Skeleton-based Hand Gesture Recognition for Assembly Line Operation

Abstract

This research aims to develop a hand gesture recognition (HGR) by combining the OpenPose and Spatial Temporal Graph Convolution Network (ST-GCN) to classify the operator’s assembly motion. By defining the hand gestures with five types of therbligs, the network model was trained to recognize the human hand gesture. Although the accuracy of recognition is 78.3% with room for improvement based on preliminary experiment results, the structure of the proposed network establishes a foundation for further improvement in future work.