# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 3 Results
### Female-Specific Adaptations to Regression Model Predictions  
Based on this gathered knowledge, a final additional attempt was made to make the regression model more suited for the female subjects. Since chest circumference was debatably the most important measurement for selecting cycling jerseys, the distribution of their body shape and weight depends on the size of the hips and chest and could be inversely proportional to each other when maintaining the same base parameters (age, weight, and stature). Therefore, another measurement should be added to enlighten the prediction model on this balance between shapes. The most known measurement under female subjects could disputably be the bra size (and conveniently, this parameter was also included in the wear database). Crudely, this bra size consists of an under chest circumference and a difference measurement between under and over chest measurement [38]. This measurement would be a perfect fit, since it provides easy insight, as long as the customer is willing to share this personal information to the platform. Bra size charts do change from region to region, standard to standard, and brand to brand, so some effort would be needed to make this a completed solution; however, this analysis is performed as a proof of concept. In the WEAR database, bra sizes were recorded in EU-size standard. Therefore, the recorded bra size (circumference) was converted from cm to. And the cup size is based on the average conversion values, as defined in the EN 12302 [39], and can be seen in Table 6. Using these results in the formula below in Table 7.  
To validate the improvements, the male and female predictions were compared once more with the new formulae, see Table 8. However, since these data were never requested from the test subjects in the initial study, this analysis cannot be performed on our test subjects' data. The only available sample data are from the wear database, and thus, the same data as used to train the model itself. This was, therefore, tested on a random set of 400 subjects from the Dutch female subset of the wear database and was not comparable to the data in the previous charts that were obtained for real-life subjects. To be able to compare the accuracy to other subjects, the original male regression formulae was also tested again, this time on 400 Dutch male subjects. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cup-Size** & **AA** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline Correction Value (mm) & 11 mm & 13 mm & 15 mm & 17 mm & 19 mm & 21 mm & 23 mm & 25 mm & 27 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cup size to correction dimension between chest circumference and under bust circumference. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{3}{*}{Female chest circumference} & & 0.0007104 \(\times\) age\({}^{2}\) & \\  & & \(-0.0015879\)\(\times\) weight\({}^{2}\) & +0.7503453 \(\times\) weight \\  & & \(-0.0005683\)\(\times\) stature\({}^{2}\) & \\  & & +0.402006 \(\times\) bra\_size & +0.846651 \(\times\) cup\_size-correction \\  & & +22.1669827 & \\ \hline \multirow{3}{*}{Female hip circumference} & & \(-0.0272542\)\(\times\) age & \\  & & +0.0013592 \(\times\) weight\({}^{2}\) & +88.0784348 \(\times\) log(weight) \\ \cline{1-1}  & & \(-0.00163\)\(\times\) stature\({}^{2}\) & +138.4747899 \(\times\) log(stature) \\ \cline{1-1}  & & \(-0.097014\)\(\times\) bra\_size & \(-0.0113216\)\(\times\) cup\_size-correction \\ \cline{1-1}  & & \(-452.389437\) & \\ \hline \hline \end{tabular}  
* Units: Weight in kg, age in years, bra size and cup size in mm, stature in mm, and results in mm.  
\end{table} 
Table 7: Non-linear regression formulae to predict chest and hip circumferences for female subjects, whereby female prediction also uses bra size information.