Artificial Intelligence Aided Training in Ping Pong Sport Education

Abstract

Recently, artificial intelligence has made huge strides in sports analysis. This paper attempts to focus this technology into table tennis with a real-time machine learning system that enables individual ping pong players to have independent training. This system enables table tennis players to maintain the benefits of training with a coach, without the physical presence of one. This, of course, also helps to practice social distancing under present situations. Our system uses a SensorTile development hardware and embedded workbench software to collect real time sensor data, using a variety of MEMS sensors such as accelerometers, gyroscopes, and magnetometers. Therefore, the mounted SensorTile system can detect the motion and orientation of the table tennis racket. We used machine learning (ML) methods to perform real-time table tennis stroke classification producing accurate classification results. Using this proposed machine learning system, players now have an effective training machine that is able to tell them if their strokes are accurate. This also reduces private coaching time in an attempt to limit unnecessary exposure, while still allowing players to receive feedback to improve their game.