## 5 Disscusion and Conclusions
### Disscusion
For example, the standard deviation of TCN is 1.9643, and the error fluctuation is obvious. The mean of R\({}^{2}\) indicator is between 0.3216 (LSTM) and 0.5470 (Model IV). The standard deviation reflects the stability difference of the model fitting effect on the data. For example, the standard deviation of BioMat is 0.1331, and the fitting stability is poor. In general, through the comprehensive comparison of various indicators, it can be seen that the MAE model (Model IV) surpasses other models in all evaluation indicators and shows better performance and stability. These minimal differences underscore a critical insight: Under specific challenges such as small sample sizes and high noise levels, which are characteristic of biological signal sequences, the prediction accuracy of time series models tends to be very similar. This phenomenon is corroborated by the data presented in Table 7, highlighting the resilience of these models under stringent test conditions. Moreover, according to Table 6, Table 6 shows the results of ablation studies on five subjects using Models I, II, III, and IV, focusing on the performance improvement of the MAE model (i.e., Model IV), especially when combined with HFMESLoss. From the perspective of VAF indicators, its mean gradually increased from 0.5423 (Model I) to 0.6478 (Model IV). The standard deviation shows the fluctuation of each model on different subjects. The standard deviation of Model III is 0.0635, which is relatively large, while the standard deviation of Model I is 0.0311, which is relatively stable. The mean of RMSE indicators dropped from 12.089 (Model I) to 9.7957 (Model IV), reflecting that the model prediction error gradually decreased. Among them, the standard deviation of Model I is 3.1087, which is relatively large, indicating that its error fluctuates significantly. The mean of R\({}^{2}\) indicators increased from 0.4075 (Model I) to 0.5359 (Model IV), indicating that the model fits the data better and better. The standard deviations of each model reflect the difference in fitting stability. Model IV performs best in all evaluation indicators, which fully proves that it has significant performance advantages over the traditional Transformer model (Model I) and other model variants. the innovative application of our proposed HTFLoss in conjunction with the Transformer model (Model I) has demonstrably amplified the performance metrics of the Transformer in the context of this study. This suggests that the HTFLoss, by prioritizing frequency domain errors, adapts the model to more effectively capture rapid changes in joint dynamics--an aspect critical in biomechanical applications. The MAE model, specifically adapted for the Transformer framework, further refines performance metrics, indicating that the model's architecture benefits significantly from modifications tailored to the specifics of biomechanical signal processing. Notably, the integration of the MAE model with HTFLoss (Model IV) markedly elevates the experimental outcomes across all considered metrics, as evidenced in Tables 5-7. This comprehensive enhancement suggests a synergetic effect between the MAE's architectural innovations and the targeted error sensitivity of the HTFLoss. In assessing the performance of different models, as illustrated in Figure 6, Model IV demonstrates significant advantages across several key metrics. Initially, in Figure 6a, Model IV's VAF is notably higher than that of other models, indicating its effective capture of dynamic physiological signals, which is crucial for accurately simulating biomechanical processes. Further observations in Figure 6b reveal that Model IV maintains a consistently lower error rate in RMSE compared to other models. This capability of Model IV to significantly reduce deviations from actual values is particularly important for applications requiring precise control, such as biomechanical simulations and prosthetic control. In Figure 6c, Model IV exhibits superior performance in the R\({}^{2}\) metric.