Muscle force prediction in OpenSIm using skeleton motion optimization results as input data

Rahid Zaman, Yujiang Xiang, Ritwik Rakshit, James Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper describes an integrated approach to predict human leg and spine muscle forces during lifting by integration of a predictive skeletal model with OpenSim. The two-dimensional (2D) skeletal lifting motion is first predicted by using an inverse dynamics optimization method. Then, the prediction outputs, including joint angle profiles, ground reaction forces, and center of pressure, are incorporated in OpenSim biomechanics software to analyze muscle forces for lifting. Therefore, the integrated approach has predictive capability on musculoskeletal level. By using this method, we can predict and analyze muscles forces for heavy weight lifting motion which is difficult to simulate directly using a 3D musculoskeletal model.

Original languageEnglish
Title of host publication39th Computers and Information in Engineering Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859179
DOIs
StatePublished - 2019
EventASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States
Duration: Aug 18 2019Aug 21 2019

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume1

Conference

ConferenceASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
CountryUnited States
CityAnaheim
Period08/18/1908/21/19

Keywords

  • Inverse dynamics optimization
  • Lifting
  • Motion prediction
  • Muscle force
  • OpenSim

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