## 2 Methodology
### Simplified Feature Transformer Module  
The goal of the SFT model is to generate precise predictions highly correlated with joint moment, as depicted in Figure 4. This process requires the fusion of information from different branches to produce the final prediction or output. Based on the Transformer model, this study employs a single-layer encoder structure in the feature extraction part, reducing the consumption of computational resources and enabling better handling of small sample data. Moreover, the traditional multi-head attention mechanism is replaced with a Multilayer Perceptron (MLP) to offer a more flexible and efficient learning process. The MLP can better capture the complex nonlinear relationships and long-range dependencies in features such as biological signals by compressing the input dimensions and then expanding them, which helps reduce noise interference in the data. This dynamic focus is particularly important in capturing the local characteristics of muscle signals and joint angles at different time intervals. When processing features such as biological signals, small batch training combined with Layer Normalization (LayerNorm) enhances both the stability and speed of convergence. This also helps improve the model's ability to handle practical data, such as sEMG signals and joint angle data. Additionally, the model integrates spatiotemporal information across various branches, ensuring that different time-related features are appropriately weighted. The methodological details are described as follows:  
Figure 3: The diagram of the adaptive model illustrates the processing of three input channels through Depthwise Separable Convolutions (DSC) and Multilayer Perceptrons (MLP). Initially, each channel undergoes convolution with 60 1D filters of size 3, followed by convolution with 60 1D filters of size 1. The outputs from these convolutions are then merged and passed through a three-layer MLP, each layer containing 60 neurons. Finally, adaptive weights are generated as the output of this processing pipeline.  
(1) Encoder Architecture: The SFT model utilizes a single-layer encoder structure in its feature extraction phase. This choice helps reduce computational resource demands, making the model more suitable for handling datasets with small sample sizes.  
(2) Attention Mechanism Replacement: In the SFT model, traditional multi-head attention mechanisms are replaced with MLP. This adjustment allows the model to more flexibly learn relationships between input sequence features. Specifically, the MLP compresses the input dimensions and then expands them, which helps to obscure noise interference in the data. This enables the model to effectively capture complex nonlinear relationships and long-range dependencies, which are prevalent in biological signals. MLPs are particularly effective in this context, improving the model's ability to understand intricate feature relationships and enhancing its overall performance.  
(3) Focus on Local Characteristics: The model is adept at capturing local characteristics of sEMG signals and joint angles at various time intervals. This ability is crucial for accurately modeling biological signals.  
(4) Training Enhancements: Employing small batch training coupled with Layer Normalization (LayerNorm) enhances the model's training stability and speeds up convergence. These features are beneficial when processing signals like sEMG and joint angle data.