Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model

Rahid Zaman, Yujiang Xiang, Ritwik Rakshit, James Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities. Methods: First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out. Results: Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model. Conclusion: The proposed hybrid model combines the skeletal models rapid motion prediction with OpenSims complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.

Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
DOIs
StateAccepted/In press - 2021

Keywords

  • Analytical models
  • Biological system modeling
  • Computational modeling
  • Hybrid model
  • Load modeling
  • Muscles
  • Musculoskeletal system
  • OpenSim
  • Predictive models
  • lifting
  • motion prediction
  • musculoskeletal model
  • skeletal model

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