Title
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.
Year
DOI
Venue
2012
10.1109/CISIS.2012.213
CISIS
Keywords
Field
DocType
nintendo wii remote,sports instruction,computer-based instructional system,real-time classification,pattern clustering,forehand swing,available computing,computer-based instructional systems,computing facilities,prototype system,motion sensors,internet facilities,sport,internet,motion recognition,internet facility,adaptive nature,overhand swing,certain type,tennis skill,sensors,tennis swing,signal normailisation,e-learning,sports movement,adaptive clustering,tennis swing classification,computer aided instruction,e-learning tennis instruction system,racket-handle like shape,backhand swing,artificial intelligence
Real time classification,Computer science,Software,Motion sensors,Cluster analysis,Multimedia,Backhand,Input device,Swing,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4673-1233-2
4
0.50
References 
Authors
6
3
Name
Order
Citations
PageRank
Kin Fun Li115833.92
Ana-Maria Sevcenco2133.24
Kosuke Takano34815.61