poster

Protractor3D: a closed-form solution to rotation-invariant 3D gestures

Authors:

Sven Kratz,

Michael RohsAuthors Info & Claims

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

Pages 371 - 374

https://doi.org/10.1145/1943403.1943468

Published: 13 February 2011 Publication History

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Abstract

Protractor 3D is a gesture recognizer that extends the 2D touch screen gesture recognizer Protractor to 3D gestures. It inherits many of Protractor's desirable properties, such as high recognition rate, low computational and low memory requirements, ease of implementation, ease of customization, and low number of required training samples. Protractor 3D is based on a closed-form solution to finding the optimal rotation angle between two gesture traces involving quaternions. It uses a nearest neighbor approach to classify input gestures. It is thus well-suited for application in resource-constrained mobile devices. We present the design of the algorithm and a study that evaluated its performance.

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Vatavu R(2023)iFAD Gestures: Understanding Users’ Gesture Input Performance with Index-Finger Augmentation DevicesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580928(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580928
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
Alam MKwon KMd Imtiaz SHossain MKang BKim N(2022)TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM NetworkHuman Behavior and Emerging Technologies10.1155/2022/60637792022(1-13)Online publication date: 24-Sep-2022
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Index Terms

Protractor3D: a closed-form solution to rotation-invariant 3D gestures
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 & Contributors

Information

Published In

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

February 2011

504 pages

ISBN:9781450304191

DOI:10.1145/1943403

General Chairs:
Pearl Pu
EPFL, Switzerland
,
Michael Pazzani
Rutgers University, USA
,
Program Chairs:
Elisabeth André
Augsburg University, Germany
,
Doug Riecken
CCLS Columbia University, USA

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: 13 February 2011

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IUI '11

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IUI '11: 16th International Conference on Intelligent User Interfaces

February 13 - 16, 2011

CA, Palo Alto, USA

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

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Cited By

View all

Vatavu R(2023)iFAD Gestures: Understanding Users’ Gesture Input Performance with Index-Finger Augmentation DevicesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580928(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580928
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
Alam MKwon KMd Imtiaz SHossain MKang BKim N(2022)TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM NetworkHuman Behavior and Emerging Technologies10.1155/2022/60637792022(1-13)Online publication date: 24-Sep-2022
https://doi.org/10.1155/2022/6063779
Xia HGlueck MAnnett MWang MWigdor D(2022)Iteratively Designing Gesture Vocabularies: A Survey and Analysis of Best Practices in the HCI LiteratureACM Transactions on Computer-Human Interaction10.1145/350353729:4(1-54)Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1145/3503537
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
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
Kenshimov CSundetov TKunelbayev MAmirgaliyeva ZYedilkhan DAuelbekov O(2021)Development of a Verbal Robot Hand Gesture Recognition SystemWSEAS TRANSACTIONS ON SYSTEMS AND CONTROL10.37394/23203.2021.16.5316(573-583)Online publication date: 11-Nov-2021
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Baldassin ABarreto JCastro DRomano P(2021)Persistent MemoryACM Computing Surveys10.1145/346540254:7(1-37)Online publication date: 18-Jul-2021
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Wang SCao LWang YSheng QOrgun MLian D(2021)A Survey on Session-based Recommender SystemsACM Computing Surveys10.1145/346540154:7(1-38)Online publication date: 18-Jul-2021
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Magrofuoco NRoselli PVanderdonckt J(2021)Two-dimensional Stroke Gesture RecognitionACM Computing Surveys10.1145/346540054:7(1-36)Online publication date: 18-Jul-2021
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Protractor: a fast and accurate gesture recognizer

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