# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 3 Methods
### Improved O-Inception Convolution Structure for HAR  
As the progenitor of the O-Inception convolution structure, Inception's design principles offer valuable insights for human activity recognition. The core idea of Inception is to substitute a large convolution kernel with several small ones. This approach maximizes thepotential of convolution and pooling, integrates convolutional feature maps, and connects the outputs of each convolutional group. The use of Inception can mitigate the issue of exponentially increasing parameters that occurs when the network is deepened or widened.  
Taking the commonly used GoogLeNet structure as an example, the inception Native structure increases the width of the network on the one hand and enhances its adaptability to scaling on the other. However, when faced with the high precision and rapid response speed requirements of human activity recognition, this simple solution has two main drawbacks. Primarily, a larger network usually means more parameters, making the extended network more susceptible to overfitting, particularly when the training set has a limited sample of labels. Subsequently, blindly increasing the size of the network can lead to an exponential increase in computational resource requirements.  
To overcome the issues of increased time consumption and decreased recognition speed caused by improving feature expression ability in the original Inception architecture, we propose a new O-Inception convolutional structure. We use dark green to represent the Previous layer and filter Concatenation, this structure achieves higher accuracy and response speed in human activity recognition by replacing the 5 \(\times\) 5 convolution in the original structure with 2 \(\times\) 2 and 3 \(\times\) 3 two-layer convolutions, replacing the original 3 \(\times\) 3 pooling layer convolution with a 2 \(\times\) 2 structure, and replacing the convolution after the pooling layer with 1 \(\times\) 1 to 3 \(\times\) 3 convolutions. The replaced convolution is represented in light yellow, and the replaced pooling layer is represented in light red. Three 1 \(\times\) 1 convolutions that are not replaced are represented in dark yellow. The improved O-Inception module is shown in Figure 2.  
The O-Inception structure uses the approach of replacing a large convolution kernel with multiple small convolution kernels to address the issue of excessive time complexity. This transformation significantly reduces the time complexity while keeping the feature map unchanged, thereby increasing the network's recognition and training speed. Moreover, the replacement of the pooling layer can reduce the loss of features to a certain extent. For instance, a 3 \(\times\) 3 pooling operation will lose eight pieces of information while a 2 \(\times\) 2 pooling operation only loses three pieces of information [33]. Finally, the O-Inception structure employs a 3 \(\times\) 3 convolution to replace the 1 \(\times\) 1 convolution and enhances the convolution field of view by increasing the field of view window, thereby improving the model's accuracy.