Destelle, Francois, Ahmadi, Amin, Moran, Kieran ORCID: 0000-0003-2015-8967, O'Connor, Noel E. ORCID: 0000-0002-4033-9135, Zioulis, Nikolaos, Chatzitofis, Anargyros, Petros, Daras, Zarpalas, Dimitrios ORCID: 0000-0002-9649-9306, Unzueta, Luis, Rodriguez, Mikel, Goenetxea, Jon, Linaza, Maria Teresa, Magnenat Thalmann, Nadia and Tisserand, Yvain (2015) A multi-modal 3D capturing platform for learning and preservation of traditional sports and games. In: ACM Multimedia Modeling (ACM MM'15), 26 Oct 2015, Brisbane, Australia.
Abstract
We present a demonstration of a multi-modal 3D captur- ing platform coupled to a motion comparison system. This work is focused on the preservation of Traditional Sports and Games, namely the Gaelic sports from Ireland and Basque sports from France and Spain. Users can learn, compare and compete in the performance of sporting gestures and compare themselves to real athletes. Our online gesture database provides a way to preserve and display a wide range of sporting gestures. The capturing devices utilised are Kinect 2 sensors and wearable inertial sensors, where the number required varies based on the requested scenario. The fusion of these two capture modalities, coupled to our inverse kinematic algorithm, allow us to synthesize a fluid and reliable 3D model of the user gestures over time. Our novel comparison algorithms provide the user with a per- formance score and a set of comparison curves (i.e. joint angles and angular velocities), providing a precise and valu- able feedback for coaches and players.
Metadata
Item Type: | Conference or Workshop Item (Poster) |
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Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Motion capture; Sensor Fusion; Vision; Motion Retargeting |
Subjects: | UNSPECIFIED |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance Research Initiatives and Centres > INSIGHT Centre for Data Analytics |
Published in: | Proceedings ACM Multimedia. . |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 21154 |
Deposited On: | 22 Jun 2016 10:22 by Kieran Moran . Last Modified 03 Feb 2023 14:51 |
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