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Force feasible set prediction with artificial neural network and musculoskeletal model

Author

Listed:
  • Vincent Hernandez
  • Nasser Rezzoug
  • Philippe Gorce
  • Gentiane Venture

Abstract

Developing tools to predict the force capabilities of the human limbs through the Force Feasible Set (FFS) may be of great interest for robotic assisted rehabilitation and digital human modelling for ergonomics. Indeed, it could help to refine rehabilitation programs for active participation during exercise therapy and to prevent musculoskeletal disorders. In this framework, the purpose of this study is to use artificial neural networks (ANN) to predict the FFS of the upper-limb based on joint centre Cartesian positions and anthropometric data. Seventeen right upper-limb musculoskeletal models based on individual anthropometric data are created. For each musculoskeletal model, the FFS is computed for 8428 different postures. For any combination of force direction and joint positions, ANNs can predict the FFS with high values of coefficient of determination (R2 > 0.89) between the true and predicted data.

Suggested Citation

  • Vincent Hernandez & Nasser Rezzoug & Philippe Gorce & Gentiane Venture, 2018. "Force feasible set prediction with artificial neural network and musculoskeletal model," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 21(14), pages 740-749, October.
    • Handle: RePEc:taf:gcmbxx:v:21:y:2018:i:14:p:740-749
    DOI: 10.1080/10255842.2018.1516763
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