# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 3 Results
### Step 4: Comparison Preferred Size versus Model and Chart Predictions  
To predict the correct garments based on the predictions from the previous sections, a comparison was performed between the preferred and handpicked clothing sizes, and the ruler measurements and size chart on the one hand, and the regression prediction on the other hand. Both values determined the selection that would be made from the Bioracer size chart [7] based on chest circumference for the shirt and maximal hip circumference for the shorts, as described in their instructions, see Figure 1. These selections were compared to the piece of clothing that the subjects picked themselves, as described above.  
Table 5 shows that this resulted in a larger prediction error than the original sizing charts given when compared to the handpicked choices. However, it was visible in the raw data that certain users are way off from the prediction as well as from the measured size. This means that personal preference does play a significant role in the prediction methodology, and should, therefore, be considered in a commercial system. In general, both the measurement-based selection and the predicted selection undersize compared to the preferences of the subjects. This became clear throughout the tests, since plenty of the test subjects stated that they would prefer not to wear such tight clothing. However, the bias of the test could come into play here, since the subjects were explained the goal of the research, and that it should be a sporty fit. Additionally, it a difference between the male and female results was noticeable. Men, on average, chose close to what the prediction and measurements picked, but women were further off on both methods, and thus, seemed to prefer looser shirts but tighter pants. This can also have to do with the prediction model overestimating the hip to chest size in female subjects.  
Figure 5: ICC values (higher is better) for all three methods in order for all measurements: Regression model (M, F, M + F), Base Shape model (M, F, M + F), and Extended Shape model (M, F, M + F).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Shirts**} & \multicolumn{2}{c}{**Shorts**} \\ \cline{2-5}  & **Handpicked vs.** & **Handpicked vs.** & **Handpicked vs.** & **Handpicked vs.** \\  & **Size Chart Selection** & **Predicted** & **Size Chart Selection** & **Predicted** \\ \hline \multirow{2}{*}{**Average error**} & \(-0.11\) sizes & \(-0.43\) sizes & \(-0.19\) sizes & \(-0.05\) sizes \\  & (0.27 \(\mid\) \(-1\)) & (\(-0.08\)\(\mid\) \(-1.27\)) & (\(-0.69\)\(\mid\) \(1\)) & (\(-0.62\)\(\mid\) \(1.27\)) \\ \hline \multirow{2}{*}{**Std**} & 0.94 & 1.07 & 1.05 & 1.27 \\  & (0.67 \(\mid\) 0.89) & (0.84 \(\mid\) 1.1) & (0.74 \(\mid\) 0.63) & (0.94 \(\mid\) 0.9) \\ \hline \multirow{2}{*}{**Absolute average**} & \(\pm 0.65\) sizes & \(\pm 0.81\) sizes & \(\pm 0.84\) sizes & \(\pm 0.97\) sizes \\  & (\(\pm 0.42\)\(\mid\)\(\pm 1.18\)) & (\(\pm 0.62\)\(\mid\)\(\pm 1.27\)) & (\(\pm 0.77\)\(\mid\)\(\pm 1\)) & (\(\pm 0.77\)\(\mid\)\(\pm 1.45\)) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline  & Combined results; (Male results \(\mid\) Female results); Positive values are a larger size predicted or measured then chosen.  
\end{table}
Table 5: Comparisons between the handpicked (preferred) garment sizes and the size chart selected ones, as well as between the handpicked and the predicted sizes based on the regression model for both shirts and shorts.