Scientific Reports (Dec 2023)

Muscle-driven simulations and experimental data of cycling

  • Caitlin E. Clancy,
  • Anthony A. Gatti,
  • Carmichael F. Ong,
  • Monica R. Maly,
  • Scott L. Delp

DOI
https://doi.org/10.1038/s41598-023-47945-5
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 10

Abstract

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Abstract Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40–216 W) and cadences (75–99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling_sim for others to reproduce these results and build upon this research.