## 5 Discussion  
Unlike previous vision-based methods [22], our proposed method enables ergonomic assessments at the joint level, which can be more accurate and beneficial for specific corrective actions. The reason for this lies in the new method developed for the analysis of postural data. This study used a temporal convolutional model that takes 2D keypoint sequences as the input and produces 3D human pose estimations as the output. This convolutional model allows for the parallel processing of multiple frames, unlike recurring networks. The benefits of this work include (i) the non-intrusive collection of posture data and performance of ergonomic risk assessment without interfering with the normal activities of workers; (ii) the provision of 3D posture data instead of 2D posture data, so that joint angles can be measured accurately; and (iii) the adaptability of the model to different 2D keypoint detectors and effective management of large-scale scenarios through dilated convolutions. This research resulted from industrial activities aimed at developing a framework for the automated estimation of human poses in workplaces. The proposed model was developed through computer vision and machine learning techniques to produce cobots that can  
Figure 10: (a) Reconstruction of 3D Human Pose from 2D Image. (b) Reconstruction of 3D Human Pose from 2D Image. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ergonomic Parameters**} \\ \hline  & **Right Elbow** & **Right Shoulder** & **Neck Bending** & **Spine** & **Repetition of the Same Movements** \\ \hline Range According & & & & & \\ to RULA & (0\({}^{\circ}\)–45\({}^{\circ}\)) & (20\({}^{\circ}\)–90\({}^{\circ}\)) & (0\({}^{\circ}\)–20\({}^{\circ}\)) & (0\({}^{\circ}\)–60\({}^{\circ}\)) & (From 25\% to 80\%) \\ \hline Elementary & Normalized to the & Normalized to the & Normalized to the & Normalized to the & Normalized to the & Maximum Value \\ Operation & Maximum Value & Maximum Value & Maximum Value & Maximum Value \\ \hline EO\({}_{\text{i}}\) in Figure 10a & 0.438 & 0.977 & 0.596 & 0.272 & 0.158 \\ \hline EO\({}_{\text{i}}\) in Figure 10b & 0.497 & 0.791 & 0.475 & 0.103 & 0.354 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ergonomic assessment. support production and safety managers in preventing workplace health problems due to WMSDs, with the goal of improving production capacity and decreasing ergonomic risk. In the case study, we chose to analyze a task in which the assembly of the semi-finished product is mainly manual, consisting of numerous elementary operations, and whose analysis allowed us to identify which tasks should be assigned to the collaborative robot. This task is generally performed by an operator with over 10 years of experience; a comparison of ergonomic and productivity terms was therefore carried out considering the use of the cobot alongside an expert worker, while also taking into account that the benefit that this solution brings in terms of reducing stress and increasing the number of pieces completed must be related to the following:  
The need to modify the layout of the workstation and the entire line;  
The need to adopt specific semi-finished containers for handling by the cobot;  
The costs of purchasing and programming the cobot and training the operator to interact with it. From a managerial perspective, this study and its activities addressed the effect of a collaborative robot on process flow, supporting the company in evaluating the impact of ergonomic risk and productivity within a single framework. The developed research methodology was also validated for another workstation. Although this research has shown significant results, it has certain limitations. The first limitation pertains to workplace analysis when there are interactions between several human operators, or if there are objects that may partially occlude the area to be analyzed.