利用虚拟球拍收集和分类网球挥拍

A. Sevcenco, K. F. Li, Kosuke Takano
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引用次数: 5

摘要

由于人工智能和决策支持的进步,计算机化学习系统最近很受欢迎。使用计算机学习体育运动是一个新的研究领域,但它需要在运动传感、建模和数据挖掘领域付出额外的努力。我们正在设计一个网球电子学习系统,使用任天堂Wii遥控器作为练习挥拍的虚拟球拍。本工作介绍了摆动运动数据的采集过程。使用主成分分析和k均值聚类等各种技术探索摆动数据的分类。从图形数据可以明显看出,不同类型的网球挥拍在三维空间中具有不同的特征。不同的包络形状的波动可以表征和区分使用描述性统计。分类结果强调了网球学习者挥拍动作的一致性和挥拍动作的相似性,这在最终的学习过程中很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collection and Classification of Tennis Swings Using a Virtual Racket
Computerized learning systems are popular these days due to the advances in artificial intelligence and decision support. Learning sports using a computer is a new field of research but it requires additional effort in the areas of motion sensing and modeling, and data mining. We are designing a tennis e-learning system using the Nintendo Wii remote as a virtual racket for practicing swings. This work introduces the swing motion data collection process. Classification of the swing data is explored using various techniques such as principal component analysis and K-means clustering. It is evident from the graphical data that different types of tennis swings have dissimilar characteristics in the 3-D space. The distinct envelope shape of the swings can be characterized and differentiated using descriptive statistics. Classification results are presented with emphasis on the swing consistency of tennis learners as well as the similarity of the swing motions which are important in the eventual learning process.
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