An Intelligent Tennis Training Robot with Timely Motion Feedback

Abstract

Tennis is widely popular across various age groups. However, mastering fluid stroke mechanics remains a significant challenge, requiring substantial time and practice. The lack of scientifically grounded tools for skill acquisition and training tools further hampers the development of tennis proficiency. In this paper, we investigate the application of an intelligent tennis robot equipped with advanced human-machine interaction capabilities, designed to serve as an effective tool for tennis learners, especially for enhancing motion geometry and coordination. First, we detail the design and development of the intelligent tennis robot, highlighting its core functions and operational principles, including ball serving, human motion sensing, and motion feedback mechanisms. Subsequently, we introduce a novel human motion analysis method that integrates motion geometry and kinematic analyses. Following this, we present a real-time motion feedback system that identifies deficiencies in players' movements, thereby facilitating the enhancement of their motion memory. Finally, we conduct experiments over players of varying skill levels, analyzing their motion patterns and providing practical examples. The proposed human-machine interaction framework offers a pioneering solution for intelligent tennis training, enabling players to understand their movements and correct errors immediately after each stroke.