## 1 Introduction and Literature Review
Recently, several authors conducted studies that required workers to be confined to typical situations, such as changes in outside light and being constrained to a limited range of movement. These authors suggested that changes in the camera's viewing angle affected the accuracy and precision of the results [12; 15; 25; 27]. These studies, regarding the automation of the posture assessment task, required additional equipment and were difficult to adapt to general industry [15; 28; 29; 30]. A number of approaches use computer vision algorithms to assess postural risk and forecast RULA scores. In this context, Convolutional Neural Networks (CNNs) were used in [28] to predict kinematic data based on images and a network. In this approach, the output provided by the RULA score was also classified. In a study on WMSD risk prediction, ref. [14] compared the most widely used supervised machine learning classifiers, such as the Random Forest algorithm, the Naive Bayes classifier, the Decision Tree algorithm, the K-Nearest Neighbors algorithm and Neural Networks (NNs). As previously stated, in addressing posture assessment problems, CV approaches may grant better measurement accuracy and have smaller impacts on workers and working environments [16; 17; 18]. The CV approach allows us to more accurately appraise posture and obtain measurements of body joints than wearable sensors. This is due to the possibility of accurately identifying the sizes of the body's parts and their proportions and locating the body in the environment. If using wearable sensors, the distance between the joints would have to be estimated, which would lead to a greater error. The CV approach also leads to smaller impacts on workers in cases of technology upgrades or environmental changes. In CV, the goal of human posture assessment is to identify the joints in the human body (knees, elbows, shoulders, etc.) in a digital image, and then, to search for a specific pose that matches the observed joint from a selection of possible joint postures. The use of artificial intelligence tools such as CNNs has increased the robustness of these methods. In this research area, there here have been several advances regarding the estimation of human poses, especially from 2D images, based on data collected on a large scale and deep learning techniques. Yet, the performance of the 3D human pose estimation remains unsatisfactory due to the lack of in situ 3D datasets. The authors of [31; 32] proposed an algorithm for fusing multi-viewpoint video (MVV) with inertial measurement unit (IMU) sensor data to accurately estimate 3D human poses. Modern open-source software tools, such as OpenPose [9], allow for real-time joint and limb detection from digital images and videos. OpenPose is a bottom-up approach to estimating the poses of multiple individuals; it takes an entire image as the input to a two-branch CNN to jointly produce confidence maps for predicting the associations between body part detection and the fields of affinity of each party. Given an image as the input, the network returns a list of detected bodies, each with its own skeleton of previously defined joints. Several works, such as [33], in which the authors use VideoPose3D, have enriched the research of posture assessment using 3D estimation models that employ pose estimation to achieve a more realistic and complete skeleton keypoint representation, enabling the application of such models in many domains, including industrial production. In recent years, collaborative robots have become increasingly popular in the manufacturing industry and represent a solution to reducing risks [34]. Within a production system implementing collaborative robots, human operators can be flexibly supported in their physical workloads and different tasks. The combination of the agility and cognitive abilities of human operators and the repeatability and load capacity of robots can have a positive impact on productivity, flexibility, safety and costs [35]. The rest of this paper is organized as follows.