Real-Time Classification of Sports Movement Using Adaptive Clustering

Abstract

Computer-based instructional systems provide an ideal setting for learning certain types of sports. In particular, the sports that require premium space could leverage the widely available computing and Internet facilities to teach individual users anywhere and anytime. An e-learning tennis instruction system is currently being designed and developed. The Nintendo Wii Remote is selected as the input device for its low cost and racket-handle like shape. After the data from motion sensors are captured, they have to be cleansed, normalised clustered and classified. Data of three common swings, backhand, forehand, and overhand, have been recorded from fifty people of various levels of tennis skill. Experiments are carried out to identify the most suitable techniques to classify a tennis swing. The adaptive nature of a prototype system is also introduced.