# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
###### Abstract
[11] employed an RBF-NN to estimate complex parameters to improve the comfort and adaptability needed for sportswear. The generalized regression neural network, of [PERSON] et al. [2], utilizes a particular RBF-NN, and demonstrated a high degree of accuracy in predicting 76 specific human body parameters, without the need for manual measurements or 3D body scanning. This model has shown remarkable predictive capabilities, in line with the growing trend of predicting human body dimensions rather than relying on direct measurements. Support vector regression (SVR) networks comprise state-of-the-art (SOTA) ANN models, but they still fall short in prediction accuracy and computation efficiency. However, SVR models are the most suitable for nonlinear regression problems in which only small-batch samples are available, and they are robust to outliers. [PERSON] and [PERSON] [12] used an SVR network to construct a regression model for two-dimensional width and depth features and the corresponding circumference sizes of three important measurement parts (i.e., bust, waist, and hip) of young female samples. However, the establishment of this method relied on cross-sectional data of the human body and cannot avoid the need for 3D scanning. [PERSON] et al. [13] demonstrated the possibility of superior performance over traditional linear regression methods by employing an SVR model with a Gaussian kernel for the estimation of height and weight using anthropometric data alone. While the results of this method were said to be insensitive to race and gender, in practical application, there may be other unaccounted factors that challenge the model's robustness. [PERSON] et al. [14] introduced a data-driven model based on particle swarm optimization (PSO) to optimize the least-squares support vector machine (LSSVM) algorithm. This model was applied to solve the problems of garment style recognition and size prediction for pattern making. Tailoring experience was used as the training basis to improve prediction accuracy. However, the ambiguity of the relationship between the variables made the aforementioned single direct prediction model unsuitable for the fashion industry, due to the diversity of body shapes and precise body dimensions. Although the prediction results exhibit a certain level of accuracy, there is ample room for improvement. Furthermore, anthropometry found extensive applications in the field of intelligent clothing design. For instance, [PERSON] et al. [15] employed fuzzy logic and genetic algorithms to generate initial clothing patterns. They utilized SVR to learn about the quantitative relationships between clothing structural lines, control points, and pattern parameters. These relationships were then used to predict and adjust the pattern parameters, achieving pattern adaptability. [PERSON] et al. [16] proposed a machine learning framework that combines hybrid feature selection and a Bayesian search to estimate missing 3D body measurements, addressing the challenge of incomplete data in 3D body scanning. The study found that this approach leverages hybrid feature selection and the Bayesian search to enhance the performance of random forest (RF) and XGBoost 0.72, particularly in filling in missing data, where RF outperforms XGBoost. [PERSON] et al. [17] introduced an approach that utilizes multiple machine learning frameworks, including RBF-NN, GA, PNN (probabilistic neural network), and SVR, for interactive personalized clothing design. This method enhanced the capability of personalized clothing design by estimating body dimensions, generating customized design solutions, quantifying consumer preferences, predicting clothing fit, and self-adjusting design parameters. The global optimization of parameters can improve prediction accuracy and generalizability, and modernized meta-heuristic swarm intelligence optimization algorithms can simulate individual and group behaviors that are suitable for complex optimization problems. They also rapidly converge with fewer parameters, and are easily implemented [18].