Evaluation of Vision-Based Hand Tool Tracking Methods for Quality Assessment and Training in Human-Centered Industry 4.0

Abstract

Smart industrial workstations for the training and evaluation of workers are an innovative approach to face the problems of manufacturing quality assessment and fast training. However, such products do not implement algorithms that are able to accurately track the pose of a hand tool that might also be partially occluded by the operator’s hands. In the best case, the already proposed systems roughly track the position of the operator’s hand center assuming that a certain task has been performed if the hand center position is close enough to a specified area. The problem of the pose estimation of 3D objects, including the hand tool, is an open and debated problem. The methods that lead to high accuracies are time consuming and require a 3D model of the object to detect, which is why they cannot be adopted for a real-time training system. The rise in deep learning has stimulated the search for better-performing vision-based solutions. Nevertheless, the problem of hand tool pose estimation for assembly and training procedures appears to not have been extensively investigated. In this study, four different vision-based methods based, respectively, on ArUco markers, OpenPose, Azure Kinect Body Tracking and the YOLO network have been proposed in order to estimate the position of a specific point of interest of the tool that has to be tracked in real-time during an assembly or maintenance procedure. The proposed approaches have been tested on a real scenario with four users handling a power drill simulating three different conditions during an assembly procedure. The performance of the methods has been evaluated and compared with the HTC Vive tracking system as a benchmark. Then, the advantages and drawbacks in terms of the accuracy and invasiveness of the method have been discussed. The authors can state that OpenPose is the most robust proposal arising from the study. The authors will investigate the OpenPose performance in more depth in further studies. The framework appears to be very interesting regarding its integration into a smart workstation for quality assessment and training.

Keywords

• Industry 4.0
• manufacturing
• hand tool tracking
• artificial vision
• deep learning
• tracker