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A method for outdoor skateboarding video games

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Keywan TonekaboniAuthors Info & Claims

ACE '10: Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology

Pages 40 - 44

https://doi.org/10.1145/1971630.1971642

Published: 17 November 2010 Publication History

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Abstract

Video games aimed at motivating players to exercises have gained popularity over the last few years, but most games are still designed for indoor scenarios. In this paper, we present a platform for a novel game concept: a mobile video game that is controlled by performing tricks on a real skateboard. The platform consists of two parts. A well-protected small wireless sensor module integrated unobtrusively into a skateboard and trick detection software that employs data mining techniques to classify skateboarding tricks from the raw data. We show the feasibility of the approach by presenting Tilt'n'Roll, a prototype skateboarding game application built on this platform.

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Cited By

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Vec VTomažič SKos AUmek A(2024)Trends in real-time artificial intelligence methods in sports: a systematic reviewJournal of Big Data10.1186/s40537-024-01026-011:1Online publication date: 26-Oct-2024
https://doi.org/10.1186/s40537-024-01026-0
Abdullah MIbrahim MShapiee MMohd Razman MMusa RAbu Osman NZakaria MP. P. Abdul Majeed A(2023)An Evaluation of Different Input Transformation for the Classification of Skateboarding Tricks by Means of Transfer LearningInnovation and Technology in Sports10.1007/978-981-99-0297-2_22(269-275)Online publication date: 18-Apr-2023
https://doi.org/10.1007/978-981-99-0297-2_22
Ibrahim MShapiee MAbdullah MRazman MMusa RMajeed A(2021)The Classification of Skateboarding Tricks: A Support Vector Machine Hyperparameter Evaluation OptimisationRecent Trends in Mechatronics Towards Industry 4.010.1007/978-981-33-4597-3_93(1013-1022)Online publication date: 16-Jul-2021
https://doi.org/10.1007/978-981-33-4597-3_93
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Published In

ACE '10: Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology

November 2010

136 pages

ISBN:9781605588636

DOI:10.1145/1971630

General Chairs:
Victor R. L. Shen
National Taipei University, Taiwan
,
Henry Been-Lirn Duh
National University of Singapore, Singapore
,
Masahiko Inami
Keio University, Japan
,
Program Chairs:
Michael Haller
Upper Austria University of Applied Sciences, Austria
,
Yoshifumi Kitamura
Tohoku University, Japan

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2010

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ACE '10

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NTPU
CUTE

ACE '10: Advances in Computer Entertainment Technology Conference

November 17 - 19, 2010

Taipei, Taiwan

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Overall Acceptance Rate 36 of 90 submissions, 40%

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Vec VTomažič SKos AUmek A(2024)Trends in real-time artificial intelligence methods in sports: a systematic reviewJournal of Big Data10.1186/s40537-024-01026-011:1Online publication date: 26-Oct-2024
https://doi.org/10.1186/s40537-024-01026-0
Abdullah MIbrahim MShapiee MMohd Razman MMusa RAbu Osman NZakaria MP. P. Abdul Majeed A(2023)An Evaluation of Different Input Transformation for the Classification of Skateboarding Tricks by Means of Transfer LearningInnovation and Technology in Sports10.1007/978-981-99-0297-2_22(269-275)Online publication date: 18-Apr-2023
https://doi.org/10.1007/978-981-99-0297-2_22
Ibrahim MShapiee MAbdullah MRazman MMusa RMajeed A(2021)The Classification of Skateboarding Tricks: A Support Vector Machine Hyperparameter Evaluation OptimisationRecent Trends in Mechatronics Towards Industry 4.010.1007/978-981-33-4597-3_93(1013-1022)Online publication date: 16-Jul-2021
https://doi.org/10.1007/978-981-33-4597-3_93
Abdullah MIbrahim MShapiee MZakaria MMohd Razman MMusa RP. P. Abdul Majeed A(2021)The Effect of Image Input Transformation from Inertial Measurement Unit Data on the Classification of Skateboarding TricksRiTA 202010.1007/978-981-16-4803-8_42(424-432)Online publication date: 5-Aug-2021
https://doi.org/10.1007/978-981-16-4803-8_42
Abdullah MIbrahim MShapiee MAbdul Majeed AMohd Razman MMusa RZakaria M(2020)The Classification of Skateboarding Tricks by Means of Support Vector Machine: An Evaluation of Significant Time-Domain FeaturesEmbracing Industry 4.010.1007/978-981-15-6025-5_12(125-132)Online publication date: 9-Jul-2020
https://doi.org/10.1007/978-981-15-6025-5_12
Groh BFleckenstein MKautz TEskofier B(2017)Classification and visualization of skateboard tricks using wearable sensorsPervasive and Mobile Computing10.1016/j.pmcj.2017.05.00740:C(42-55)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.05.007
Mueller FKhot RGerling KMandryk R(2016)Exertion GamesFoundations and Trends in Human-Computer Interaction10.1561/110000004110:1(1-86)Online publication date: 29-Dec-2016
https://dl.acm.org/doi/10.1561/1100000041
Mubin ONovoa MForrester JIqbal RFaily SJiang NDogan HJacqui T(2016)GiggleBatProceedings of the 30th International BCS Human Computer Interaction Conference: Fusion!10.14236/ewic/HCI2016.33(1-10)Online publication date: 11-Jul-2016
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Ghosh SShah PNavarro LChen XPaternò FVäänänen KChurch KHäkkilä JKrüger ASerrano M(2016)MusiSkateProceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct10.1145/2957265.2961854(753-759)Online publication date: 6-Sep-2016
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Groh BFleckenstein MEskofier B(2016)Wearable trick classification in freestyle snowboarding2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2016.7516238(89-93)Online publication date: Jun-2016
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