{"title":"Different approaches of conducting ergonomic assessment utilizing digital\n human models and motion capture in industrial site assembly","authors":"Clara Fischer, Pat Rupprecht, S. Schlund","doi":"10.54941/ahfe1002854","DOIUrl":null,"url":null,"abstract":"The further development of Industry 4.0 to 5.0 focuses even more on\n human-centred and sustainable production. The ergonomic factor plays a major\n role, as it is crucial for the well-being and productivity of workers and\n should already be considered in production planning. One of the most common\n ergonomic analysis methods is the “Ergonomic As-sessment Worksheet (EAWS)”,\n which is based on a paper & paper method for assessing human working\n posture. Currently, there are various approaches to automate this\n evalua-tion process with the help of digital human models or motion capture\n systems. All of these methods have their pros and cons; however, companies\n are faced with the problem of finding the best suited method for their\n processes. This paper compares three different methods to conduct an EAWS\n study for industrial site assembly in terms of methodology, effort, and\n efficiency. For this purpose, an evaluation of the physical movement with\n the original manual paper and pencil method was created and a generic\n movement with a digital human model was implemented and automatically\n evaluated. Furthermore, using motion capture, the automatic recording of\n physical movement data was carried out, which was computer-assisted\n evaluated using digital human models. To exclude software-specific\n inconsistencies, we used two different process simulation tools. As a final\n result, this paper shows a comparison of different implementation\n possibilities of the EAWS anal-yses and indicates the effort and efficiency\n for their use in industry. Furthermore, this initial analysis provides an\n opportunity for further research on digital human models and motion capture.","PeriodicalId":269162,"journal":{"name":"Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54941/ahfe1002854","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The further development of Industry 4.0 to 5.0 focuses even more on
human-centred and sustainable production. The ergonomic factor plays a major
role, as it is crucial for the well-being and productivity of workers and
should already be considered in production planning. One of the most common
ergonomic analysis methods is the “Ergonomic As-sessment Worksheet (EAWS)”,
which is based on a paper & paper method for assessing human working
posture. Currently, there are various approaches to automate this
evalua-tion process with the help of digital human models or motion capture
systems. All of these methods have their pros and cons; however, companies
are faced with the problem of finding the best suited method for their
processes. This paper compares three different methods to conduct an EAWS
study for industrial site assembly in terms of methodology, effort, and
efficiency. For this purpose, an evaluation of the physical movement with
the original manual paper and pencil method was created and a generic
movement with a digital human model was implemented and automatically
evaluated. Furthermore, using motion capture, the automatic recording of
physical movement data was carried out, which was computer-assisted
evaluated using digital human models. To exclude software-specific
inconsistencies, we used two different process simulation tools. As a final
result, this paper shows a comparison of different implementation
possibilities of the EAWS anal-yses and indicates the effort and efficiency
for their use in industry. Furthermore, this initial analysis provides an
opportunity for further research on digital human models and motion capture.