# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 3 Results
### Step 2: Selection of Input Parameters for Models  
To select the optimal measures, a prior research project [37] performed on the Wear dataset showed that the most important descriptors are, in order of accuracy of prediction: stature, weight, hip circumference, and gender with an improved accuracy with each added measure. For this research, the aim was to use those descriptive measures that most users know by heart. Thus, the stature, weight, and gender are used together with age. To compare the offset with added measures, an additional analysis was performed on as much input data as could be gathered in a method that could be commercially used. To that end, a Styku scanner was used that provides seven additional measurements on top of the four known features.  
In Figure 4, a comparison is made between both shape model prediction methods. The first uses only the base parameters and the second includes five additional measurements from the Styku scanner on top of the base parameters. The graph shows the relative error in comparison to each other as well as the 95% confidence interval. Visible is that the additional Styku data do provide more accurate predictions on most of the measurements, with some exceptions, i.e., the neck circumference and the acronium shoulder breadth. Some improve by a lot with these additional data, since these measurements are the same as those that are predicted by the model. However, issues might come from overfitting, whereby the shape model needs to make weird shapes to fit through the given inputs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} Male chest \\ circumference \\ \end{tabular} } & \multirow{2}{*}{=} & \(84.630845\times\text{log}(\text{age})\) & \\  & & \(-0.028169\times\text{weight}^{2}\) & \(+14.480524\times\text{weigh}\) & \(-647.094102\times\text{log}(\text{weight})\) \\  & & \(-0.000204\times\text{stature}^{2}\) & \(+2053.400368\times\text{log}(\text{stature})\) & \\  & & \(-4895.536588\) & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Male hip \\ circumference \\ \end{tabular} } & \multirow{2}{*}{=} & \(0.072907\times\text{age}^{2}\) & \(-10.385996\times\text{age}\) & \(+350.015534\times\text{log}(\text{age})\) \\  & & \(+0.037967\times\text{weight}^{2}\) & \(-8.498566\times\text{weight}\) & \(+1363.200158\times\text{log}(\text{weight})\) \\  & & \(-0000.301\times\text{stature}^{2}\) & & \\  & & \(-1311.519809\) & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Female chest \\ circumference \\ \end{tabular} } & \multirow{2}{*}{=} & \(0.016543\times\text{age}^{2}\) & \(-43.189966\times\text{log}(\text{age})\) & \\  & & \(-0.041868\times\text{weight}^{2}\) & \(+16.94243\times\text{weight}\) & \(-551.488704\times\text{log}(\text{weight})\) \\  & & \(-0000.8065\times\text{stature}^{2}\) & & \\  & & \(+1285.248759\) & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Female hip \\ circumference \\ \end{tabular} } & \multirow{2}{*}{=} & \(-0.339797\times\text{age}\) & \multirow{2}{*}{\(-0.23598\times\text{weight}^{2}\)} & \multirow{2}{*}{\(+0.23598\times\text{weight}^{2}\)} & \multirow{2}{*}{\(-4.287962\times\text{weight}\)} & \multirow{2}{*}{\(+1221.98981\times\text{log}(\text{weight})\)} \\  & & \(-0000.5088\times\text{stature}^{2}\) & & \\ \cline{1-1}  & & \(-855.63836\) & & \\ \hline \hline \end{tabular}  
Units: Weight in kg, age in years, stature in mm, and results in mm.  
\end{table}
Table 1: Non-linear regression formulae to predict chest and hip circumferences for male and female subjects.