# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 4 Discussion
### Future Opportunities  
Regarding future research opportunities, the used grid pattern on the test clothing of the experiment offers possibilities to map the stretch of the clothing in a visual, colored, and graphed way. At the moment, these grid lines on the clothing were added to easily measure the stretch at any given location using a small ruler (i.e., a measurement of 12 mm across two lines would indicate a stretch of 20% on the 10 mm grid). Given the available 3D scan information all around the body, all these data could be combined to analyze stretch in a colormap visualization on the subjects' 3D model. This could aid garment design and customers fitting sessions by visualizing the problem areas, and analyzing the severity of the problem. It can also aid the designers by achieving their desired fit, by showing the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Regression Model+** & \multicolumn{3}{c}{**RMSE (mm)**} & \multicolumn{3}{c}{**Relative Error (\%)**} & \multicolumn{3}{c}{**ICICC**} \\ \hline  & **M** & **F** & **F +** & **Bra Size** & **M** & **F** & **F +** & **F +** & **Bra Size** \\ \hline
**Chest circumference** & 40.64 & 46.15 & 38.65 & 0.2 & 0.21 & 0.15 & 0.92 & 0.91 & 0.94 \\ \hline
**Hip circumference** & 36.42 & 40.61 & 40.23 & 0.12 & 0.15 & 0.17 & 0.91 & 0.92 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overview of results on the regression model prediction based on the base parameters with the bra size information.  
users in a color scale what stretch is desired, and that loose fitting clothes is not advised for optimal performance.  
The following two setups can be used to translate the prediction options within this paper to a commercial setup. First, a system to enter personal (base) parameters in a system (application, website, or other). Second, a setup whereby stores would have a Styku scanner system that takes a set of standard measurements on top of those base parameters to improve accuracy. Conversely, in the future, any measurement system (smartphone, webcam, or custom setup) could measure the users body proportions (or even measurements if 3D scanners become implemented in more systems) and add to the model's prediction accuracy.  
The advantage in designing for stretch sports garments over confection apparel is the stretchy nature of the fabric. This provides a margin of error whereby the fit is not perfect, but the discomfort is acceptable. When compared to the research of [PERSON] [5], this research does not try to modify the garments to fit. Instead, it is aimed to select the optimal garment out of the confection line of garments as shown in the sizing chart of Figure 1. Thus, the degrees of freedom are constrained with less variability in the output. However, the input is also constrained given the limitation of inputs without manual measuring and using known to heart parameters.  
For this work, a comparison is made between shape model predictions and regression analysis. An alternative upcoming route is to go for machine learning and thereby allowing additional complexity in the prediction algorithms. However, as is the case with shape models, this also hides the actual dynamic between in- and outputs. The clean relation between the variables is considered a great benefit in real-life applications, since the abilities and limitations are visible.