Estimating cycling aerodynamic performance using anthropometric measures

Raman Garimella*, Thomas Peeters, Eduardo Parrilla, Jordi Uriel, Seppe Sels, Toon Huysmans, Stijn Verwulgen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
49 Downloads (Pure)


Aerodynamic drag force and projected frontal area (A) are commonly used indicators of aerodynamic cycling efficiency. This study investigated the accuracy of estimating these quantities using easy-to-acquire anthropometric and pose measures. In the first part, computational fluid dynamics (CFD) drag force calculations and A (m2) values from photogrammetry methods were compared using predicted 3D cycling models for 10 male amateur cyclists. The shape of the 3D models was predicted using anthropometric measures. Subsequently, the models were reposed from a standing to a cycling pose using joint angle data from an optical motion capture (mocap) system. In the second part, a linear regression analysis was performed to predict A using 26 anthropometric measures combined with joint angle data from two sources (optical and inertial mocap, separately). Drag calculations were strongly correlated with benchmark projected frontal area (coefficient of determination R2 = 0.72). A can accurately be predicted using anthropometric data and joint angles from optical mocap (root mean square error (RMSE) = 0.037 m2) or inertial mocap (RMSE = 0.032 m2). This study showed that aerodynamic efficiency can be predicted using anthropometric and joint angle data from commercially available, inexpensive posture tracking methods. The practical relevance for cyclists is to quantify and train posture during cycling for improving aerodynamic efficiency and hence performance.

Original languageEnglish
Article number8635
Pages (from-to)1-16
Number of pages16
JournalApplied Sciences (Switzerland)
Issue number23
Publication statusPublished - 2020


  • 3D shape modeling
  • Aerodynamics
  • Computational fluid dynamics
  • Cycling
  • Inertial sensors
  • Joint biomechanics
  • Motion capture system
  • Projected frontal area


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