February 2010 Publication History

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Abstract

We present the $3 Gesture Recognizer, a simple but robust gesture recognition system for input devices featuring 3D acceleration sensors. The algorithm is designed to be implemented quickly in prototyping environments, is intended to be device-independent and does not require any special toolkits or frameworks, but relies solely on simple trigonometric and geometric calculations. Our method requires significantly less training data than other gesture recognizers and is thus suited to be deployed and to deliver results rapidly.

References

[1]

Sanna Kallio, Juha Kela, Jani Mäntyjärvi, and Johan Plomp. Visualization of hand gestures for pervasive computing environments. In Proc. AVI'06, pages 480--483. ACM, 2006.

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[2]

Sven Kratz and Michael Rohs. A $3 gesture recognizer - simple gesture recognition for devices equipped with 3d acceleration sensors. In Proc. IUI'10, 2010.

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[3]

Thomas Schloemer, Benjamin Poppinga, Niels Henze, and Susanne Boll. Gesture recognition with a wii controller. In Proc. TEI '08, pages 11--14. ACM, 2008.

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[4]

Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes. In Proc. UIST '07, pages 159--168, New York, NY, USA, 2007. ACM.

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Cited By

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Khanna PRamakrishnan IJain SBi XBalasubramanian A(2024)Hand Gesture Recognition for Blind Users by Tracking 3D Gesture TrajectoryProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642602(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642602
Villarreal-Narvaez SSluÿters AVanderdonckt JMbaki Luzayisu E(2022)Theoretically-Defined vs. User-Defined Squeeze GesturesProceedings of the ACM on Human-Computer Interaction10.1145/35678056:ISS(73-102)Online publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1145/3567805
Taranta EMaslych MGhamandi RLaViola J(2022)The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold SelectionProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502000(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502000
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Index Terms

The $3 recognizer: simple 3D gesture recognition on mobile devices
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens
    2. Interactive systems and tools

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Information & Contributors

Information

Published In

IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces

February 2010

460 pages

ISBN:9781605585154

DOI:10.1145/1719970

General Chairs:
Charles Rich
Worcester Polytechnic Institute, USA
,
Qiang Yang
Hong Kong University of Science & Technology, China
,
Program Chairs:
Marc Cavazza
Teesside University, UK
,
Michelle Zhou
IBM Research, China

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2010

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IUI '10

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IUI '10: 15th International Conference on Intelligent User Interfaces

February 7 - 10, 2010

Hong Kong, China

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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March 24 - 27, 2025

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Khanna PRamakrishnan IJain SBi XBalasubramanian A(2024)Hand Gesture Recognition for Blind Users by Tracking 3D Gesture TrajectoryProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642602(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642602
Villarreal-Narvaez SSluÿters AVanderdonckt JMbaki Luzayisu E(2022)Theoretically-Defined vs. User-Defined Squeeze GesturesProceedings of the ACM on Human-Computer Interaction10.1145/35678056:ISS(73-102)Online publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1145/3567805
Taranta EMaslych MGhamandi RLaViola J(2022)The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold SelectionProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502000(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502000
Vatavu RUngurean O(2022)Understanding Gesture Input Articulation with Upper-Body Wearables for Users with Upper-Body Motor ImpairmentsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501964(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501964
Xu HLyu FHuang JTu H(2022)Applying Sonification to Sketching in the Air With Mobile AR DevicesIEEE Transactions on Human-Machine Systems10.1109/THMS.2022.318659252:6(1352-1363)Online publication date: Dec-2022
https://doi.org/10.1109/THMS.2022.3186592
Magrofuoco NRoselli PVanderdonckt J(2021)Two-dimensional Stroke Gesture RecognitionACM Computing Surveys10.1145/346540054:7(1-36)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465400
Taranta II EPittman CMaghoumi MMaslych MMoolenaar YLaviola Jr J(2021)MacheteACM Transactions on Computer-Human Interaction10.1145/342806828:1(1-46)Online publication date: 20-Jan-2021
https://dl.acm.org/doi/10.1145/3428068
Shaw ARuiz JAnthony L(2021)A Survey on Applying Automated Recognition of Touchscreen Stroke Gestures to Children’s InputInteracting with Computers10.1093/iwc/iwab00932:5-6(524-547)Online publication date: 21-Apr-2021
https://doi.org/10.1093/iwc/iwab009
Maghoumi MLaViola J(2019)DeepGRU: Deep Gesture Recognition UtilityAdvances in Visual Computing10.1007/978-3-030-33720-9_2(16-31)Online publication date: 21-Oct-2019
https://doi.org/10.1007/978-3-030-33720-9_2
Gheran BVanderdonckt JVatavu RKoskinen ILim YCerratto-Pargman TChow KOdom W(2018)Gestures for Smart RingsProceedings of the 2018 Designing Interactive Systems Conference10.1145/3196709.3196741(623-635)Online publication date: 8-Jun-2018
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A $3 gesture recognizer: simple gesture recognition for devices equipped with 3D acceleration sensors