## 4 Results
### Comparative Experiments and Ablation Studies
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **VAF** & **RMSE** & **R\({}^{2}\)** \\ \hline LSTM & 0.5257 \(\pm\) 0.0609 & 10.2672 \(\pm\) 1.6786 & 0.3216 \(\pm\) 0.0963 \\ TCN & 0.5069 \(\pm\) 0.0447 & 10.5900 \(\pm\) 1.9643 & 0.3640 \(\pm\) 0.0756 \\ BioMat & 0.5033 \(\pm\) 0.0825 & 10.4791 \(\pm\) 1.6053 & 0.3255 \(\pm\) 0.1331 \\ Model I & 0.5101 \(\pm\) 0.0430 & 11.5938 \(\pm\) 1.8149 & 0.3436 \(\pm\) 0.0700 \\ Model II & 0.5764 \(\pm\) 0.0392 & 10.6148 \(\pm\) 1.6914 & 0.4653 \(\pm\) 0.0676 \\ Model III & 0.5251 \(\pm\) 0.1269 & 9.1864 \(\pm\) 1.7192 & 0.3800 \(\pm\) 0.1662 \\
**Model IV** & **0.6485 \(\pm\) 0.0607** & **8.3952 \(\pm\) 1.7500** & **0.5470 \(\pm\) 0.0847** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance metrics—VAF, RMSE, and R\({}^{2}\)—for seven different models trained using the initial treadmill data from Subject 5. The models include LSTM, TCN, BioMat, Model I, Model II, Model III, and Model IV. The average values and standard deviations of these metrics, documented for six subsequent experiments, demonstrate that the MAE model (Model IV) surpassed all other models in performance across all evaluated metrics. model (Model I), the MAE model exhibited enhanced predictive capabilities, significantly boosting the performance of Model I with the aid of HFMESLoss. Model IV surpassed all other ablation models in all evaluation metrics. Additionally, this study also trained and tested other models (LSTM [44], TCN [47] and BioMat [46]) that predict biological signals from biological signals. The results, as displayed in Table 7, indicate that our proposed MAE model consistently outperformed all other models across all metrics. To visually demonstrate the performance of each model, we converted the metrics for all seven models across all subjects into box plots, as depicted in Figure 6. The results distinctly showcase the significant advantages of our innovative model, also affirming the feasibility of using small samples for predicting joint moments. This offers new insights for mechanical control and the study of transient changes in the field. \begin{table}
\begin{tabular}{l c c c} \hline
**Model** & **VAE** & **RMSE** & **R\({}^{2}\)** \\ \hline Model I & 0.5423 \(\pm\) 0.0311 & 12.089 \(\pm\) 3.1087 & 0.4075 \(\pm\) 0.0444 \\ Model II & 0.5996 \(\pm\) 0.0514 & 11.3454 \(\pm\) 2.4639 & 0.4745 \(\pm\) 0.0643 \\ Model III & 0.6074 \(\pm\) 0.0635 & 10.4601 \(\pm\) 2.6492 & 0.4719 \(\pm\) 0.0794 \\
**Model IV** & **0.6478 \(\pm\) 0.0549** & **9.7957 \(\pm\) 2.3446** & **0.5359 \(\pm\) 0.0838** \\ \hline \end{tabular}
\end{table} 
Table 6: The outcomes of ablation studies conducted on five subjects using Models I, II, III, and IV. It particularly emphasizes the performance enhancements observed in the MAE model, especially when augmented with HFMESLoss. The results demonstrate how the MAE model, referred to as Model IV, significantly outperforms the traditional Transformer model (Model I) and other model variants across all evaluation metrics. \begin{table}
\begin{tabular}{l c c c} \hline
**Model** & **VAE** & **RMSE** & **R\({}^{2}\)** \\ \hline LSTM & 0.5142 \(\pm\) 0.0517 & 12.7595 \(\pm\) 3.1162 & 0.3472 \(\pm\) 0.0667 \\ TCN & 0.5818 \(\pm\) 0.0591 & 12.0837 \(\pm\) 3.2476 & 0.4108 \(\pm\) 0.1004 \\ BioMat & 0.5398 \(\pm\) 0.0915 & 11.641 \(\pm\) 2.5968 & 0.4151 \(\pm\) 0.085 \\
**Model IV** & **0.6478 \(\pm\) 0.0549** & **9.7957 \(\pm\) 2.3446** & **0.5359 \(\pm\) 0.0838** \\ \hline \end{tabular}
\end{table} 
Table 7: This table shows how the MAE model (Model IV) surpasses other models such as LSTM, TCN, and BioMat in predicting biological signals, across all performance metrics. Figure 6: In figures (**a-c**), the green annotations represent the average metrics obtained from the newly developed MAE model integrated with HTFLoss (Model IV). It is evident that in figure (**a**), Model IV achieves the highest VAF value. In figure (**b**), Model IV exhibits the lowest RMSE value. In figure (**c**), Model IV achieves the highest R\({}^{2}\) value.