# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 2 Materials and Methods  
### Existing Shape Model  
The Shape model used in this article stems from prior research [25; 26; 27]. This Shape model is based on 901 of the most complete subjects (317 male and 484 female) from the Dutch subset of the WEAR/CAESAR database, and is filtered based on correct posture, complete mesh, and metadata completeness. The raw scans are processed by rigging to determine correspondence between reference points on both the scan and a reference model. This corrects for errors in posture, and normalizes all scans [27]. In this standardized pose, all points are analyzed in their positional differences compared to the average reference model. The resulting Shape model is based on linear interpolation on all 102.045 points in the mesh model. The database used includes subjects of all ages (18 to 97), statures, body types, and backgrounds, ranging from muscular subjects to obese samples. The model describes an almost full body model (torso, head, arms, and legs, but no hands or feet), but where needed, subsections of the data can be used to minimize errors from undesired areas. Thus, despite efforts of improved rigging, oftentimes, arms or legs are completely removed from the model, since these have the largest errors that could stray the analysis and conclusions. The shape model holds all the information required to predict the best matching (L\({}^{2}\) metric) size and shape of the body that would fit within the given specs or dimensions/parameters entered in the model. For example, providing a user's stature would provide the most average body shape from the model with exactly that stature [27]. In order to predict a random test subject's shape and parameters, the shape model receives a list of measurements or parameters as input, and then tries to predict the best matching size and shape of the subject to fit within these parameters. For this research, the programmed shape model is separated for female and male subjects in the dataset and is developed to return the predictions for 28 different pre-defined measurements [28] as a CSV list to a plain text file and a 3D model to an.stl file containing the same 102,045 points in a mesh, adjusted to this prediction. Although there is pure math behind the shape models, the input measures and outcomes are approximations in high-dimensional Hilbert space; to make these useful in practice models are used to transform these spaces into measurement approximations. Details on the algorithms, applications in industrial design and useful methodologies behind shape models are build up for rigid products like EEG headsets [22]. For example, when a subject is predicted based on their stature, gender, and age, the weight of the subject is unknown. In that case, the model takes the average, and thus, the most probable shape out of the range of possible postures. However, models need to be programmed to handle a specific set of input measurements, so no optional inputs are possible in practice. For this research, two prediction models are defined; one that uses four input parameters, i.e., age, gender, stature, and weight, and one that uses the same four parameters with an additional set of five measurements received from the Styku scan. These five are the most accurate measurements from the Styku scanner [1], which are also present in the shape model and feature the same definition: chest circumference, maximal hip circumference, minimal waist circumference, bicep circumference, and neck circumference. In theory, adding more inputs increases the accuracy of the model. However, consideration needs to be taken to prevent overfitting of the model, and whether measurements are defined in the exact same way as in the model [29]. Many of the measurements in Styku are based on features that are not part of the ISO standard [16]. Each additional parameter should increase the accuracy of the prediction a little, but the added benefit might be slim in comparison to the added effort.