Musculoskeletal modeling of user groups for virtual product and process development

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 22; no. 15; pp. 1209 - 1218
• Main Author: Miehling, Jörg
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 18.11.2019; Taylor & Francis Ltd
• Subjects: action force; Anthropometry; Biomechanical engineering; Biomechanics; Digital human model population; Domains; Mapping; Materials handling; Model matching; Modelling; musculoskeletal modeling; population data; Strength; strength adaption; Three dimensional models; User groups; musculoskeletal modeling; Digital human model population; strength adaption; population data; action force
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2019.1651296

Although biomechanical digital human models find their way into virtual engineering processes, biomechanical considerations are currently still unrecognized to a large extent. One major obstacle lies in the fact that even though subject-specific modeling procedures are developed, virtual user groups or populations are still missing. The objective of this contribution is to create such groups of musculoskeletal models. Therefore, a modeling procedure based upon population data is described. First of all, two generic three-dimensional models, one with female and one with male average anthropometric dimensions, were obtained. These models constitute the starting point for the following model adjustment phases. Evenly distributed dimensionless values for gender, age, height, mass, range of motion and strength are sampled and translated into more expressive parameters for the mentioned modeling domains serving as input data for the creation of each individual model of the desired population or user group. The most sophisticated step of the adaption is the strength mapping aiming to create models matching arbitrary target joint torques. Finally, the models' maximal strength is assessed in a manual material handling task and compared to empirical strength data. The approach is shown using the example of the German population.