## 5 Disscusion and Conclusions
### Conclutions  
In this study, we introduced the MAE model, specifically designed for predicting hip flexion joint moments using biological signals such as sEMG signals and joint angles. Central to our model is the Adaptive Weight Module, which integrates sEMG signals, joint angles, and their combinations as three input branches. This module employs a deep separable convolution combined with a Multi-Layer Perceptron (MLP) to dynamically allocate weights to specific branches, optimizing the processing of small-sample data and enhancing the model's ability to efficiently capture local features essential for accurate motion prediction.  
In addition, we proposed the SFT Module, based on a streamlined Transformer architecture, to improve the efficiency of time-series feature extraction. By replacing the traditional attention mechanism with an MLP and eliminating the decoder component, the SFT module reduces the computational burden while preserving the model's ability to recognize complex relationships within the data. This architectural modification not only enhances the model's efficiency but also makes it particularly suitable for training with small batch sizes, improving long-term prediction accuracy.  
A key innovation in our approach is the integration of the Hybrid Time-Frequency Loss (HTFLoss) function. This loss function helps the model better integrate time-domain and frequency-domain information, refining the prediction of joint moments and improving the model's sensitivity to rapid biomechanical changes, which are critical for accurate dynamic joint moment predictions. The HTFLoss effectively fine-tunes the model's focus on joint moment characteristics, ensuring smoother and more precise transition state predictions.  
To evaluate the effectiveness of our proposed MAE model, we conducted experimental validations using a public dataset of five subjects performing treadmill activities. The results demonstrated superior performance across key metrics, including VAF, RMSE, and R\({}^{2}\), showcasing the MAE model's substantial improvements over traditional models such as LSTM and TCN. These findings confirm the model's ability to manage long-term dependencies and small-sample data, further highlighting its robust generalization across different speeds and its potential application in complex biomechanical scenarios.  
In conclusion, the MAE model offers a comprehensive approach for predicting joint moments, particularly in the context of biomechanics, rehabilitation, and human-machine interaction. Its ability to efficiently process biological signals, capture temporal nuances, and adapt to real-world challenges with limited data underscores its potential for broader applications in clinical and industrial settings.  
Conceptualization, B.X.; Methodology, B.X. and J.L.; Software, B.X., J.L. and W.N.; Validation, B.X., J.L., W.N., Z.S. and S.H.; Formal analysis, B.X. and J.L.; Investigation, B.X.; Resources, B.X., J.L., W.N., Z.S. and S.H.; Data curation, B.X. and J.L.; Writing - original draft, B.X., J.L. and S.H.; Writing - review and editing, B.X., J.L. and S.H.; Visualization, S.H.; Supervision, J.L., W.N., Z.S. and S.H.; Project administration, B.X. and S.H.; Funding acquisition, B.X. and W.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Regional Development Project in Fujian Province (2024H4004, 2024Y3002). And this research was funded by the Central Guidance on Local Science and Technology Development Program (2023L3030).  
Data Availability Statement: The dataset provided in this article is not easy to obtain because the data needs to be downsampled or preprocessed according to the research situation.