Interactive gesture recognition method based on spatiotemporal mask and variational mode decomposition

In order to reduce the error recognition rate and response time of interactive gestures, this study proposes a new interactive gesture recognition method based on spatiotemporal masking and variational mode decomposition. Firstly, the spatiotemporal features of interactive gestures are extracted using spatiotemporal masks, and combined with graph convolutional networks and self attention mechanisms, the modelling of hand joint data is optimised. Secondly, the variational mode decomposition technique is used to extract time-frequency features from interactive gestures. Finally, the principal component analysis method is used to reduce the dimensionality of high-dimensional gesture features, and a support vector machine classifier is employed to recognise different types of interactive gestures in the reduced feature space. The experimental results show that the proposed method performs well in interactive gesture recognition tasks, with a maximum error rate of no more than 1% and a response time of only 0.31 s.