Convolutional neural network for gesture recognition human-computer interaction system design.

ABSTRACT: Gesture interaction applications have garnered significant attention from researchers in the field of human-computer interaction due to their inherent convenience and intuitiveness. Addressing the challenge posed by the insufficient feature extraction capability of existing network models, which hampers gesture recognition accuracy and increases model inference time, this paper introduces a novel gesture recognition algorithm based on an enhanced MobileNet network. This innovative design incorporates a multi-scale convolutional module to extract underlying features, thereby augmenting the network's feature extraction capabilities. Moreover, the utilization of an exponential linear unit (ELU) activation function enhances the capture of comprehensive negative feature information. Empirical f

SUBMITTER: Niu P 

PROVIDER: S-EPMC11838865 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature