A Deep-Learning Based Worker’s Pose Estimation

Abstract

Work in a factory is physically demanding. It requires workers to perform tasks in different awkward positions. Thus, long work shifts might have prolonged effects on workers’ physical health. To minimize the risks, we introduce an automatic workers’ pose estimation system, which will calculate a worker’s body angle and indicate which angles are safe or not safe for performing tasks in various work places. By combining CMU OpenPose with body assessment tools, such as Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA), the proposed system automatically determines a worker’s risk pose. This method, intended to replace a manual analysis of work posture, will help build safer environments for workers.