Research on the Improvement of Openpose Motion Recognition Based on Somatosensory Games

In the field of somatosensory game design, traditional human motion recognition methods have limitations. They often overlook the role of the pre-pose estimation algorithm and fail to fully extract motion features, which affects the accuracy and user experience of somatosensory games. This study proposes an improved method for somatosensory game design based on Openpose motion recognition to address this issue. By combining lightweight Openpose and an attention-guided graph convolutional network, it aims to enhance the accuracy and efficiency of the game in recognizing players' motions. This method mainly includes an Openpose algorithm based on ShuffleNet and a graph convolutional algorithm with attention on adjacency matrices at different scales. During the game operation, the input video is processed by the lightweight Openpose to obtain information on 18 human key points and transform it into the form of basic spatio-temporal graph data. Subsequently, the influence of the adjacency matrices corresponding to the neighbor information of nodes at different scales is calculated through the self-attention mechanism. The weighted combination of the adjacency matrices at each scale is input into the graph convolutional network to extract features. The extracted discriminative features are then output as action categories through global average pooling and a Softmax classifier, effectively improving the effect of motion recognition in somatosensory games, bringing more accurate and smooth game experiences to players, possessing high practical value and application prospects, and is expected to promote the further development and improvement of motion recognition technology in somatosensory games.