AI and Statistical Theory–Driven Wearable System for Intelligent Recognition of Professional Table Tennis Skills

Abstract

In traditional table tennis instruction, beginners typically rely on professional coaches to guide their motor skills, a process that depends on experience and is time‐consuming and labor‐intensive. Addressing this issue, this paper proposes a sensor‐based wearable system for automatically monitoring and identifying six table tennis motor skills. The system employs an embedded platform equipped with inertial sensors to collect multidimensional data from the athlete′s wrist during gameplay. Feature engineering and principal component analysis (PCA) are applied to preprocess and extract features from the raw data, effectively reducing dimensionality while preserving critical information. For skill recognition, an improved support vector machine (SVM) model is proposed. Its performance is compared against traditional convolutional neural network (CNN) models. Experimental results demonstrate the following: (1) The designed table tennis player skill monitoring system effectively captures in‐game data and enables athlete health monitoring; (2) the proposed improved SVM model demonstrates outstanding performance in technical skill recognition, achieving an average recognition accuracy of 97.77%. This represents a 5.07% improvement over the 92.70% accuracy of traditional CNN models, enabling more precise identification of various table tennis skills. The successful development of this system provides an effective data support and analysis tool for scientific athlete training and technical enhancement.