Data-based parametric biomechanical models for cyclic motions

John Rasmussen*, Morten Enemark Lund, Rasmus Plenge Waagepetersen

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)


We present a method to convert motion capture data and anthropometric statistics into parametric biomechanical models of cyclic motions, such as walking, cycling and running. The motivation is ease of modelling and the desire to make models prospective. We have developed a data processing pipeline, which precompiles a large amount of motion capture trials into a parametric model relying on the correlations between the input variables. The compilation converts optical motion capture data into anatomical joint angle variations and anatomical body dimensions. Finally, a quadratic programming method with a closed-form solution is developed to predict motion patterns meeting subject-specific requirements. The method is demonstrated on running models, and we conclude that the method can facilitate new uses of biomechanical models.

Original languageEnglish
Title of host publicationDHM 2020 - Proceedings of the 6th International Digital Human Modeling Symposium
EditorsLars Hanson, Dan Hogberg, Erik Brolin
Number of pages8
PublisherIOS Press
Publication date24 Aug 2020
ISBN (Electronic)9781614994398
Publication statusPublished - 24 Aug 2020
Event6th International Digital Human Modeling Symposium, DHM 2020 - Skovde, Online, Sweden
Duration: 31 Aug 20202 Sept 2020


Conference6th International Digital Human Modeling Symposium, DHM 2020
CitySkovde, Online
SponsorESI Group, Industrial Path Solutions (IPS), University of Skovde, Volvo Car Corporation
SeriesAdvances in Transdisciplinary Engineering


  • Anthropometry
  • Principal Component Analysis
  • Running
  • Statistics


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