## 5 Disscusion and Conclusions  
### Disscusion  
In this paper, we introduced a newly developed MAE model and its integration with HTFLoss, tailored for the task shown in Figure 1, to predict hip flexion joint moments in a subsequent 300 ms period based on biological signals recorded over a 300 ms interval. The study utilized a public dataset comprising biological data from multiple subjects performing on a treadmill, including sEMG signals, joint angles, and hip flexion joint moments derived through OpenSim. To evaluate the stability of our model with small sample data, it used data from a single experimental run (involving four speed variations) to predict hip flexion joint moments for six additional experimental runs (comprising 28 speeds). As illustrated in Figure 5, our model demonstrates minor discrepancies between predicted values and actual values, effectively capturing sharp fluctuations at peaks and troughs. This validates the feasibility of training models on small batches and predicting biological signals using few data samples. Table 4 displays the average values of three metrics across different subjects, highlighting individual variability which not only reflects in demographic data (age, gender, height, weight) presented in Table 1 but also in differences in skeletal frameworks and behavioral habits that cause variability in the computed data. Table 4 shows the performance of the MAE model combined with HTFLoss on six test sets of five subjects, evaluated by VAF, RMSE, and R\({}^{2}\) indicators. From the data, the VAF mean is between 0.5782 and 0.7025, and the overall mean is about 0.6478, indicating that the model has a certain ability to explain the variance of biological signals, but there are differences between different subjects. The standard deviation of subject 4 is 0.1548, and the fluctuation is relatively large, reflecting that the test data is large The difference or the adaptability of the model is unstable; the RMSE mean is in the range of 7.5606-12.9176, and the overall mean is about 9.7957. The error of subject 4 is relatively small, while the standard deviation of subject 2 is 2.3302, and the error fluctuates greatly; the R\({}^{2}\) mean is between 0.4347 and 0.6223, and the overall mean is about 0.5359, indicating that the model has a good fitting effect on the biological signal of subject 4, but the standard deviation of subjects 1 and 4 is 0.1873, and the fitting stability is poor. In general, the performance of the model on different subjects varies. The mean and standard deviation of each indicator reflect the uneven accuracy and consistency of the model in interpreting biological signals. Further correlation analysis can be used to explore the intrinsic connection between the indicators to deeply evaluate the model performance. Furthermore, hardware detection errors during data collection introduced noise into the dataset, contributing to the variability observed. On Subject 5, our model was juxtaposed against six others, revealing through Table 5 that models such as LSTM, TCN, BioMat, and Transformer Model (Model I) exhibit only marginal variations across metrics like VAF, RMSE, and R\({}^{2}\). Table 5 presents the performance metrics, including VAF, RMSE, and R\({}^{2}\), of seven different models (LSTM, TCN, BioMat, Model I, Model II, Model III, and Model IV) trained using the initial treadmill data of Subject 5, and records the mean and standard deviation of these metrics for the subsequent six experiments. From the VAF indicator, its mean range is between 0.5033 (BioMat) and 0.6485 (Model IV). The standard deviation shows that the degree of fluctuation of each model in different experiments is different. For example, the standard deviation of Model III is 0.1269, which fluctuates greatly, while the standard deviation of Model II is 0.0392, which is relatively stable. The mean of RMSE indicator is between 8.3952 (Model IV) and 11.5938 (Model I). The standard deviation reflects the fluctuation of the prediction error of each model.