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A $3 gesture recognizer: simple gesture recognition for devices equipped with 3D acceleration sensors

Authors:

Sven Kratz,

Michael RohsAuthors Info & Claims

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

Pages 341 - 344

https://doi.org/10.1145/1719970.1720026

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. It relies solely on simple trigonometric and geometric calculations. A user evaluation of our system resulted in a correct gesture recognition rate of 80%, when using a set of 10 unique gestures for classification. Our method requires significantly less training data than other gesture recognizers and is thus suited to be deployed and to deliver results rapidly.

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Cited By

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Yu XZhou ZZhang LLi X(2024)ThumbUp: Secure Smartwatch Controller for Smart Homes Using Simple Hand GesturesIEEE Transactions on Mobile Computing10.1109/TMC.2022.321692723:1(865-878)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3216927
Roy DAli APal SAdhikary A(2023)Impedimetric Hydrogel Sensor for the Identification of Hexose Using Machine LearningIEEE Sensors Journal10.1109/JSEN.2023.324075023:6(6272-6281)Online publication date: 15-Mar-2023
https://doi.org/10.1109/JSEN.2023.3240750
Costagliola GDe Rosa MFuccella V(2023)Gesture‐Based ComputingHandbook of Human‐Machine Systems10.1002/9781119863663.ch32(397-408)Online publication date: 7-Jul-2023
https://doi.org/10.1002/9781119863663.ch32
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Index Terms

A $3 gesture recognizer: simple gesture recognition for devices equipped with 3D acceleration sensors
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens

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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

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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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Cited By

View all

Yu XZhou ZZhang LLi X(2024)ThumbUp: Secure Smartwatch Controller for Smart Homes Using Simple Hand GesturesIEEE Transactions on Mobile Computing10.1109/TMC.2022.321692723:1(865-878)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3216927
Roy DAli APal SAdhikary A(2023)Impedimetric Hydrogel Sensor for the Identification of Hexose Using Machine LearningIEEE Sensors Journal10.1109/JSEN.2023.324075023:6(6272-6281)Online publication date: 15-Mar-2023
https://doi.org/10.1109/JSEN.2023.3240750
Costagliola GDe Rosa MFuccella V(2023)Gesture‐Based ComputingHandbook of Human‐Machine Systems10.1002/9781119863663.ch32(397-408)Online publication date: 7-Jul-2023
https://doi.org/10.1002/9781119863663.ch32
Calado ARoselli PErrico VMagrofuoco NVanderdonckt JSaggio G(2022)A Geometric Model-Based Approach to Hand Gesture RecognitionIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.313858952:10(6151-6161)Online publication date: Oct-2022
https://doi.org/10.1109/TSMC.2021.3138589
Vatavu RBilius L(2021)GestuRING: A Web-based Tool for Designing Gesture Input with Rings, Ring-Like, and Ring-Ready DevicesThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474780(710-723)Online publication date: 10-Oct-2021
https://dl.acm.org/doi/10.1145/3472749.3474780
Ousmer MSluÿters AMagrofuoco NRoselli PVanderdonckt J(2020)Recognizing 3D Trajectories as 2D Multi-stroke GesturesProceedings of the ACM on Human-Computer Interaction10.1145/34273264:ISS(1-21)Online publication date: 4-Nov-2020
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Aloba AWoodward JAnthony LTruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)FilterJoint: Toward an Understanding of Whole-Body Gesture ArticulationProceedings of the 2020 International Conference on Multimodal Interaction10.1145/3382507.3418822(213-221)Online publication date: 21-Oct-2020
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Yu XZhou ZXu MYou XLi X(2020)ThumbUp: Identification and Authentication by Smartwatch using Simple Hand Gestures2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127367(1-10)Online publication date: Mar-2020
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Coppers SLuyten KVanacken DNavarre DPalanque PGris C(2019)FortunettesProceedings of the ACM on Human-Computer Interaction10.1145/33311623:EICS(1-20)Online publication date: 13-Jun-2019
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