Towards the next generation of exergames: Flexible and personalised assessment-based identification of tennis swings

TL;DR

This paper introduces a novel, personalized method for automatically identifying erroneous tennis swings using inertial sensors, capable of learning from small datasets and capturing subjective coaching criteria.

Contribution

It presents a new approach combining motion gradient analysis and polynomial regression with RBF classification to detect errors in tennis swings without relying on ball impact data.

Findings

Achieved 84.5-94.6% accuracy in identifying erroneous swings.

Capable of learning from small datasets and subjective coaching criteria.

Demonstrated flexibility for players of various skill levels.

Abstract

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.