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GestureMap : Supporting Visual Analytics and Quantitative Analysis of Motion Elicitation Data by Learning 2D Embeddings

Title data

Dang, Hai ; Buschek, Daniel:
GestureMap : Supporting Visual Analytics and Quantitative Analysis of Motion Elicitation Data by Learning 2D Embeddings.
2021
Event: CHI Conference on Human Factors in Computing Systems , 08.05.2021 - 13.05.2021 , online (originally: Yokohama, Japan).
(Conference item: Conference , Paper )
DOI: https://doi.org/10.1145/3411764.3445765

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AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

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https://osf.io/dzn5g/

Abstract in another language

This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture groups at a glance; 2) compute a new consensus measure (variance around average gesture); and 3) cluster gestures with k-means. We evaluate GestureMap and its concepts with eight experts and an in-depth analysis of published data. Our findings show how GestureMap facilitates exploring large datasets and helps researchers to gain a visual understanding of elicited gesture spaces. It further opens new directions, such as comparing elicitations across studies. We discuss implications for elicitation studies and research, and opportunities to extend our approach to additional tasks in gesture elicitation.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Gesture elicitation; dimensionality reduction; deep learning; visual analytics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 27 May 2021 06:20
Last Modified: 27 May 2021 06:20
URI: https://eref.uni-bayreuth.de/id/eprint/64581