Pose-Based Action Recognition in Tennis using MediaPipe and LSTM - Walid Hanif Ataullah; Isa Mulia Insan; Sheina Fathur Rahman (IJoICT (International Journal on Information and Communication Technology), 2025)
Pose recognition in tennis is an essential aspect for analyzing playing techniques and evaluating athlete performance. This study develops a tennis pose recognition system that integrates MediaPipe for pose feature extraction with Long Short-Term Memory (LSTM) networks for movement classification. The research dataset consists of 2,010 images of tennis movements across four categories: backhand, forehand, ready position, and serve, annotated in COCO format. MediaPipe successfully extracted pose landmarks from 1,782 images (88.7%), generating 33 pose landmarks flattened into a 99-dimensional feature vector. The LSTM model is designed with a 3-layer LSTM architecture and 2 dense layers, trained using a stratified train-test split with an 80:20 ratio. Model evaluation uses various metrics including accuracy, precision, recall, and F1-score. The results show that the system achieves 90.20% accuracy, with the best performance in the ready position category (F1-score: 91.28%) and the lowest in the forehand category (F1-score: 88.89%). The model demonstrates good computational efficiency with a memory footprint of 714.39 KB, enabling deployment on mobile devices. This study contributes to the development of automated sports analysis systems and demonstrates the feasibility of integrating MediaPipe-LSTM for real-time tennis pose recognition applications.