## 4 Results
### Comparative Experiments and Ablation Studies  
In this section, experiments will be conducted using data from five healthy individuals performing treadmill exercises. The primary objective of these experiments is to evaluate the effectiveness of the proposed MAE model in predicting hip joint moments using a small sample dataset. A comprehensive performance evaluation will be carried out by comparing this model with the original Transformer model [43] and other deep learning models in terms of predictive accuracy and generalization capabilities, which include:  
(1) **Model I** : The original Transformer model [43]. (2) **Model II**: The original Transformer model utilizing HTFLoss. (3) **Model III**: The MAE model using traditional Mean Squared Error (MSE) loss. (4) **Model IV**: The MAE model employing HTFLoss. Figure 5: Prediction results of the MAE model for six test sets of subject Subject 4, where the blue line represents the moments calculated by the inverse dynamics method, and the red line represents the estimates from our model. Additionally, our model will be benchmarked against LSTM [44], TCN [45] and BioMat [46] models through multiple runs to highlight the distinctions in handling long-term dependencies between the MAE model and traditional memory-based approaches. This comparison aims to demonstrate the superior capability of the MAE model in managing long-term dependencies efficiently, which is crucial for dynamic and accurate prediction of joint moments.The results of this study are expected to provide valuable insights into the scalability and applicability of the MAE model in biomechanical prediction tasks, showcasing its potential to improve upon existing methodologies in the field. The performance of the model was assessed over six test sets for each subject, comprising a total of 30 test sets. Evaluation metrics included VAF, RMSE, and the R\({}^{2}\). Table 4 presents the mean values and standard deviations of these metrics for each subject's test sets, demonstrating the model's consistency and accuracy in interpreting biological signal data across different test instances. To ascertain the superiority of the MAE model, we employed biological signal data from the initial treadmill experiment of subject Subject 5 to train seven different models: LSTM, TCN, BioMat, Model I, Model II, Model III, and Model IV. These models were then tested using six subsequent experimental datasets. The average values and standard deviations of the VAF, RMSE, and R\({}^{2}\) metrics for these models are documented in Table 5. The results demonstrate that the MAE model (Model IV) outperformed all other comparative models across all evaluation metrics. Further investigation involved validating the robustness of the results through ablation studies conducted on five individuals with Models I, II, III, and IV. The outcomes of these experiments, as shown in Table 6, highlighted the significant advantages of the MAE model, particularly when integrated with HFMESLoss. Compared to the traditional Transformer \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Subject** & **VAF** & **RMSE** & **R\({}^{2}\)** \\ \hline Subject 1 & 0.5782 \(\pm\) 0.1317 & 8.4471 \(\pm\) 0.7599 & 0.4347 \(\pm\) 0.1873 \\ Subject 2 & 0.7025 \(\pm\) 0.0661 & 12.9176 \(\pm\) 2.3302 & 0.6095 \(\pm\) 0.0799 \\ Subject 3 & 0.6095 \(\pm\) 0.0629 & 11.6581 \(\pm\) 0.8800 & 0.4661 \(\pm\) 0.0784 \\ Subject 4 & 0.7003 \(\pm\) 0.1548 & 7.5606 \(\pm\) 0.7454 & 0.6223 \(\pm\) 0.1873 \\ Subject 5 & 0.6485 \(\pm\) 0.0607 & 8.3952 \(\pm\) 1.7500 & 0.5470 \(\pm\) 0.0847 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: This table displays the performance of the MAE model with HTFLoss, evaluated using metrics VAF, RMSE, and R\({}^{2}\) across six test sets for each of the five subjects. The mean values and standard deviations of these metrics are presented to highlight the model’s consistency and accuracy in interpreting biological signals from diverse testing scenarios.