A novel bio-kinematic encoder for human exercise representation and decomposition - Part 1: Indexing and modelling

Saiyi Li, T. Caelli, M. Ferraro, P. Pathirana
{"title":"A novel bio-kinematic encoder for human exercise representation and decomposition - Part 1: Indexing and modelling","authors":"Saiyi Li, T. Caelli, M. Ferraro, P. Pathirana","doi":"10.1109/ICCAIS.2013.6720524","DOIUrl":null,"url":null,"abstract":"Current bio-kinematic encoders use velocity, acceleration and angular information to encode human exercises. However, in exercise physiology there is a need to distinguish between the shape of the trajectory and its execution dynamics. In this paper we propose such a two-component model and explore how best to compute these components of an action. In particular, we show how a new spatial indexing scheme, derived directly from the underlying differential geometry of curves, provides robust estimates of the shape and dynamics compared to standard temporal indexing schemes.","PeriodicalId":347974,"journal":{"name":"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS.2013.6720524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Current bio-kinematic encoders use velocity, acceleration and angular information to encode human exercises. However, in exercise physiology there is a need to distinguish between the shape of the trajectory and its execution dynamics. In this paper we propose such a two-component model and explore how best to compute these components of an action. In particular, we show how a new spatial indexing scheme, derived directly from the underlying differential geometry of curves, provides robust estimates of the shape and dynamics compared to standard temporal indexing schemes.
一种用于人体运动表征和分解的新型生物运动学编码器-第1部分:索引和建模
目前的生物运动学编码器使用速度、加速度和角度信息对人体运动进行编码。然而,在运动生理学中,有必要区分运动轨迹的形状及其执行动力学。在本文中,我们提出了这样一个双组件模型,并探讨了如何最好地计算一个动作的这些组件。特别是,我们展示了一种新的空间索引方案,直接从曲线的底层微分几何推导,与标准的时间索引方案相比,如何提供对形状和动力学的稳健估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信