Skeleton-based comparison of throwing motion for handball players

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 player's performance. To that end, we introduced a new RGB-D dataset that can be used for an objective comparison and evaluation of handball player's 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.