A Real Time Artificial Intelligent System for Tennis Swing Classification

In recent times, The “Stay at Home” order has made it a challenge for physical education, especially sports. Tennis players require routine training, but both players and coaches need a new way to continue training while maintaining social distance. This paper proposes a real time machine learning system that enables individual tennis players to have real and independent tennis training without social contact. Our system uses a SensorTile development hardware and embedded workbench software to collect real time sensor data utilizing accelerometers, gyroscopes, and magnetometers. This data can be utilized to detect the motion and orientation of the tennis racket, with this SensorTile system mounted on it. We used several machine learning methods to perform real time tennis swing classification with a variety of tennis players, producing very accurate classification results. Therefore, using this proposed machine learning system, players now have an effective training machine that can tell them if their swings are accurate, eliminating the possibility for human error.