E. Brolin, Niclas Delfs, Martin Rebas, T. Karlsson, L. Hanson, D. Högberg
{"title":"基于人体形状数据的数字人体模型的发展","authors":"E. Brolin, Niclas Delfs, Martin Rebas, T. Karlsson, L. Hanson, D. Högberg","doi":"10.17077/dhm.31759","DOIUrl":null,"url":null,"abstract":"This paper presents the development of body shape data based digital human models, i.e. manikins, for ergonomics simulations. In Digital human modelling (DHM) tools it is important that the generated manikin models are accurate and representative for different body sizes and shapes as well as being able to scale and move during motion simulations. The developed DHM models described in this paper are based on body scan data from the CAESAR anthropometric survey. The described development process consists of six steps and includes alignment of body scans, fitting of template mesh through homologous body modelling, statistical prediction of body shape, joint centre prediction, adjustment of posture to T-pose and finally generation of relation between predicted mesh and manikin mesh. The implemented method can be used to create any type of manikin size that directly can be used in a simulation. To evaluate the results a comparison was done of original body scans and statistically predicted meshes generated in an intermediary step as well as the resulting DHM manikins. The accuracy of the statistically predicted meshes are relatively good even though differences can be seen, mostly related to postural differences and differences around smaller areas with distinct shapes. The biggest differences between the final manikin models and the original scans can be found in the shoulder and abdominal area, in addition to the significantly different initial posture that the manikin models have. To further improve and evaluate the generated manikin models additional body scan data sets that includes more diverse postures would be useful. DHM tool functionality could also be improved to enable evaluation of the accuracy of the generated manikin models, possibly resulting in DHM tools more compliant with standard documents. At the same time standard documents might need to be updated in some aspects to include more three-dimensional accuracy analysis.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of body shape data based digital human models for ergonomics simulations\",\"authors\":\"E. Brolin, Niclas Delfs, Martin Rebas, T. Karlsson, L. Hanson, D. Högberg\",\"doi\":\"10.17077/dhm.31759\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the development of body shape data based digital human models, i.e. manikins, for ergonomics simulations. In Digital human modelling (DHM) tools it is important that the generated manikin models are accurate and representative for different body sizes and shapes as well as being able to scale and move during motion simulations. The developed DHM models described in this paper are based on body scan data from the CAESAR anthropometric survey. The described development process consists of six steps and includes alignment of body scans, fitting of template mesh through homologous body modelling, statistical prediction of body shape, joint centre prediction, adjustment of posture to T-pose and finally generation of relation between predicted mesh and manikin mesh. The implemented method can be used to create any type of manikin size that directly can be used in a simulation. To evaluate the results a comparison was done of original body scans and statistically predicted meshes generated in an intermediary step as well as the resulting DHM manikins. The accuracy of the statistically predicted meshes are relatively good even though differences can be seen, mostly related to postural differences and differences around smaller areas with distinct shapes. The biggest differences between the final manikin models and the original scans can be found in the shoulder and abdominal area, in addition to the significantly different initial posture that the manikin models have. To further improve and evaluate the generated manikin models additional body scan data sets that includes more diverse postures would be useful. DHM tool functionality could also be improved to enable evaluation of the accuracy of the generated manikin models, possibly resulting in DHM tools more compliant with standard documents. At the same time standard documents might need to be updated in some aspects to include more three-dimensional accuracy analysis.\",\"PeriodicalId\":111717,\"journal\":{\"name\":\"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17077/dhm.31759\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17077/dhm.31759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of body shape data based digital human models for ergonomics simulations
This paper presents the development of body shape data based digital human models, i.e. manikins, for ergonomics simulations. In Digital human modelling (DHM) tools it is important that the generated manikin models are accurate and representative for different body sizes and shapes as well as being able to scale and move during motion simulations. The developed DHM models described in this paper are based on body scan data from the CAESAR anthropometric survey. The described development process consists of six steps and includes alignment of body scans, fitting of template mesh through homologous body modelling, statistical prediction of body shape, joint centre prediction, adjustment of posture to T-pose and finally generation of relation between predicted mesh and manikin mesh. The implemented method can be used to create any type of manikin size that directly can be used in a simulation. To evaluate the results a comparison was done of original body scans and statistically predicted meshes generated in an intermediary step as well as the resulting DHM manikins. The accuracy of the statistically predicted meshes are relatively good even though differences can be seen, mostly related to postural differences and differences around smaller areas with distinct shapes. The biggest differences between the final manikin models and the original scans can be found in the shoulder and abdominal area, in addition to the significantly different initial posture that the manikin models have. To further improve and evaluate the generated manikin models additional body scan data sets that includes more diverse postures would be useful. DHM tool functionality could also be improved to enable evaluation of the accuracy of the generated manikin models, possibly resulting in DHM tools more compliant with standard documents. At the same time standard documents might need to be updated in some aspects to include more three-dimensional accuracy analysis.