Towards the next generation of exergames: Flexible and personalised assessment-based identification of tennis swings
This addresses the need for personalized, qualitative technique assessment in exergames for tennis players and coaches, though it is incremental in applying existing methods to a new domain.
The paper tackled the problem of automating the identification of erroneous tennis swings without ball impact data, achieving 84.5-94.6% accuracy in detecting unseen errors.
Current exergaming sensors and inertial systems attached to sports equipment or the human body can provide quantitative information about the movement or impact e.g. with the ball. However, the scope of these technologies is not to qualitatively assess sports technique at a personalised level, similar to a coach during training or replay analysis. The aim of this paper is to demonstrate a novel approach to automate identification of tennis swings executed with erroneous technique without recorded ball impact. The presented spatiotemporal transformations relying on motion gradient vector flow and polynomial regression with RBF classifier, can identify previously unseen erroneous swings (84.5-94.6%). The presented solution is able to learn from a small dataset and capture two subjective swing-technique assessment criteria from a coach. Personalised and flexible assessment criteria required for players of diverse skill levels and various coaching scenarios were demonstrated by assigning different labelling criteria for identifying similar spatiotemporal patterns of tennis swings.