Human Motion Recognition for Industrial Human-Robot Collaboration based on a Novel Skeleton Descriptor

In the industrial human-robot collaboration environment, workers and robots often need to collaborate to complete complex tasks in a shared space. Accurate and robust motion recognition is the key to improving the productivity and safety of human-robot collaboration. Accurately tracking and recognizing workers' motions can provide clues about tasks to be performed, thus ensuring the safety of collaborative workspaces. Aiming at the differences in postures and characteristics of workers' motions in the working environment, the human body's self-occlusion and partial occlusion, this paper uses a skeleton recognition algorithm to estimate the 3D skeleton information in monocular video. A novel skeleton descriptor that describes static and dynamic features of the 3D skeletons in the short time around the current frame is proposed. Besides, we train a deep neural network to recognize the human motion. Experimental results show that the proposed skeleton descriptor can improve the accuracy of motion recognition by about 4%, and the neural network we use outperforms the other three classical classification methods.