Collision detection and response approaches for computer muscle modelling

Ondřej Havlíček, M. Cervenka, J. Kohout
{"title":"Collision detection and response approaches for computer muscle modelling","authors":"Ondřej Havlíček, M. Cervenka, J. Kohout","doi":"10.1109/Informatics57926.2022.10083500","DOIUrl":null,"url":null,"abstract":"Computer muscle modelling is used for many pur-poses, from injury recovery and treatment of chronic diseases to disease prediction. These predictions often involve computing the muscle's internal forces to determine further how fast something may happen (e.g. how quickly the muscle joint wears out). During the simulation of such a model, collisions of soft and rigid bodies inevitably occur. This paper tests various state-of-the-art collision handling methods: voxelisation, one using Signed Distance Fields and one based on Bounding Volume Hierarchies. These methods are tested in the context of muscle modelling with the previously proposed position-based dynamics approach. Compared to the other options, using the Discregrid library for Signed Distance Field generation shows the best results, mainly due to its accuracy to the speed of execution ratio. In contrast to the current system, visually pleasant improvements are significant.","PeriodicalId":101488,"journal":{"name":"2022 IEEE 16th International Scientific Conference on Informatics (Informatics)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 16th International Scientific Conference on Informatics (Informatics)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Informatics57926.2022.10083500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Computer muscle modelling is used for many pur-poses, from injury recovery and treatment of chronic diseases to disease prediction. These predictions often involve computing the muscle's internal forces to determine further how fast something may happen (e.g. how quickly the muscle joint wears out). During the simulation of such a model, collisions of soft and rigid bodies inevitably occur. This paper tests various state-of-the-art collision handling methods: voxelisation, one using Signed Distance Fields and one based on Bounding Volume Hierarchies. These methods are tested in the context of muscle modelling with the previously proposed position-based dynamics approach. Compared to the other options, using the Discregrid library for Signed Distance Field generation shows the best results, mainly due to its accuracy to the speed of execution ratio. In contrast to the current system, visually pleasant improvements are significant.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
计算机肌肉建模中的碰撞检测与响应方法
计算机肌肉建模用于许多目的,从损伤恢复和慢性疾病的治疗到疾病预测。这些预测通常涉及计算肌肉的内力,以进一步确定事情可能发生的速度(例如,肌肉关节磨损的速度)。在该模型的仿真过程中,不可避免地会发生软体和刚体的碰撞。本文测试了各种最先进的碰撞处理方法:体素化,一种使用Signed Distance Fields,另一种基于Bounding Volume Hierarchies。这些方法在肌肉建模的背景下与先前提出的基于位置的动力学方法进行了测试。与其他选项相比,使用Discregrid库生成Signed Distance Field显示出最好的结果,主要是由于它的准确性与执行速度的比率。与目前的系统相比,视觉愉悦的改进是显著的。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Software Engineers' Questions and Answers on Stack Exchange Collision detection and response approaches for computer muscle modelling Supervised learning data preprocessing for short-term traffic flow prediction A 1D CNN-based model for IoT anomaly detection using INT data Image steganography with using QR code
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1