Real-Time Classification of Sports Movement Using Adaptive Clustering
Real-Time Classification of Sports Movement Using Adaptive Clustering
复制标题
使用自适应聚类对体育运动进行实时分类
DOI：
10.1109/cisis.2012.213
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
and Kosuke Takano
中科院分区：
文献类型：
--
作者：
Kin Fun Li;Sevcenco Ana-Maria;and Kosuke Takano
基于计算机的教学系统为学习某些类型的运动提供了理想的环境。特别是，需要优质空间的体育项目可以利用广泛可用的计算和互联网设施，随时随地教授个人用户。目前正在设计和开发一个电子学习网球教学系统。任天堂Wii遥控器因其低成本和球拍手柄形状而被选为输入设备。来自运动传感器的数据被捕获后，必须对它们进行清理、归一化、聚类和分类。从50名不同网球技术水平的人那里记录了反手、正手和上手三种常见挥杆的数据。进行了实验以确定最适合对网球挥杆进行分类的技术。并介绍了原型系统的自适应特性。
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.