# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Model Prediction Performance and Garment Pattern Making  
Figure 6A,B depict the results of our qualitative analysis for verifying the effectiveness of our method, which seeks to demonstrate the efficacy of GRFN training on the prediction of crotch height and knee circumference. By correcting the residual errors in the training data, the best results pair \((C_{best},g_{best})\) of the SVR hyperparameter optimization method were (12.1940, 0.3538) and (12.5767, 0.1852), respectively. Notably, the GRFN model had a good fit between the measured and estimated values, according to the datasets used for training and testing. Table 7 displays the error between the estimated and ground-truth values. Among these results, the extreme of the maximum error in the testing set was 0.3720 cm, and that of the average error was 0.1182 cm, aligning with the GB/T 23698-2009 standard [52]. Compared with the results of [PERSON] et al. [6], who used a BPNN-based model, the mean square error (MSE) and standard error (SE) were 2.06 \(\pm\) 0.2. Using the PSO-LSSVM model, [PERSON] et al. [14] could only predict the sleeve sizes at MSE and SE measures of 1.057 \(\pm\) 0.06. As shown in Table 7, our proposed hybrid model predicts eight difficult-to-measure lower body sizes, where the total MSE and SE were 0.0054 \(\pm\) 0.07. Moreover, compared with the research conducted by [PERSON] et al. [2], which utilized a generalized RBF-NN regression model and yielded an average \(R^{2}\) value of 0.971 and an average \(\varepsilon_{RMSE}\) of 5.823 mm, ours achieved significantly improved results of 0.9997 and 0.6142 mm. The prediction effects from the incremental increase in the number of components/parts are displayed in Figure 7A-D. In summary, the hybrid model clearly improved prediction accuracy and generalizability. Figure 5: Comparison of MAE and RMSE with different input training samples; (**A**) crotch-height, (**B**) knee circumference. Figure 6: Predicted results of GRFN hybrid model; (**A**) crotch-height, (**B**) knee circumference. A subject's stature, waist circumference, hip circumference, waist height, and hip height vectors were 161.7, 68.9, 93.7, 100.3, and 83.5, respectively, as inputs, and the ground-truth outputs were 69.55, 43.7, 53.85, 77.4, 79.2, 37.4, 20.4, and 96.6 for crotch height, knee height, thigh circumference, total crotch length, abdomen circumference, knee circumference, crotch width, and abdomen height, respectively. The outputs predicted by the GRFN hybrid model were 69.559, 43.718, 53.804, 77.501, 79.101, 37.357, 20.397, and 96.310. These predictions are exceptionally accurate and can be used to design patterns for women's sports trousers and other lower-body garments. As shown in Figure 8A, a customer participant can input a few basic body parameters and accurately predict the relevant dimensions for pattern making via the GRFN. We also provide an interactive automatic pattern generation prototype, built using Microsoft Visual Studio 2013 and the Qt 5 integrated C++ development environment. Figure 8B depicts a screenshot of this system. During the pattern making process, a point-numbering method [53] is used to draw the structural lines of clothing patterns. The pattern for three \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Criteria** & **CH** & **KH** & **TC** & **TCL** & **AC** & **KC** & **CW** & **AH** \\ \hline \(e_{RMSE}\) (cm) & 0.1225 & 0.0254 & 0.0409 & 0.1289 & 0.0776 & 0.0395 & 0.0161 & 0.0405 \\ \(e_{MAE}\) (cm) & 0.0641 & 0.0228 & 0.0317 & 0.1182 & 0.0706 & 0.0383 & 0.0119 & 0.0319 \\ \(R^{2}\) & 0.9995 & 0.9999 & 0.9999 & 0.9998 & 0.9999 & 0.9997 & 0.9996 & 0.9999 \\ Mean Error/cm & 0.0641 & 0.0228 & 0.0017 & 0.1182 & 0.0706 & 0.0383 & 0.0118 & 0.0319 \\ Maximum Error/cm & 0.3720 & 0.0381 & 0.0054 & 0.2037 & 0.1018 & 0.0598 & 0.0419 & 0.0910 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Error between calculated and measured values.