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ApOL-Application Oriented Workload Model for Digital Human Models for the Development of Human-Machine Systems

Sänger, Johannes ORCID iD icon 1; Wirth, Lukas 1; Yao, Zhejun; Scherb, David; Miehling, Jörg; Wartzack, Sandro; Weidner, Robert; Lindenmann, Andreas ORCID iD icon 1; Matthiesen, Sven 1
1 Institut für Produktentwicklung (IPEK), Karlsruher Institut für Technologie (KIT)

Abstract:

Since musculoskeletal disorders are one of the most common work-related diseases for assemblers and machine operators, it is crucial to find new ways to alleviate the physical load on workers. Support systems such as exoskeletons or handheld power tools are promising technology to reduce the physical load on the humans. The development of such systems requires consideration of the interactions between human and technical systems. The physical relief effect of the exoskeleton can be demonstrated in experimental studies or by simulation with the digital human model (DHM). For the digital development of these support systems, an application-oriented representation of the workload is necessary. To facilitate digital development, an application-oriented workload model (ApOL model) of an overhead working task is presented. The ApOL model determines the load (forces, torques) onto the DHM during an overhead screw-in task using a cordless screwdriver, based on experimental data. The ApOL model is verified by comparing the simulated results to the calculated values from a mathematical model, using experimental data from three participants. The comparison demonstrates successful verification, with a maximum relative mean-absolute-error (rMAE) of the relevant load components at 11.4%. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000164064
Veröffentlicht am 09.11.2023
Originalveröffentlichung
DOI: 10.3390/machines11090869
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktentwicklung (IPEK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2075-1702
KITopen-ID: 1000164064
Erschienen in Machines
Verlag MDPI
Band 11
Heft 9
Seiten Art.-Nr.: 869
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 29.08.2023
Nachgewiesen in Dimensions
Web of Science
Scopus
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