## 2 Background and Methods
### Proposed Framework  
Figure 1 shows the proposed ML-enabled framework for the 3D body measurement estimation. First, the original data is loaded from the real-world 3D body measure dataset consisting of 212 participants in the US Midwest from the age of six to seventy-five. Institutional review board (IRB) approval was granted before the recruitment of participants started. Participants are asked to change into standardized scanning attire, which is a set of undergradments. Next, the researchers manually measure participant's standing height and weight, and then they are 3D body scanned in the a-frame scanning position [30] as shown in Figure 2. Second, a preprocessing stage is applied to remove outliers and normalize the dataset. The preprocessing stage mainly removes outliers and normalize the value of the dataset. An a-frame neutral posture in the anthropometric position is important in obtaining accurate measurements, but this posture in a 3D body scanner can occlude the sensors from reading some body areas such as the underarm, inner thighs, and crotch. The automatic detection of landmarks in those areas is difficult. Thus, the automatically extracted measurements at these locations can be outliers that need to be removed. Normalization in ML is the process of translating data into the range of [0, 1], which improves the performance and accuracy of the ML models using various techniques and algorithms. In this paper, the Interquartile Range (IQR) is used for removing outliers, and the Min-Max normalization is adopted to rescale the data into the range of [0, 1]. Those preprocessing steps are automatically run prior to the ML module. Then, the entire framework is initialized by importing required libraries or packages in the preprocessing stage. Next, the body measure dataset is split into a training subset and a testing subset by random sampling. A combination of all the hyperparameters is selected before the ML model starts. Here, we apply two ML models--RF and XGBoost--as the prediction techniques, which are the state-of-the-art ML-based regression models. In the training process, we enabled the Bayesian search algorithm as the optimization to find the best combination of hyperparameters for XGBoost and RF. It is worth mentioning that optimizing the combinations of hyperparameters is equivalent to solving a nonlinear optimization problem. This will be discussed in detail later. Then, feature importance is ranked and only those important features are selected to effectively reduce the dimensions of the input training dataset. Note that the proposed framework will move to the next body feature if the remaining body feature is in the dataset. Once the framework iterates overall body features, a validation step is established by calculating the Mean Absolute Percentage Error  
(MAPE) between 0.79% and 13.75% with the top 20 features of the highest frequency. The reason for selecting the top 20 features with the highest frequency will be demonstrated in Section 3 of this paper. In this way, only a subset of the body features are considered in the subsequent body measurement estimation to improve the training efficiency.