## 2 Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle Name** & **Acronym** & **Points of the Skeleton Joints Involved** \\ \hline Left elbow & EL & \textless{}13, 12, 11 \\ Right elbow & ER & \textless{}14, 15, 16 \\ Left shoulder & SL & \textless{}12, 11, 08 \\ Right shoulder & SR & \textless{}15, 16, 08 \\ Left clavicle & CL & \textless{}11, 08, 07 \\ Right clavicle & RC & \textless{}14, 08, 07 \\ Left knee & KL & \textless{}04, 05, 06 \\ Right knee & KR & \textless{}01, 02, 03 \\ Neck twisting & NT & \textless{}10, 09 08 \\ Neck bending left & NB & \textless{}09, 08, 11 \\ Neck bending right & NBR & \textless{}09, 08, 14 \\ Neck flexion & NF & \textless{}09, 08, 00 \\ Trunk twisting right & TT & \textless{}11, 00, 04 \\ Trunk twisting left & TTL & \textless{}14, 00, 04 \\ Trunk Bending & TB & \textless{}04, 00, 07 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RULA joint angles from 17 skeleton data. Points are numbered according to Figure 2.  
Figure 2: Kinematic representation of the human body by 17 keypoints. The model used in this study is a 17-joint skeleton, and the skeletal keypoints are listed as joint angles in Table 1 and in skeleton form in Figure 2. For each joint, VideoPose 3D provides (i) a vector with its relative position in the image and (ii) the confidence of the estimation, ranging from 0 (null) to 1 (complete). From this information, we calculate the overall confidence of skeletal detection as an average of the confidence of the joint estimates, which will be used for filtering out noisy or spurious detections. An example of this step is given in Table 1. The left elbow angle (EL) is calculated from the positions of the left shoulder, elbow and wrist, which correspond to skeleton joints #11, #12 and #13. To compute the ergonomic risk value, the threshold values of the joint angles per skeleton must be described. These thresholds are explicit for some joint angles, considering the RULA method (e.g., elbows and neck), but not for others. Thus, to define these threshold values, the approach of [14; 40] is used. The results are shown in Table 2.  
Step 3. The total criticality indices  
This portion of the study explores the uses of a FIE to calculate the total criticality indices, considering some ergonomic indicators. From the literature, it can be determined that fuzzy logic allows us to simulate complicated processes and solve problems with qualitative, vague or uncertain information [41]. Recently, there have been several applications of this methodology in the field of safety and risk analysis, such as system reliability and risk assessment [42; 43; 44] and the analysis of human reliability [45; 46; 47; 48]. The authors of [49] use this methodology to assess the risk of human error, and those of [50] propose a framework based on fuzzy logic to address the inaccuracy of input data regarding ergonomic evaluation due to human subjectivity in field observation. A further ergonomic assessment based on fuzzy logic is proposed in [51], with the aim of assessing and defining the level of risk in manual load-handling and the severity of the impact on workers' health. Therefore, in this study, we use this methodology to determine the total criticality index. A fuzzy system consists of four basic units: a knowledge base and three computational units (fuzzification, inference and defuzzification).