###### Abstract  
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 \(A\) (m\({}^{2}\)) 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 R\({}^{2}\) = 0.72). \(A\) can accurately be predicted using anthropometric data and joint angles from optical mocap (root mean square error (RMSE) = 0.037 m\({}^{2}\)) or inertial mocap (RMSE = 0.032 m\({}^{2}\)). 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. 3D shape modeling; aerodynamics; computational fluid dynamics; cycling; projected frontal area; inertial sensors; joint biomechanics; motion capture system +
Footnote †: journal: Applied Soft  
on models of cyclists and bicycles under various conditions to determine the optimum cyclist pose and selection of equipment and accessories [6; 7; 8; 9; 10] as well as the influence of aerodynamics during professional cycling races [11; 12; 13]. To use CFD in the field of cycling aerodynamics, two-dimensional (2D) or three-dimensional (3D) models of the cyclist and equipment are required. A 3D scanning device provides accurate models, but state-of-the-art scanning equipment is difficult to access for most athletes. Indirect methods to predict the 3D shape of a given human using select anthropometric data have been described in the literature [14; 15; 16; 17]. If the 3D model of a cyclist is available, directly or indirectly, but not in a desirable pose configuration, it is possible to re-pose the model using animation techniques [18; 19]. In this study, we used 3D models obtained from an algorithm [16; 17] and re-posed them to various pose configurations to investigate if the cyclist aerodynamic drag could be predicted by a combination of anthropometric data and CFD analyses. Aerodynamic drag force F (N) on an object moving through a fluid is given by  
\[\mathrm{F}=0.5\cdot\mathrm{C_{D}}\cdot\rho\cdot\mathrm{v^{2}}\cdot\mathrm{A}, \tag{1}\]  
where \(\mathrm{C_{D}}\) is the drag coefficient, a dimensionless quantity that is a function of Reynold's number, Mach number, the form drag and skin friction of the object (\(\mathrm{C_{D}}\) is generally found from experiment); \(\rho\) is the air density at a given pressure and temperature (kg/m\({}^{3}\)); \(\mathrm{v}\) is the velocity of air relative to the object (m/s); and \(\mathrm{A}\) is the projected frontal area of object (m\({}^{2}\)). Projected frontal area of the cyclist is the biggest factor influencing drag during cycling (up to 90%) [8] where the cyclist accounts for up to 70% and the rest is accounted for by the bicycle [20]. The importance of the projected frontal area as a key metric for cycling performance has been stressed in the literature, with several methods proposed for measurement and estimation including (digital) photogrammetry, planimetry, wind tunnel tests, and field tests [21; 22; 23; 24; 25]. The projected frontal area of a cyclist depends upon the shape and pose of the cyclist.