## 1 Introduction  
The integration of technology into sports has ushered in a new era, transforming the way that athletes train, compete, and recover [1, 2, 3, 4]. From grassroots levels to the pinnacle of professional sports, technological advancements are redefining the boundaries of human performance and enhancing the overall experience for players, coaches, teachers, and fans alike. Technology in sports encompasses a broad spectrum of innovations, including advanced training equipment, performance monitoring devices, and sophisticated analytics software [5], which could provide valuable feedback to players. Table tennis, a sport celebrated for its lightning-fast rallies and strategic finesse, is also undergoing a technological transformation with the integration of science and technology into training and teaching [6]. The combination of science and technology with sports  
training can contribute to innovative training concepts, methods, and techniques [3; 7; 8; 9; 10; 11; 12]. Although technology is revolutionizing training methods in table tennis, the popularity of intelligent technology in table tennis teaching remains relatively low. One of the biggest challenges in table tennis is the accurate determination (over 90%) of the position of a fast-moving (up to 100 km/h) [13] and small table tennis ball (40+ mm diameter), as well as the evaluation of the effectiveness of a player's stroke. In the field of table tennis ball recognition, sensor arrays are the most widely used technique to provide feedback on ball position [8; 14]. However, the deployment of sensors is still troublesome and time-consuming. Video streams represent a more effective way of tracking objects, but the accuracy of video-based techniques has been problematic because the small ball only occupies a few pixels on the camera screen. To address this issue, machine learning algorithms, such as deep learning, have been proposed [15; 16; 17]. These algorithms can be easily integrated into computer vision systems for both ball recognition and stroke effectiveness analysis. For instance, [PERSON] et al. developed an artificial intelligence (AI)-based system for badminton that enables shuttlecock tracking and automatic game analysis [18]. Similarly, [PERSON] et al. proposed TrackNet, a neural network-based ball trajectory tracking framework for tennis, achieving accurate predictions of ball trajectories in real time [19]. Compared to these applications, our work focuses on table tennis, which involves smaller, faster-moving balls and requires different algorithmic considerations for ball detection and player evaluation. To the best of our knowledge, few studies have addressed both ball tracking and stroke effectiveness evaluation in table tennis simultaneously. Moreover, unlike professional table tennis training, a widely adopted intelligent table tennis training system would require robustness and a relatively low cost. Existing products are expensive due to complex technology and outdated algorithms. For example, a high-speed image processing stereo camera [8] and/or the combination of inertial measurement units [20] are commonly used to estimate the trajectory and state of the table tennis ball to assist in training. However, these devices significantly increase the cost of the system, often over USD 10,000, which hinders large-scale deployment of the technology. This study aims to develop a cost-effective AI-driven table tennis instruction system that synergistically unites real-time 3D ball trajectory analysis (with the YOLOv5 algorithm) with advanced human motion evaluation (dynamic time wrapping) to enable a precise diagnosis of ball dynamics and player technique. By using consumer-grade hardware, our novel system collectively establishes causal relationships between player technique (e.g., joint angles) and ball dynamics (e.g., speed and placement), achieving this capacity at less than 10% of conventional costs.