## 3 Methods
### GAM-MLP Information Fusion
#### 3.4.1 Human Pose Feature Information Fusion
Figure 4: The model architecture of GAM-MLP and the process of action classification. MLP serves the purpose of introducing nonlinearity into the model, while the Transform Block, a crucial part of the Transformer architecture, is utilized for effectively modeling sequential data. This integration is geared towards enhancing the model's capacity for representation learning and nonlinear modeling, thereby enabling it to adaptively generate distinct weights between modules and effectively capture the intricate relationships and patterns present in the data. The Multi-Layer Perceptron (MLP) serves as a conduit for interchanging and amalgamating distinct features within the same group. It encompasses a series of layers including  
Figure 5: Different activity features of FFT feature extraction. Figure 6: Flow chart of action classification and recognition with group attention module. the fully connected, activation, and standardization layers [28; 29; 30]. The training process employs backpropagation, iteratively adjusting weights and biases. This fosters incremental learning and the integration of pertinent information across features in the same group. The fully connected layer embodies input and output neurons. Given the ith input, \(x_{i}\), and the jth output, \(y_{j}\), their relationship is governed by the weight, \(w_{ij}\), and bias, \(b_{j}\), represented as  
\[y_{j}=\sum_{i=1}^{10}w_{ij}x_{i}+b_{j} \tag{14}\]  
Two fully connected layers are followed by the activation layer and the standardization layer, respectively, to achieve the nonlinearity of network representation and improve training speed, generalization ability, and robustness, while reducing overfitting, gradient disappearance or gradient explosion problems. For the activation layer, the Rectified Linear Unit (ReLU) is selected, which is more efficient and safer than other activation functions while making the input of neurons a nonlinear transformation. Its function expression is shown as follows:  
\[\text{ReLU}(x)=\text{max}(0,x) \tag{15}\]  
where max represents taking the larger value of the two numbers, and 0 is the cutoff point of ReLU, that is, when \(x<0\), the value of ReLU is 0. For the normalization layer, we utilize batch normalization. The characteristics of each sample, \(x^{(i)}=\left(x_{1}^{(i)},x_{2}^{(i)},\cdots,x_{m}^{(i)}\right)\), where \(m\) indicates feature count, are transformed according to  
\[\text{Mean}:\mu_{B}=\frac{1}{m}{\sum_{i=1}^{m}x_{i}} \tag{16}\]  
\[\text{Variance}:\sigma_{B}^{2}=\frac{1}{m}{\sum_{i=1}^{m}(x_{i}-\mu_{B})}^{2} \tag{17}\]  
\[\text{Normalization}:\hat{x}_{i}=\frac{x_{i}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+ \epsilon}} \tag{18}\]  
Figure 6.0  
where \(\mu_{B}\) and \(\sigma_{B}^{2}\) denote batch mean and variance, respectively, \(\hat{x}_{i}\) is the normalized feature, \(\gamma\) and \(\beta\) are learnable parameters for scaling and shifting, and \(\epsilon\) prevents division by zero. This process standardizes data, maintains distribution information, and optimizes data representation. The synergetic integration of these layers fortifies the MLP's capacity to comprehend and amalgamate interrelationships among features within the same group. This amalgamation enhances activity recognition accuracy through a robust and sophisticated learning process. Subsequently, we employ transformer blocks to enhance attention evaluation and sequential modeling, leveraging the self-attention mechanism to improve the model's performance. Specifically, the transformer block facilitates establishing global dependencies across various points within the input sequence, enabling the capture of longer-range semantic dependencies and enhancing the model's efficacy in human motion recognition tasks. Transformer blocks typically comprise several sub-layers, including a self-attention layer, a fully connected layer, and a normalization layer. The self-attention layer calculates attention weights at different positions within the input sequence, facilitating the correlation of distinct parts of the input sequence.