DRN-SEAM: A Deep Residual Network Based on Squeeze-and-Excitation Attention Mechanism for Motion Recognition in Education

Abstract

In order to solve the shortcomings of the traditional motion recognition methods and obtain better motion recognition effect in education, this paper proposes a residual network based on Squeeze-and-Excitation attention mechanism. Deep residual network is widely used in various fields due to the high recognition accuracy. In this paper, the convolution layer, adjustment batch normalization layer and activation function layer in the deep residual network model are modified. Squeeze-and-Excitation (SE) attention mechanism is introduced to adjust the structure of network convolution kernel. This operation enhances the feature extraction ability of the new network model. Finally, the expansibility experiments are conducted on WISDM(Wireless Sensor Data Mining), and UCI(UC Irvine) data sets. In terms of F1, the value exceeds 90%. The results show that the proposed model is more accurate than other state-of-the-art posture recognition models. The proposed method can obtain the ideal motion recognition results.