## 4 Discussion  
In order to evaluate and compare ArUco-, OpenPose-, YOLO- and AKBT-based methods, the authors designed a system composed of an Azure Kinect device, HTC Vive tracking system (the benchmark) and an application with all of the frameworks in one that runs on a computer with this configuration: Intel I7-8750H, GTX 1060 with 6 GB memory and 16 GB RAM.  
The frame per second (FPS) performance for the frameworks on the machine were the following: YOLO 30+ FPS, AKBT 10 FPS, OpenPose 10 FPS and ArUco 30+ FPS. The leveling off of all performances to the lowest FPS (10 FPS) was required. The final performance of the application was under 10 FPS because it was subjected to the overhead of the post-processing phases and the simultaneous execution of the methods.  
The authors designed an experiment in which a user handling a power drill simulated three different conditions during an assembly procedure: holding the tool in a static position (stationary condition) and moving the tool at two different velocities (slow-motion condition, 4 cm/s, and fast-motion condition, 8 cm/s).  
The performance of the four methods has been evaluated on two metrics: the root mean square point to point distance (D. RMS) and the multivariate \(R^{2}\) of a trajectory compared to the benchmark system (HTC Vive tracking system).The results have been reported as boxplot charts and a statistical analysis has been performed.  
As reported in Figure 11, ArUco resulted in a very accurate method in the stationary condition, showing a D. RMS lower than all the other methods and a low variability. The slow-motion and fast-motion conditions boxplot charts (Figure 11) show that the better accuracy of ArUco is also preserved in dynamic conditions, even if the variability increases, becoming comparable with the one of OpenPose and YOLO for the slow-motion condition and even being the worst overall for the fast-motion condition. The correlation boxplots in Figure 12 confirm the same situations, and the ranking of the methods seems, globally, the following: ArUco, OpenPose, YOLO, AKBT.  
The authors could presume that ArUco would be the most accurate of the frameworks because it implements a marker-based technique, unlike the others. It confirms the results of other studies, such as [37]. Nevertheless, it is not enough, because an invasive method cannot be seriously considered in a real industrial application; it is rarely accepted, for safety and ergonomics reasons, to install markers on a hand tool and to handle it in a way that always keeps them visible.  
Although OpenPose does not have the best performance in term of D. RMS, it could be considered a valid framework for building a virtual guidance system; the low variability of the D. RMS suggests it is a reliable method if the user can accept a D.RMS of around 10 cm. Furthermore, its non-invasiveness and flexibility make OpenPose a brilliant option to adopt. As a matter of fact, its performance is probably reduced by the limited hardware used for the experiment; a network with a higher resolution should improve its performance.  
YOLO v4 did not show an exceptional performance, but it has a good variability of D.RMS for dynamic conditions. The main problem of the framework is that it requires being retrained with many pictures of the hand tool under consideration (735 images were used in this study) in order to make the model able to recognize the point/s of interest. This framework would require a deeper investigation exploiting different points of interest for the selected hand tool to be detected and considering a more effective reinforcement learning for the DL model.  
Azure Kinect Body tracking returned the worst results. It is certainly an innovative, compact, lightweight and well-documented framework; its DL model capabilities and accuracy improve with time thanks to continuous support from Microsoft. A study could be designed, in order to reinforce the model performance, on tracking some interesting points of the upper limb for more accurately deriving the hand tool position.