## 2 Methods  
This study attempts to answer some basic questions about postural assessment based on AI in conjunction with a collaborative robot (cobot). According to [34], the ever-increasing implementation of collaborative robotics in manufacturing companies has led to changes in some of the operator activities they support, with effects on the outcomes of ergonomic assessments. Our first research question aims to assess the impact of cobots as a possible solution to reducing ergonomic stress in workers. RQ1: What are the comparative advantages and limitations of using a collaborative robot (cobot) to mitigate ergonomic stress in high-risk elementary operations (EOi)? The second research question aims to investigate the overall potential impact of cobots on the performance of production lines. This was carried out using a collaborative robot in a real case study. RQ2: How does the implementation of cobots impact production capacity, worker productivity and ergonomic stress levels in manufacturing environments? Starting with a 3D reproduction of the human skeleton with 17 keypoints, the joint angles were computed. Then, a criticality index (Ic) was defined to (i) measure the ergonomic stress of each operation conducted by the worker and (ii) define the impact on the ergonomic assessment of each upper-body quadrant. The second part of this study explores the use of a collaborative robot to reduce and/or eliminate those elementary operations (EOis) with the highest criticality indices. The research methodology was divided into four phases according to the structure shown in Figure 1. The first phase identifies all the EOi's and defines the domains of ergonomic analysis. In the second phase, a novel method of assessing ergonomic risk is explored in which the 3D human pose is analyzed through computer vision and machine learning techniques. In the third step, we perform a criticality analysis for the EOi's through RULA combined with a fuzzy inference engine (FIE). This tool computes the total criticality index (IcTOT) for each EOi. In the fourth step, the criticality classes are calculated. In this step, we explore the use of a cobot for those EOi's whose indices fall into the highest criticality classes. The last portion of this study provides a new risk assessment method that assesses the ability of the collaborative robot in the operation to reduce the criticality classes for those EOi's. The main steps are as follows:  
* Worker activities are divided into EOi's [36]. These EOi's define the input of the ergonomic analysis. * A system based on artificial vision is introduced for the estimation of human poses in 3D; this outperforms some methods evaluated in the literature in which the estimation  
Figure 1: Research methodology. of posture scores in the workplace is evaluated in posture images by detecting the 2D coordinates of the body joints, including the wrist joints [1; 37]. * The total criticality indices for the EOiS are balanced through a fuzzy interface. These indices summarize the workers' ergonomic stress during manufacturing operations. * A cobot is implemented for elementary operations with higher criticality classes. Step 1. EOi Identification In this phase, elementary operations [38] are identified through a video recording of production activities during a work shift [36]. The main goal is to analyze the EOiS to determine the joint angles, while also considering the cyclic and non-cyclic operations conducted within the cycle time. In this phase, repetitive operations are also considered, as they can cause ergonomic problems [39]. Step 2. Human Pose Assessment The 3D CV is explored to perform an ergonomic risk assessment for each EOi. Using the approach presented in VideoPose3D [33], the keypoints of the human body are detected from digital images or videos. VideoPose3D uses a system based on the dilated temporal convolution of 2D keypoint trajectories to estimate 3D keypoint coordinates. Given an input image or a video, the network provides a list of detected keypoints, as shown in Table 1 and Figure 2.