extended-abstract

Movement interaction design for immersive media using interactive machine learning

Authors: Nicola Plant, Ruth Gibson, Carlos Gonzalez Diaz, Bruno Martelli, Michael Zbyszyński, Rebecca Fiebrink, Marco Gillies, Clarice Hilton, Phoenix PerryAuthors Info & Claims

MOCO '20: Proceedings of the 7th International Conference on Movement and Computing

Article No.: 33, Pages 1 - 2

https://doi.org/10.1145/3401956.3404252

Published: 15 July 2020 Publication History

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Abstract

Interactive Machine Learning is a promising approach for designing movement interaction because it allows developers to capture complex movements by simply performing them. We introduce a new tool being developed to make embodied interaction design faster, adaptable and accessible to developers of varying experience and background. Using the tool, we conduct workshops with creative practitioners and developers to explore techniques that equip users with embodied ideation design strategies encouraging full body interaction for immersive media.

References

[1]

Carlos Gonzalez Diaz, Phoenix Perry, and Rebecca Fiebrink. 2019. Interactive Machine Learning for More Expressive Game Interactions. In 2019 IEEE Conference on Games (CoG). IEEE, London, United Kingdom, 1--2.

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[2]

Marco Gillies. 2019. Understanding the Role of Interactive Machine Learning in Movement Interaction Design. ACM Transactions on Computer-Human Interaction 26, 1 (Feb. 2019), 1--34.

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[3]

Kristina Höök. 2018. Designing with the Body - Somaesthetic Interaction Design. In Anais Estendidos do XVII Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais (IHC). Sociedade Brasileira de Computação (SBC).

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Elena Márquez Segura, Laia Turmo Vidal, Asreen Rostami, and Annika Waern. 2016. Embodied Sketching. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, San Jose California USA, 6014--6027.

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Escamilla AMelenchón JMonzo CMoran J(2023)User Clustering Visualization and Its Impact on Motion-Based Interaction DesignHuman-Computer Interaction10.1007/978-3-031-35596-7_4(47-63)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35596-7_4
Correia NMasu RPrimett WJürgens SFeitsch JPlácido da Silva H(2022)Designing Interactive Visuals for Dance from Body Maps: Machine Learning and Composite Animation ApproachesProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533467(204-216)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533467
Hilton CPlant NGonzález Díaz CPerry PGibson RMartelli BZbyszynski MFiebrink RGillies M(2021)InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive mediaProceedings of the 27th ACM Symposium on Virtual Reality Software and Technology10.1145/3489849.3489879(1-10)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3489849.3489879

Index Terms

Movement interaction design for immersive media using interactive machine learning
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction techniques
      1. Gestural input
  2. Interaction design
    1. Systems and tools for interaction design

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Published In

MOCO '20: Proceedings of the 7th International Conference on Movement and Computing

July 2020

205 pages

ISBN:9781450375054

DOI:10.1145/3401956

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Rutgers University: Rutgers University

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Association for Computing Machinery

New York, NY, United States

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Published: 15 July 2020

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Engineering and Physical Sciences Research Council

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MOCO '20

MOCO '20: 7th International Conference on Movement and Computing

July 15 - 17, 2020

NJ, Jersey City/Virtual, USA

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Escamilla AMelenchón JMonzo CMoran J(2023)User Clustering Visualization and Its Impact on Motion-Based Interaction DesignHuman-Computer Interaction10.1007/978-3-031-35596-7_4(47-63)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35596-7_4
Correia NMasu RPrimett WJürgens SFeitsch JPlácido da Silva H(2022)Designing Interactive Visuals for Dance from Body Maps: Machine Learning and Composite Animation ApproachesProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533467(204-216)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533467
Hilton CPlant NGonzález Díaz CPerry PGibson RMartelli BZbyszynski MFiebrink RGillies M(2021)InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive mediaProceedings of the 27th ACM Symposium on Virtual Reality Software and Technology10.1145/3489849.3489879(1-10)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3489849.3489879

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Interactive Machine Learning for Embodied Interaction Design: A tool and methodology

InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media

Understanding the Role of Interactive Machine Learning in Movement Interaction Design

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Interactive Machine Learning for Embodied Interaction Design: A tool and methodology

InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media

Understanding the Role of Interactive Machine Learning in Movement Interaction Design

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