## 2 Methodology
### AdaptiveWeight Module  
The innovative AdaptiveWeightModule is depicted in Figure 3. The module integrates Depthwise Separable Convolution (DSC) and Multilayer Perceptrons (MLP) within a multi-branch structure. This design allows the module to learn effectively from diverse feature spaces, including surface sEMG signals, joint angles, and their combinations. The AdaptiveWeightModule is particularly designed to handle the significant spatiotemporal discrepancies that arise from muscle synergy as well as from the forward and inverse dynamics of muscles and joints [37]. The core component of this module, the Depthwise Separable Convolution, operates by processing each input data channel (e.g., sEMG, joint angles, etc.) independently at every time step. Unlike traditional convolution, where multiple channels are processed together, DSC applies a filter to each channel separately, making it computationally efficient. After this process, the output from each channel is then combined, allowing the network to focus on extracting features specific to each input type. This method reduces the number of parameters needed, which in turn enhances the model's efficiency and enables it to handle the biological signals more effectively [38].  
Following the DSC layer, the Multilayer Perceptrons (MLPs) take over to further refine the output. The MLPs integrate the refined features from the DSC with the initial inputs from the respective branches, generating adaptive weights. These adaptive weights help the model focus on the most relevant features for predicting joint moments at different time points. Essentially, the MLPs assess the significance of each input channel (like sEMG or joint angles) over time, assigning more importance to features that are crucial for accurate predictions. By considering the temporal relevance of each feature, the model becomes better equipped to predict joint moments, leading to more accurate and nuanced predictions.This workflow ensures that the module can dynamically adapt to varying input  
Figure 1: (**a**) The input and output for a single prediction within 300 ms, where the input consists of the previous 300 ms of sEMG signals and two joint angles, and the output is the joint moments for the subsequent 300 ms. (**b**) The flowchart from the first prediction to the last prediction throughout an entire training task, as shown in the figure, where the yellow section represents the input time window and the blue section represents the output time window.  
Figure 2: Schematic diagram of the MAE model.  
signals and improve the overall accuracy of joint moment predictions. The workflow is as follows:  
(1) This method allows the model to independently process features of input data channels at each time point, which reduces the number of parameters and enhance feature extraction flexibility. For input features \(X\), the depthwise separable convolution can be expressed as:  
\[X_{out}=ReLU(BN(PC(ReLU(BN(DC(X)))))) \tag{1}\]  
where \(DC\) represents the depthwise convolution operation, \(PC\) denotes the pointwise convolution, \(BN\) is batch normalization, and \(ReLU\) is the activation function.  
(2) The MLP is employed to learn feature weights from the output after depthwise separable convolution, enhancing the precision and adaptability of predictions. This can be depicted as:  
\[W=MLP(X_{out}) \tag{2}\]  
where \(W\) represents the learned weights, used for subsequent feature fusion and decision-making.