# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 3 Results
### Step 3: Comparison Manual Measurements versus Model Predictions  
Using these prediction methods, it would be possible to do a three-way comparison between the various 3D shape model predictions, the non-linear regression model predictions, and the manually taken measurements. However, in order to compare all, the manual measurements were taken as ground truth reference, since they are taken by trained researchers. In the following tables, data are shown as root mean square error, as well as relative error, and the inter correlation coefficient (ICC) shows whether the method is reliable and accurate enough to be effectively used. Table 2 shows a comparison of the non-linear regression model's results compared to the manually performed measurements on the 37 test subjects. This gave a clear insight that this method was significantly more accurate for male subjects than for female on all measurements, except for the under bust circumference. That one has a slightly better ICC (intraclass correlation coefficient). Based on the ICC for each prediction, only the chest and hip predictions were excellent and would provide sufficiently accurate predictions. Table 3 shows the comparison on the shape model predictions using the base parameters (age, gender, weight, and stature). What can be seen is that this model obtains excellent scores on the chest circumference, hip circumference, and circumference under bust, and would, thus, make accurate predictions. However, the difference between male and female \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Regression Model** & \multirow{2}{*}{**RMSE (mm)**} & \multirow{2}{*}{**Relative Error (\%)**} & \multirow{2}{*}{**ICC**} & \\ \cline{1-2} \cline{5-10} \multicolumn{1}{c}{**(Base Parameters)**} & & & & & & & \\ \hline  & **M** & **F** & **M+F** & **M** & **F** & **M + F** & **M** & **F** & **M + F** \\ \hline Chest circ. & 24.7 & 49.3 & 34.9 & \(-0.72\) & \(-2.65\) & \(-1.36\) & 0.95 & 0.84 & 0.90 \\ \hline Circ. under Bust & 104.5 & 108.2 & 105.8 & \(-11.0\) & \(-9.57\) & \(-10.5\) & 0.47 & 0.51 & 0.49 \\ \hline Waist circ. & 64.7 & 144.8 & 98.9 & \(-5.64\) & \(-14.1\) & \(-8.47\) & 0.74 & 0.43 & 0.56 \\ \hline Hip circ. & 29.1 & 49.5 & 37.2 & 1.62 & 2.00 & 1.75 & 0.90 & 0.81 & 0.87 \\ \hline Arm length & 127.9 & 86.4 & 115.7 & 24.85 & 16.79 & 22.16 & 0.12 & 0.09 & 0.13 \\ \hline Waist front length & 48.2 & 66.0 & 54.8 & \(-8.42\) & \(-12.5\) & \(-9.79\) & 0.41 & 0.05 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of results on the regression model prediction based on the base parameter inputs. Figure 4: Relative error and 95% confidence interval for both shape model prediction methods. subjects is greater here, showing that for female subjects only, the chest circumference is a usable correlated prediction. Table 4 shows the comparison on the shape model predictions with the base parameters and additional Styku measurements. This model scores best on a series of measurements: chest circumference, circumference under bust, waist circumference, and hip circumference. It is also capable of predicting all of these with high correlation for both male and female subjects. Only the arm length and waist front length predictions are still very poorly and show no correlation whatsoever. Figure 5 shows all ICC results combined; the minimal acceptable target is 0.75, although a higher score is better. It should be noted that the root means square error indicated that the shape model with the Styku scanner data was most optimal overall, but not for all measurements. For the waist front length, for some reason, the models based on base data only score much better for males, and thus, also for the combined data. Additionally, it is visible that in a lot of cases, the predictions for females score worse than the ones for males. Depending on the measurement, the results are still accurate, but just less ideal. Thus, considerations need to be taken depending on the target group of the customers population.