DSANet: A lightweight hybrid network for human action recognition in virtual sports

Abstract

Human activity recognition (HAR) has significant potential in virtual sports applications. However, current HAR networks often prioritize high accuracy at the expense of practical application requirements, resulting in networks with large parameter counts and computational complexity. This can pose challenges for real-time and efficient recognition. This paper proposes a hybrid lightweight DSANet network designed to address the challenges of real-time performance and algorithmic complexity. The network utilizes a multi-scale depthwise separable convolutional (Multi-scale DWCNN) module to extract spatial information and a multi-layer Gated Recurrent Unit (Multi-layer GRU) module for temporal feature extraction. It also incorporates an improved channel-space attention module called RCSFA to enhance feature extraction capability. By leveraging channel, spatial, and temporal information, the network achieves a low number of parameters with high accuracy. Experimental evaluations on UCIHAR, WISDM, and PAMAP2 datasets demonstrate that the network not only reduces parameter counts but also achieves accuracy rates of 97.55%, 98.99%, and 98.67%, respectively, compared to state-of-the-art networks. This research provides valuable insights for the virtual sports field and presents a novel network for real-time activity recognition deployment in embedded devices.