IoT for Next-Generation Racket Sports Training

Abstract

We propose an IoT framework for next-generation racket sports training. To validate its performance, a wireless wearable sensing device (WSD) based on MEMS (microelectromechanical systems) motion sensors was used to recognize different badminton strokes and classify skill levels from different badminton players. The system includes a customized sensor node for data collection, a mobile app and a cloud-based data processing unit. The WSD developed is low-cost, easy-to-use and computationally efficient compared to video-based methods for analyzing badminton strokes. It offers the advantage of dynamic monitoring of multiple players in indoor and outdoor environments. In this paper we present the hardware design, mobile software implementation, and data processing algorithms of the system. Twelve right-handed male subjects wore the WSD on their wrists while each performed 30 trials of different strokes in a real badminton court. The results show that our system is capable of recognizing three different actions, i.e., smashes, clears and drops, with an accuracy rate of 97%. The skill assessment function can differentiate between professional, sub-elite, and amateur players from their stroke performance. This IoT framework aims to change the way of racket sports training from experience-driven (subjective) to data-driven (objective), and which can be easily extended to analyze the motions and skill levels of players in other racket sports (e.g., tennis, table tennis and squash) for training and/or practice.