Advancing Table Tennis Analytics: A CNN-LSTM Framework for Precision Serve Shot Analysis

As for the current work, this research proposes a CNN-LSTM model systematized for evaluating and predicting the serve strokes in real-time table tennis. It is supposed to address the deficiency of extensive and timely sports analysis by combining the spatial FE capability of Convolutional Neural Networks (CNN) with the temporal dynamics learning function of Long Short-Term Memory (LSTM) networks. In the case of our signal, high-frame-rate video data is used to study and identify all manner of serve shots with a reasonable degree of precision. It also makes predictions that give essential indications of the strategic games. It is recommended that the suggested model be used to evaluate its performance, which was conducted with the help of the dataset containing the video recordings of table tennis serves. These recordings were captured at 120 frames per second, and each video was of the dimension $1920 \times 1080$ pixels. The presented model showcased the top performance of $\mathbf{9 2} \%$ with the precision and recall of 90 $\%$ and $\mathbf{8 5 \%}$ on the topspin serves. It proved to be $\mathbf{2 0 \%}$ better than the standard analytical methods and other independent schemes, which include CNNs and SVMs. Our study underscores the potential of the CNN-LSTM model to revolutionize sports analysis. By integrating this model, coaches, and athletes can significantly enhance their strategies and performance, thanks to the comprehensive vital information it provides. Moreover, this model improves our understanding of serve mechanics and paves the way for its application to other complex motions in sports.

I. Introduction

The serve stroke in table tennis is one of the most crucial as it sets the mood and predicts the game’s strategies. The opponent does not interfere, and the player is in total control of the positioning of the ball [1], as well as its rotation and velocity, in the case of this particular stroke. From current research and analysis of any encounter, successful serves may prove decisive to the match in most occasions as the opposition is forced into an unfavorable position from the early instance. However, due to the complexity and speed of serve shots, they cannot be well assessed and predicted in advanced observational analytics [2]. Thus, there is a need to study serve strokes in table tennis to develop the players and improve the strategies used in the game [3]. Problem Statement: However, there is an apparent lack of highly sophisticated and utterly functional serve shot analyzing tools for real-time and strategic analysis of situations. Previous approaches in the literature mainly [11]. Involve human observation and video playback analysis, which lacks the necessary data to study the shot mechanics and their consequences extensively. This constraint limits the ability to make the most of powerful shots as a strategist. It restricts the coaching methods to general plans, which may or may not be appropriate for a given style of a player or a specific situation in the matches [4]. Objective: In more detail, the overall aim of this project is to develop a new CNN-LSTM-based deep [10] learning framework that would enhance the analysis, understanding, and prediction of the serve shot dynamics in table tennis [5]. In an attempt to classify different types of serves and predict the likely outcome of each, the framework attempts to use high frame rate video data. This will give out information that could be applied during training and specific matches [6]. The proposed CNN-LSTM model can change the statics and dynamics of table tennis by providing superior knowledge about the serve shot mechanics that would be impossible otherwise with traditional methods [9]. It has the prospect of changing how coaching is done and improving players’ performance through the rendered feedback resulting from a detailed analysis of the data collected. This means that, unlike general drills aimed at improving everyone’s serves, coaches can alter the training exercises to adjust serves that would exploit rivals’ weaknesses. Plays may also receive information on their serve performance depending on the game that is in progress. Thus, it may lead to further complexity of strategic games and improved competitive outcomes, which could be considered progress in applying artificial intelligence in sports analysis [7]. When outlining such components initially, your study sets the groundwork for effectively noting the necessity of deep learning and the possible consequences of your proposed framework in enhancing table tennis serve shot statistics [8]. This would make insight on details of the game likely to be produced in dynamics. Furthermore, conducting studies regarding the application of this paradigm for other forms of racket sports might confirm and establish the effectiveness as well as the general applicability of this approach which could subsequently transform the ways of coaching and training in several fields of sport. Finally, the concept of the CNN-LSTM is an advance in the field of sports analysis as the results can be valuable in furthering training and match strategies.