A Multi-Modal 3D Capturing Platform for Learning and Preservation of Traditional Sports and Games
Authors: François Destelle Amin Ahmadi Kieran Moran Noel E. OConnor Nikolaos Zioulis Anargyros Chatzitofis Dimitrios Zarpalas Petros Daras Jon Goenetxea Imaz Mikel Rodríguez Manderson Yvain Tisserand
Date: 26.10.2015
Abstract
We present a demonstration of a multi-modal 3D capturing 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.
BIB_text
title = {A Multi-Modal 3D Capturing Platform for Learning and Preservation of Traditional Sports and Games},
pages = {747-748},
keywds = {
Motion capture, Sensor Fusion, Vision, Motion Retargeting
}
abstract = {
We present a demonstration of a multi-modal 3D capturing 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.
}
isbn = {978-1-4503-3459-4},
isi = {1},
doi = {10.1145/2733373.2807975},
date = {2015-10-26},
year = {2015},
}
