# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 3 Model
### Improved R6-SELU Activation Function  
In a convolutional neural network, convolution and pooling are linear operations, which cannot form a complex expression space. The activation function is a kind of non-linear mapping structure in the neural network. Adding activation function to the neural network can improve the expression ability of the neural network. ReLU, as the main function of the activation function is often used in various deep neural networks. However, with the deepening of the network, ReLU itself has great limitations, as the output range of the ReLU activation function is \([0,+\infty)\), which will bring some loss of accuracy; secondly, the gradient of the ReLU activation function is constant 0 when it is in negative half axis, and the neuron will appear inactivated, resulting in the parameter not being able to be updated. In the end, the mean value of the ReLU activation function output value is always greater than zero. It will ignore some key information of the negative-valued feature information and the calculation results are difficult to converge, which affects the identification effect of the network. Although in MobileNetv2, the author changed the activation function into the ReLU6 activation function, and controlled the output range at \([0,6]\), so that the network can have a good numerical resolution during the low precision floating point operation to prevent the loss of accuracy. However, The ReLU6 activation function still has the same disadvantages as the ReLU activation function.  
Aiming at the above defects, we introduce another activation function, SELU [34]. The SELU activation function equation is shown in Equation (5), where \(\alpha\) and \(\lambda\) are fixed values derived from the formula, \(\alpha\approx 1.673\), \(\lambda\approx 1.0507\). The positive half-axis derivative of the SELU activation function is a constant \(\lambda\), which can reduce the gradient disappearance. At the same time, the negative half axis of the function is not zero, which prevents the loss of negative-valued information and the occurrence of neuron death, and enables comprehensive learning of image features.  
\[\text{SELU}(\text{x}) =\begin{cases}\lambda x&x>0\\ \lambda(\alpha e^{x}-\alpha)&x\leq 0\end{cases} \tag{5}\]  
Combining the respective advantages of ReLU6 and SELU, we proposed an improved activation function R6-SELU, and the improved activation function expression is shown in Equation (6):  
\[\text{R6-SELU}(x)=\begin{cases}\min(6,\max(0,x)),x>0\\ \lambda(\alpha e^{x}-\alpha),x\leq 0\end{cases} \tag{6}\]  
The improved activation function image is shown in Figure 6. The improved activation function is analyzed and has the following characteristics:  
1. Parameters have non-zero output in both positive and negative half axes;
2. The positive half axis inherits the characteristics of fast convergence and small precision loss of ReLU6 activation function;  
Figure 5: The linear bottleneck with the ECA module.  
3. The negative half axis inherits the SELU activation function, non-linear correction of negative-valued characteristic information, smooth convergence, and enhances the model's expression ability.