e-cosmos: Best Paper Award


Göttingen Dialogue in Digital Humanities (GDDH)

 


Im Rahmen des 2015 Wettbewerbs des Göttingen Centre for Digital Humanities (http://www.gcdh.de/en/) hat der Beitrag von Daniel Schüller, Christian Beecks, Marwan Hassani, Jennifer Hinnell, Bela Brenger, Thomas Seidl und Irene Mittelberg den ersten Preis gewonnen:

„Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action“.

Die Auswahl erfolgte auf Grundlage des eingereichten Papers und eines Vortrags in Göttingen am 23.6.2015 von Christian Beecks, Irene Mittelberg und Daniel Schüller.

 

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Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action

Daniel Schüller, Christian Beecks, Marwan Hassani, Jennifer Hinnell, Bela Brenger, Thomas Seidl and Irene Mittelberg

 

Natural Media Lab, Human Technology Centre, RWTH Aachen University

Data Management and Exploration Group, RWTH Aachen University

Department of Linguistics, University of Alberta

 

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation –  is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets.

 


 

Artikel erscheint demnächst in  ‘Digital Humanities Quaterly’, http://www.digitalhumanities.org/dhq/

 

Andere Beiträge im Wettbewerb: 

http://www.gcdh.de/en/events/gottingen-dialog-digital-humanities/