## 2 Methods
### Partial Block  
In the realm of signal processing, precisely recognizing and utilizing essential features is a paramount challenge, especially in applications like sEMG-based gesture recognition. Our innovative approach tackles this challenge by introducing feature importance learning, a pivotal component that dynamically identifies and prioritizes the most critical features  
Figure 1: The general procedure of the proposed method. The gray area is the structure of PCS-EMGNet.  
within the sEMG signal. The feature importance learning process revolves around our feature importance network, a tailored neural network meticulously designed for this task. This network boasts a 1D convolutional layer that diligently evaluates the significance of each feature. By harnessing this layer, it quantifies the importance of features and generates importance scores. These scores, constituting a multidimensional importance distribution, undergo normalization via the softmax function. This ensures that they accurately reflect the relative importance of each feature in the input channels.  
\[importance\_scores=Softmax(Conv(x_{input})) \tag{1}\]  
where \(x_{input}\) represents the input signal data within the partial block in Figure 1, which has is processed through patch embedding. The term \(importance\_scores\) denotes the importance scores of feature vectors, referred to as filters in Figure 1. Complementing our feature importance extraction is the partial conv module, a lightweight local convolutional layer that optimizes the signal processing pipeline. The partial conv module plays an indispensable role in feature processing and the reduction of computational overhead. The architecture of the partial conv module consists of a standard convolutional layer augmented by a GELU activation function and a partial convolution layer.  
\[x=x_{input}\odot importance\_scores \tag{2}\]  
where \(x\) represents the processed output signal in Figure 1. The symbol \(\odot\) denotes the dot product operation. This combination enables the efficient processing of input channels while preserving the vital components of the signal. This is achieved by selectively applying convolution to a fraction of the input channels, a crucial element for efficient feature extraction.  
Our unique methodology seamlessly intertwines feature importance learning and the partial conv module, leveraging the strengths of both components for optimized signal processing. The process begins with the feature importance network, which calculates importance scores for individual features. These scores serve as guidance for the selective feature weighting, emphasizing those features deemed most crucial. Following the feature importance-based weighting, the data are handed over to the partial conv module. This component, working in tandem with the importance scores, performs the processing. It partitions the input data into two segments, with one segment being selectively weighted based on the feature importance scores. By focusing computation on the most critical features, we substantially reduce redundancy and computational overhead.  
Our approach marries feature importance learning with the lightweight partial conv module, presenting a powerful solution for efficient sEMG signal processing. This approach not only adapts to the dynamic importance of features in various gestures but also significantly streamlines computational complexity. It stands as an ideal choice for real-time applications by reducing computational load and enhancing the effectiveness of gesture recognition systems while preserving high accuracy.