## 4 Discussion
### Projected Frontal Area Prediction Based on Anthropometrics and Joint Angles  
Furthermore, this study investigated the opportunities to predict the projected frontal area as an indicator of aerodynamic drag using several body dimensions and joint angles. Previous research showed the correlation between projected frontal area and several joint angles [38], which indicates the opportunities to link aerodynamic efficiency and mocap. The optical regression method uses 2D joint angles from pictures, added with scores of the parametric 3D body shapes and anthropometric measurements. The inertial regression method provides the same anthropometric data, added with more extensive 3D joint angles data from IMUs. The optical model uses 11 variables to predict the projected frontal area, where the inertial mocap model includes 17 variables. Regarding anthropometric data, the tight neck circumference and lower arm length are included in both regression models, whereas the upper arm length (optical model) and the neck circumference (inertial model) have the biggest influence on the projected frontal area. The change in body shape corresponding with principal component (or'mode') #6 has the biggest influence on predicting projected frontal area (beta coefficient 0.49), where other components were not included in the regression model. From Figure 9, mode 6 appears to be linked with the upper body girth and the width of the upper legs. Regarding joint angles, previous studies indicated that torso, shoulder, head, and elbow angle have the most considerable influence on aerodynamics [39; 40], which were all included in our models. The hip flexion angle had the highest beta coefficient for the optical model. However, the hip angle was excluded in the inertial mocap analysis due to moving of the strap during cycling, which causes unreliable data. This issue can be solved by using double-sided tape to attach the inertial sensor directly on the hip-joint level or using the sensor at the rear rather than front of the participant. It could be an improvement to use a similar method for the legs as well, since the straps should be really tightened to prevent from coming loose, which can be impeding or annoying for the participant during cycling. Including the back/chest angle, elbow flexion angle, and shoulder flexion angle in the regression models is obvious since these angles determine the position of the upper body. The higher these angles, the more upright the position and the higher the projected frontal area. In both regression models, joint angles are more used to predict the aerodynamics compared to anthropometric data, since they also consider the movement during cycling, where anthropometric data can only be used to predict the general influence on the projected frontal area independent of the cycling pose. The inertial regression model includes 3D joint angles, but the added value in predicting the projected frontal area is limited, since only the lateral tilt and rotation of the chest and rotation of the elbow and shoulder are included. To improve the accuracy of both models, analyzing regression models using relative values compared to the pose with the minimal projected frontal area as a reference pose was considered, but did not result in improved accuracy. Furthermore, logarithmic and exponential models can be used in future studies to optimize accuracy. Finally, combined effects of anthropometric data can be worth considering (e.g., multiplication of body height and weight can be an approach of the shape of the body). The accuracy of both models is equivalent, where the inertial mocap model has a slightly higher accuracy in general. The results of the cross-validation showed an RMSE of 0.037 m\({}^{2}\) for the optical model and 0.032 m\({}^{2}\) for the inertial mocap model. Each step in the process of predicting the projected frontal area induces possible errors. Anthropometric data are obtained by hand measurements by one researcher, which means that the error in this step is negligible.