GestureExplorer: Immersive visualisation and exploration of gesture data

Ang Li; Jiazhou Liu; Maxime Cordeil; Jack Topliss; Thammathip Piumsomboon; Barrett Ens

doi:10.1145/3544548.3580678

GestureExplorer: Immersive visualisation and exploration of gesture data

Ang Li, Jiazhou Liu, Maxime Cordeil, Jack Topliss, Thammathip Piumsomboon, Barrett Ens

Department of Human Centred Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

8 Citations (Scopus)

Abstract

This paper presents the design and evaluation of GestureExplorer, an Immersive Analytics tool that supports the interactive exploration, classification and sensemaking with large sets of 3D temporal gesture data. GestureExplorer features 3D skeletal and trajectory visualisations of gestures combined with abstract visualisations of clustered sets of gestures. By leveraging the large immersive space afforded by a Virtual Reality interface our tool allows free navigation and control of viewing perspective for users to gain a better understanding of gestures. We explored a selection of classification methods to provide an overview of the dataset that was linked to a detailed view of the data that showed different visualisation modalities. We evaluated GestureExplorer with two user studies and collected feedback from participants with diverse visualisation and analytics backgrounds. Our results demonstrated the promising capability of GestureExplorer for providing a useful and engaging experience in exploring and analysing gesture data.

Original language	English
Title of host publication	Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Editors	Stefanie Mueller, Julie R. Williamson, Max L. Wilson
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Number of pages	16
ISBN (Electronic)	9781450394215
DOIs	https://doi.org/10.1145/3544548.3580678
Publication status	Published - 2023
Event	International Conference on Human Factors in Computing Systems 2023 - , Germany Duration: 23 Apr 2023 → 28 Apr 2023 https://chi2023.acm.org/ (Website) https://dl.acm.org/doi/proceedings/10.1145/3544548 (Proceedings)

Conference

Conference	International Conference on Human Factors in Computing Systems 2023
Abbreviated title	CHI 2023
Country/Territory	Germany
Period	23/04/23 → 28/04/23
Internet address	https://chi2023.acm.org/ (Website) https://dl.acm.org/doi/proceedings/10.1145/3544548 (Proceedings)

Keywords

gesture elicitation study
immersive analytics
virtual reality

Access to Document

10.1145/3544548.3580678

Cite this

Li, A., Liu, J., Cordeil, M., Topliss, J., Piumsomboon, T., & Ens, B. (2023). GestureExplorer: Immersive visualisation and exploration of gesture data. In S. Mueller, J. R. Williamson, & M. L. Wilson (Eds.), Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems Article 380 Association for Computing Machinery (ACM). https://doi.org/10.1145/3544548.3580678

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title = "GestureExplorer: Immersive visualisation and exploration of gesture data",

abstract = "This paper presents the design and evaluation of GestureExplorer, an Immersive Analytics tool that supports the interactive exploration, classification and sensemaking with large sets of 3D temporal gesture data. GestureExplorer features 3D skeletal and trajectory visualisations of gestures combined with abstract visualisations of clustered sets of gestures. By leveraging the large immersive space afforded by a Virtual Reality interface our tool allows free navigation and control of viewing perspective for users to gain a better understanding of gestures. We explored a selection of classification methods to provide an overview of the dataset that was linked to a detailed view of the data that showed different visualisation modalities. We evaluated GestureExplorer with two user studies and collected feedback from participants with diverse visualisation and analytics backgrounds. Our results demonstrated the promising capability of GestureExplorer for providing a useful and engaging experience in exploring and analysing gesture data.",

keywords = "gesture elicitation study, immersive analytics, virtual reality",

author = "Ang Li and Jiazhou Liu and Maxime Cordeil and Jack Topliss and Thammathip Piumsomboon and Barrett Ens",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; International Conference on Human Factors in Computing Systems 2023, CHI 2023 ; Conference date: 23-04-2023 Through 28-04-2023",

year = "2023",

doi = "10.1145/3544548.3580678",

language = "English",

editor = "Stefanie Mueller and {R. Williamson}, Julie and {L. Wilson}, Max",

booktitle = "Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://chi2023.acm.org/, https://dl.acm.org/doi/proceedings/10.1145/3544548",

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Li, A, Liu, J, Cordeil, M, Topliss, J, Piumsomboon, T & Ens, B 2023, GestureExplorer: Immersive visualisation and exploration of gesture data. in S Mueller, J R. Williamson & M L. Wilson (eds), Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems., 380, Association for Computing Machinery (ACM), New York NY USA, International Conference on Human Factors in Computing Systems 2023, Hamburg, Germany, 23/04/23. https://doi.org/10.1145/3544548.3580678

GestureExplorer: Immersive visualisation and exploration of gesture data. / Li, Ang; Liu, Jiazhou; Cordeil, Maxime et al.
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ed. / Stefanie Mueller; Julie R. Williamson; Max L. Wilson. New York NY USA: Association for Computing Machinery (ACM), 2023. 380.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - This paper presents the design and evaluation of GestureExplorer, an Immersive Analytics tool that supports the interactive exploration, classification and sensemaking with large sets of 3D temporal gesture data. GestureExplorer features 3D skeletal and trajectory visualisations of gestures combined with abstract visualisations of clustered sets of gestures. By leveraging the large immersive space afforded by a Virtual Reality interface our tool allows free navigation and control of viewing perspective for users to gain a better understanding of gestures. We explored a selection of classification methods to provide an overview of the dataset that was linked to a detailed view of the data that showed different visualisation modalities. We evaluated GestureExplorer with two user studies and collected feedback from participants with diverse visualisation and analytics backgrounds. Our results demonstrated the promising capability of GestureExplorer for providing a useful and engaging experience in exploring and analysing gesture data.

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GestureExplorer: Immersive visualisation and exploration of gesture data

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