## 5 Conclusions  
In this study, the authors conducted a literature review and market research, looking for studies and out-of-the-box solutions that proposed or implemented methodologies for hand tool pose estimation during assembly and maintenance procedures in the industrial field. They discovered that, even if there are plenty of studies concerning static and dynamic object pose estimation in the literature, the problem of occluded hand tool pose estimation and tracking in the industrial environment is not extensively investigated. Furthermore, it emerged that all of the commercial solutions do not really implement algorithms that are able to accurately track the pose of a tool partially occluded by the operator's hands, but they roughly derive it by the hand pose or by using color matching techniques. For this reason, the authors selected four of the most promising computer vision and deep learning frameworks (ArUco, OpenPose, YOLO and AKBT) in the field to evaluate their performance in the task of industrial hand tool detection and pose estimation in real-time during an assembly or maintenance procedure. Two different approaches have been considered: a direct approach, in which the tool is directly detected and tracked using some robust features that are visible, and an indirect approach, in which the body joints of the operator and their hands are detected and tracked, and the tool pose is derived by considering the unique handling pose of the tool. To the best of the authors' knowledge, this study is the first that compares the performance of ArUco-, OpenPose-, YOLO- and AKBT-based methods side by side in the task of occluded hand tool tracking. In order to fairly compare these frameworks, the objective was redefined, reducing the pose estimation problem to only 3D position estimation, because it is intuitive that a method such as YOLO, which only returns a position estimation, requires being complemented with another technique for orientation estimation. A complete pose estimation investigation is worth a further study. The results of the study suggests OpenPose is the most complete proposal, thanks to its acceptable root mean square point to point distance, D. RMS (approximately 12 cm) and low variability in dynamic conditions, even when a limited network resolution is adopted. This framework is worth a dedicated study in order to exploit all model capabilities in extracting the information with a higher accuracy. OpenPose could be used to implement a tool pose estimation module in a smart workstation for training and assessment. A system designed with this feature would have a great impact in the automotive industry, especially for critical procedures that require monitoring, with high accuracy, the movements of the operator and the correct usage of hand tools for battery-pack assembling, engine repairing and overhauling, glue smearing and adhesive and sealant application. In future studies, the authors aim to investigate the problem of complete pose estimation (including orientation) and to conduct a deeper study on OpenPose, implementing a pose estimation module based on it and evaluating the performance in experiments that simulate procedures at different levels of complexity. **Author Contributions:** Conceptualization, I.D.F., D.B., N.L., G.D.S. and V.B. ; data curation, I.D.F., D.B., S.G. and G.L. ; formal analysis, I.D.F., D.B., A.B., N.L. and G.D.S. ; investigation, I.D.F. and D.B. ; methodology, I.D.F., D.B., S.G. and G.L. ; resources, I.D.F. ; software, I.D.F., D.B., S.G. and G.L. ; supervision, V.B. ; validation, I.D.F., D.B., S.G., G.L., A.B., N.L., G.D.S. and V.B. ; visualization, I.D.F. and D.B. ; writing--original draft, I.D.F., D.B., A.B., N.L., G.D.S. and V.B. ; writing--review and editing, I.D.F., D.B., A.B., N.L., G.D.S. and V.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.