## 5 Disscusion and Conclusions
### Disscusion
A higher R\({}^{2}\) value confirms that the model can reliably replicate joint behavior in real-world conditions, enhancing confidence in using this model for dynamic and real-time applications. This performance not only highlights the finesse of Model IV in handling temporal dependencies and effectively integrating multi-dimensional data inputs within its architectural design but also emphasizes its potential applications in fields such as biomechanics research, rehabilitation medicine, and human-machine interaction. These findings provide valuable insights into the development of joint dynamics prediction models in the biomechanics field, where accuracy and reliability are crucial for achieving high-quality therapeutic outcomes and adaptability to users. Table 7 shows that innovations in many aspects provide strong support for the excellent performance of Model IV. In terms of data input and feature processing, sEMG signals, joint angles and their combinations are integrated into the adaptive weight module as three input branches. The deep separable convolution combined with the MLP adaptive module can dynamically assign weights to specific branches. This innovation greatly enhances the efficiency of feature utilization. Compared with the traditional feature extraction methods of LSTM and TCN, it allows the model to learn the key features of different types of data more targeted, thereby improving the learning effect of the model. SFT, which is based on the Transformer architecture, replaces the traditional attention mechanism with MLP and changes the traditional encoder-decoder format. This improvement not only reduces the computational burden, but also retains the ability to recognize complex relationships in the data, effectively improving the efficiency and prediction accuracy of the model. However, the LSTM calculation process is complex and is prone to gradient problems when processing long sequences; although TCN has high computational efficiency, it is insufficient in capturing complex time dependencies. This architectural innovation of Model IV just makes up for the shortcomings of traditional models. In terms of loss function design, the introduction of a new Hybrid Time-Frequency Loss (HTFLoss) function can better integrate time domain and frequency domain information, enhance the structure of obvious joint torque features, and make the prediction of transition states smoother. This innovation significantly improves the prediction quality and the model's ability to respond to dynamic changes, making Model IV perform better when processing complex time series data. The performance of Model IV surpasses LSTM and TCN, not only due to the balance between time series feature extraction and computational efficiency in architectural design, but also inseparable from innovations in data input processing, architectural improvements, and loss function design. These in-depth analyses help us better understand the underlying reasons for this performance improvement and provide valuable references for further model improvement and application. Additionally, to avoid overfitting between predicted and actual values, this study also explores the inherent variability in joint moment generation during physical activities. Human muscle force during repetitive actions varies depending on energy expenditure, leading to differences in joint moments even during identical movements [48; 49]. However, due to data incompleteness and limitations in noise handling, we are unable to test across additional modalities. Under different modalities, more drastic variations in biological signals could occur, and it is uncertain whether our model can handle modalities beyond running. Moreover, as the model is designed for specific tasks, adapting it for other biological signal predictions would require adjusting parameters and weight distributions appropriately. Given the limited dataset, the model's ability to generalize across larger and more diverse populations remains uncertain.