# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
###### Abstract  
As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfactory, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures--parameters that do not require manual measuring because they are readily known by heart by almost any customer--to predict users' body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences. s 26 August 20265 October 2  
adjusted per area of the body and preference of the end user. As an example, the pressure should be lower on the chest and neck area, since this may hinder breathing. However, on the arms and legs, a higher pressure can be helpful to streamline the cyclist, improve posture, and, thus, minimize drag. Currently, when buying new outfits of the Bjoracer brand assortment, amateur cycling teams request a sample set of clothing sizes so that each customer of the team can fit all sizes [1]. In practice, this is often done by hosting one or multiple fitting moments, during which the entire team comes together for test fits. Then, everyone decides what to buy through a personalized online store page (i.e., personalized for the team), or the team lead collects all sizes and places the group order. Consequently, all team members will receive clothing with the same graphics and of the same type, but in their individual sizes. This method makes sure each customer will receive clothing in the correct size; however, this comes with a logistical expense and is not viable in all circumstances, nor is it useful for individual customers. Given the increase in online shopping and the potential of digital solutions, a new method is needed to keep up with the market [3,4]. In practice, the only commercial variant known to the authors is the method of Bivolino [5], which focuses on blouses using a larger set of input parameters to determine clothing size. In the case study within this article, the clothing is customized for specific cycling teams, and therefore, it is impossible to return the clothing items to the manufacturer without the cost of remaking them. Unfortunately, customers that are unable to attend the fitting session must often decide based on previous knowledge of their optimal size, accept the risk of buying badly fitting garments, or take inaccurate measurements of their body. For this latter purpose, many garment brands provide a lookup table on their online store [6; 7; 8], whereby the subject should measure themselves at a couple of points (e.g., chest, waist, and hip circumference) and then approximate their correct size. However, some customers assume they know their sizes based on confection clothing and tend to not use this table correctly. Moreover, this table often varies per clothing piece, style, and type.