## 2 Materials and Methods
### Procedure
#### 2.5.3 Regression Analyses  
The regression between the drag force of the 3D models and the projected area from the frontal camera is a simple least squares method to fit the data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Air Viscosity & 1.81 \(\times\) 10\({}^{-5}\) Pa-s \\ Inlet Velocity/ Air Speed U\({}_{\infty}\) (free stream) & 16.7 m/s = 60 km/h \\ Outlet Pressure & 0 Pa (static) \\ Temperature & 295.3 K \\ Relative Humidity & 39.9 \% \\ Atmospheric Pressure & 100,480 Pa \\ Saturation Vapor Pressure & 269,225.2 Pa \\ Air Density & 1.181 kg/m\({}^{3}\) \\ Turbulence Model & Shear Stress Transport (SST) k-\(\omega\) \\  & Polyhedral \\ Meshers & Surface Remesher \\  & Prism Layer Mesher \\ Base Cell Size & 0.15 m \\ Prism Layers & 5 \\ Reynolds number Re & 1.33 \(\times\) 10\({}^{6}\) \\ L (dimension of object) & \(<\)1 m \\ C, f (skin friction coefficient) & 3.46 \(\times\) 10\({}^{-3}\) \\ Wall Shear Stress \(\tau\) & 2.84 \(\times\) 10\({}^{-1}\) m/s \\ U\({}^{*}\) (dimensionless velocity) & 4.91 \(\times\) 10\({}^{-1}\) m/s \\ Y (first layer cell height) & 0.00281304 m \\ Y+ & 90 \\ \(\delta\) (prism layer height) & 0.039 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of parameters for computational fluid dynamics (CFD) analysis.  
For the second part, the projected frontal area and joint angles data were first synchronized using a hand raise at the beginning of the dynamic protocol. At this point, the projected frontal area reached a maximum, which was matched with a maximal back/chest and elbow flexion angle. Furthermore, the projected frontal area had a sample rate of 3 Hz, whereas it was 30 Hz for the optical mocap and 10 Hz for the inertial mocap. To align the data, average values of one-second intervals were used. One subject was excluded due to an error in projected frontal area calculation, resulting in a total of 914 data points in the final regression analyses.  
Two linear regression models were generated to predict the projected frontal area based on different input data, consisting of  
1. [label=(0)]
2. anthropometric data (Table 1), 2D joint angles from the optical mocap system (Table 1), and the weights of the first 20 principal components of body shape; and
3. anthropometric data and joint angles from the inertial mocap system (Table 1).  
The stepwise linear regression method (SPSS Statistics 27, IBM, Armonk, NY, USA) was used to define the optimal model. From the optimal model, the importance of each included variable in the equation was analyzed using standardized beta coefficients.  
To determine the accuracy of both regression models, the equation was cross-validated by subsequently leaving the data of one subject out to form a linear regression equation and applying these results to the excluded subject, repeating this procedure for each subject. The cross-validated prediction values were compared with the actual projected frontal area to determine the intraclass correlation coefficient (ICC) [31; 32]. The two-way-random ICC-model, type absolute agreement, was calculated for the entire dataset. Furthermore, the root mean square error (RMSE) (m\({}^{2}\)) projected frontal area between the prediction and actual values was calculated as well as the relative error of the predicted value compared to the actual projected frontal area.