## 3 Industrial Application
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Right** & **Left** & **Right** & **Left** & **Neck** & **Spine** & **Repetition of the** \\  & **Elbow** & **Elbow** & **Shoulder** & **Shoulder** & **Bending** & **[angles]** & **Same Movements** \\  & **[angles]** & **[angles]** & **[angles]** & **[angles]** & **[angles]** & **[angles]** & **[times]** \\ \hline EO\({}_{1}\) & 102.695 & 97.214 & 68.531 & 69.910 & 26.200 & 98.224 & 239.786 \\ EO\({}_{2}\) & 107.364 & 90.895 & 68.257 & 69.259 & 29.533 & 97.857 & 235.964 \\ EO\({}_{3}\) & 112.442 & 106.222 & 68.574 & 69.125 & 30.267 & 96.867 & 254.074 \\ EO\({}_{4}\) & 110.278 & 102.848 & 64.148 & 58.482 & 27.388 & 93.154 & 297.116 \\ EO\({}_{5}\) & 111.060 & 97.371 & 63.036 & 60.891 & 24.281 & 92.560 & 198.744 \\ EO\({}_{6}\) & 111.554 & 90.522 & 63.151 & 59.894 & 25.081 & 94.731 & 298.116 \\ EO\({}_{7}\) & 104.628 & 96.774 & 63.587 & 61.163 & 27.969 & 95.033 & 245.700 \\ EO\({}_{8}\) & 104.122 & 99.810 & 63.661 & 61.779 & 27.365 & 96.354 & 267.540 \\ EO\({}_{9}\) & 109.308 & 113.187 & 64.225 & 60.825 & 22.259 & 92.860 & 236.054 \\ EO\({}_{10}\) & 84.007 & 95.473 & 67.993 & 59.761 & 15.216 & 95.207 & 253.508 \\ EO\({}_{11}\) & 90.000 & 100.099 & 69.199 & 14.468 & 14.468 & 94.822 & 305.760 \\ EO\({}_{12}\) & 125.132 & 92.035 & 83.918 & 64.064 & 15.285 & 99.599 & 259.820 \\ EO\({}_{13}\) & 108.646 & 92.460 & 68.662 & 61.729 & 13.165 & 96.608 & 278.650 \\ EO\({}_{14}\) & 38.817 & 77.647 & 63.000 & 69.242 & 21.789 & 96.694 & 229.170 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input values of step 3. Figure 6: Reconstruction of 3D Human Pose from 2D Image. The system infers a numeric value for each index based on the output membership function by reading the value on the x-axis corresponding to the assigned membership degree and label. In the example given in Figure 7, only rule #21 is completely satisfied. Furthermore, it is clear that some of the rules are followed at least partially. This occurs when the input's red line intersects the trapezoidal function at one of its slopes. The individual outputs must be summed up to produce a single value. The centroid calculation method is used for the defuzzification step. \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 & (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 \\ Operation & Maximum Value & Maximum Value & Maximum Value & Maximum Value & Maximum Value \\ \hline EO\({}_{1}\) & 0.571 & 0.761 & 0.655 & 0.137 & 0.630 \\ \hline EO\({}_{2}\) & 0.596 & 0.758 & 0.738 & 0.131 & 0.620 \\ \hline EO\({}_{3}\) & 0.625 & 0.762 & 0.757 & 0.114 & 0.670 \\ \hline EO\({}_{4}\) & 0.613 & 0.713 & 0.685 & 0.052 & 0.780 \\ \hline EO\({}_{5}\) & 0.617 & 0.700 & 0.607 & 0.0427 & 0.520 \\ \hline EO\({}_{6}\) & 0.620 & 0.702 & 0.627 & 0.0789 & 0.580 \\ \hline EO\({}_{7}\) & 0.581 & 0.707 & 0.699 & 0.0839 & 0.780 \\ \hline EO\({}_{8}\) & 0.578 & 0.707 & 0.684 & 0.1059 & 0.660 \\ \hline EO\({}_{9}\) & 0.607 & 0.714 & 0.556 & 0.0477 & 0.700 \\ \hline EO\({}_{10}\) & 0.467 & 0.755 & 0.380 & 0.0868 & 0.618 \\ \hline EO\({}_{11}\) & 0.500 & 0.769 & 0.362 & 0.0804 & 0.664 \\ \hline EO\({}_{12}\) & 0.695 & 0.932 & 0.382 & 0.1600 & 0.800 \\ \hline EO\({}_{13}\) & 0.604 & 0.763 & 0.329 & 0.1101 & 0.680 \\ \hline EO\({}_{14}\) & 0.882 & 0.700 & 0.595 & 0.4449 & 0.750 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ergonomic assessment. Figure 7: Rule viewer of the MATLAB Fuzzy Logic Toolbox for EO\({}_{14}\). This methodology allows us to determine the highest ergonomic risk value for each EO\({}_{1}\).