The main goal of this work is to design an automated solution based on RGB-D data for quantitative analysis, perceptible evaluation and comparison of handball players performance. To that end, we introduced a new RGB-D dataset that can be used for an objective comparison and evaluation of handball players performance during throws. We filmed 62 handball players (44 beginners and 18 experts), who performed the same type of action, using a Kinect V2 sensor that provides RGB data, depth data and skeletons. Moreover, using skeleton data simulating 3D joint connections, we examined the main angles responsible for throwing performance in order to analyze individual skills of handball players (beginners against model and experts) relatively to throw actions. The comparison was performed statically (using only one frame) as well as dynamically during the entire throwing action. In particular, given the temporal sequence of 25 joints of each handball player, we adopted the dynamic time warping technique to compare the throwing motion between two athletes. The obtained results were found to be promising. Thus, the suggested markless solution would help handball coaches to optimize beginners movements during throwing actions.
© Copyright 2019 Journal of Ambient Intelligence and Humanized Computing. Springer. All rights reserved.
|Subjects:||handball throws technique analysis joint biomechanics auxiliary device movement movement co-ordination performance capacity training learning coordinative ability|
|Notations:||sport games biological and medical sciences|
|Published in:||Journal of Ambient Intelligence and Humanized Computing|