Variance-based shape descriptors for determining the level of expertise of tennis players
Exertion games form a vastly expanding field, crossing over to machine learning and user studies, with studies of qualitative traits of actions, such as the player's level of expertise. In this work, we show how simple shape descriptors based on variance features fare on such a demanding task. We formulate two variance-based features and experiment on a demanding sports related dataset, captured with a Kinect sensor, in an action-specific k-NN classification scheme. Results show that simple shape features can produce meaningful results on determining a player's experience level, further encouraging their incorporation in more intricate schemes and real-world applications.