## 4 Results Analysis  
After the determination of the criticality class for each elementary operation, certain production parameters were considered within a work shift that allowed us to measure the impact of implementing the cobot on stress resulting from the EO\({}_{i}\) conducted by the operator. The overall ergonomic score (OES) considering all the EO\({}_{i}\)s was determined through the approach of [40]:  
\[OES=\frac{\sum\limits_{i}PES_{i}}{3*\ NUM_{\textit{DOM}}*NUM\_\textit{ELEM\_OPS}}\]  
The elements of the above equation are defined as follows:  
* PES is the sum of each score obtained for a single elementary operation;
* NUM_DOM is the number of selected ergonomic domains;
* NUM_ELEM_OPS is the number of the EO\({}_{i}\)s.  
Figure 8 reports the OESs, normalized with respect to the maximum value, before and after the implementation of the cobot. Another interesting result pertains to the variation in production capacity for each work shift. The implementation of the cobot allows the operator to start assembling a new component while the cobot positions the component in the \"finished products\" box. Therefore, in Figure 9, we compare the production capacity trend before and after cobot implementation. From Figures 8 and 9, we can see that the cobot affects both the total ergonomic score and productive capacity. Therefore, to answer RQ2, the two figures were analyzed in detail, and we note that the OES is reduced by 13%, while production capacity increases from 673 pieces to 900 pieces produced during a work shift. In addition, Figure 8 shows the difference in production for each slot of work. At the beginning of the work shift, there is a strong effect of help from the cobot, but as the operator's stress increases, the difference in pieces produced drops considerably. The production difference in the first slot is 34.3%, while in the last work slot, it decreases by up to 20%. This difference occurs because EO \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elementary Operation** & **Ictot** & **Criticality Class** \\ \hline EO\({}_{1}\) & 0.69 & Medium \\ EO\({}_{2}\) & 0.69 & Medium \\ EO\({}_{3}\) & 0.7 & Medium \\ EO\({}_{4}\) & 0.65 & Medium \\ EO\({}_{5}\) & 0.41 & Medium \\ EO\({}_{6}\) & 0.44 & Medium \\ EO\({}_{7}\) & 0.7 & Medium \\ EO\({}_{8}\) & 0.63 & Medium \\ EO\({}_{9}\) & 0.60 & Medium \\ EO\({}_{10}\) & 0.33 & Low \\ EO\({}_{11}\) & 0.39 & Medium \\ EO\({}_{12}\) & 0.77 & High \\ EO\({}_{13}\) & 0.44 & Medium \\ EO\({}_{14}\) & 0 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 7: New criticality indices after corrective action. has not yet been modified and the operator is subjected to stress, which decreases the production capacity during these time slots. The analyzed case study aimed to test our methodology by considering a real video of a worker during the manual assembly of an electromechanical component. To study the accuracy of our method in evaluating upper quadrant angles, frames were periodically captured. Subsequently, to ensure effective validation of the methodology, an evaluation was carried out considering the frames for a mold-positioning operation performed on a hydraulic press. The same ergonomic parameters were used as the input, and the frames of the EO\({}_{\text{i}}\)s chosen within our research methodology were inserted. As the output, the system returned the human pose in 3D, as shown in Figure 10a,b. Figure 8: OESs before and after cobot implementation. Figure 9: Production capacity for one work shift before and after cobot implementation. In particular, the EO\({}_{\text{i}}\) in Figure 10a concerns the positioning and centering of the mold in the hydraulic press, and the EO\({}_{\text{i}}\) in Figure 10b concerns positioning the metal strip on the tape unwinder and inserting the end of the strip into the tape guide. In addition, the algorithm provides the joint angles calculated considering the worst case based on the human skeleton with the 17 keypoints. Table 8 shows the normalized values of the two EO\({}_{\text{i}}\)s considered.