# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 5 Conclusions  
The fit of cycling apparel depends on desired properties such as comfort and aerodynamics. However, when a fitting moment with all clothing sizes is not feasible or desired, the customer should be able to determine their optimal size without having to fit the specific clothing or use measuring tools and size charts. Therefore, this research project aimed to analyze and compare various solutions to predict a subject's body shape and size for sports garment applications, based on either manual measuring or shape models, in order to validate the use of 3D anthropometric shape models and 1D regression statistics. Advantages of using the shape models are clear, in that the full parameters and details can be used to shape the garment. However, the downsides are also clear, in that the processing of 3D data will always be much more time consuming than analyzing pure 1D measurements through regression analysis.  
Therefore, the aim to generate an easy-to-process regression-based model that is accurate enough to predict the users' sports garment is achieved. The regression model with four base parameters, i.e., age, weight, stature, and gender, achieves comparable results on multiple measurements (conveniently on those that are relevant for the sports garment), and is only surpassed by an elaborate shape model method with additional data from a full body 3D scan. The simplicity of not needing to take a physical measurement is a significant advantage, and combined with the fact that the regression models take next to no processing power to analyze, this makes this method the way to go if the data are available and accurate for the needed application.  
Especially the need for a 3D scan would be cumbersome, since this already provides all the needed measurements itself. Additionally, the time needed to take the scan could just as well be used to fit the sample garments, which would provide an even better store experience and customer satisfaction.  
Specific care should be taken to verify if all needed measurements can accurately be predicted for all populations. For females specifically, for example, extra information is a great advantage in the accurate prediction of their body proportions. For other applications, new parameters might need to be added, such as fitness or body proportion, to further improve predictions.  
J.V.: writing--original draft preparation, data curation, writing--review and editing, visualization and validation; L.V.: writing--review and editing and visualization; T.P.: Conceptualization, writing--original draft preparation, methodology, investigation and visualization; T.H.: supervision, resources and data curation; F.D.: resources and software; S.V.: supervision, writing--review and editing, project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by IWT-Flanders, Belgium [Virtual size, 2016].  
This study was approved by the Ethics Committee of UZA/UA (17/30/345, 2017).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This study was partially performed using commercially available data from the Wear dataset (formerly known as the Caesar project (1999) [15; 17].  
This research was performed with support from Bioracer.  
The authors declare no conflict of interest.