  Numerous studies have demonstrated the manifold benefits of tennis, such as
increasing overall physical and mental health. Unfortunately, many children and
youth from low-income families are unable to engage in this sport mainly due to
financial constraints such as private lesson expenses as well as logistical
concerns to and back from such lessons and clinics. While several tennis
self-training systems exist, they are often tailored for professionals and are
prohibitively expensive. The present study aims to classify tennis players'
skill levels and classify tennis strokes into phases characterized by motion
attributes for a future development of an AI-based tennis self-training model
for affordable and convenient applications running on devices used in daily
life such as an iPhone or an Apple Watch for tennis skill improvement. We
collected motion data, including Motion Yaw, Roll and Pitch from inertial
measurement units (IMUs) worn by participating junior tennis players. For this
pilot study, data from twelve participants were processed using Support Vector
Machine (SVM) algorithms. The SVM models demonstrated an overall accuracy of
77% in classifying players as beginners or intermediates, with low rates of
false positives and false negatives, effectively distinguishing skill levels.
Additionally, the tennis swings were successfully classified into five phases
based on the collected motion data. These findings indicate that SVM-based
classification can be a reliable foundation for developing an equitable and
accessible AI-driven tennis training system.
