## 1 Introduction and Literature Review  
A wide range of workplace health problems may occur due to work-related musculoskeletal disorders (WMSDs). The possible causes of WMSDs include factors such as (i) the work environment, (ii) types of activities and (iii) occupational posture [1]. Such disorders may cause inflammatory or degenerative conditions of the body's functional structures, such as nerves, tendons, ligaments and muscles [2]. Several authors have shown that WMSDs are a major cause of injury in modern industries, leading to an overall loss of productivity in developed countries [3,4]. The core of workplace ergonomics, occupational health and safety (OHS) programs, identify these sources of injury as ergonomic risk factors [5]. In many industrialized countries, mechanical overload, repetitive work and prolonged non-ergonomic postures are widely recognized as risk factors for upper limb and lumbar spine injuries [6,7]. Therefore, a robust tool for estimating and monitoring workers' posture may be critical for the prevention of musculoskeletal disorders. Such a tool could be used to assess imbalances between workplace requirements and the capabilities of workers to prevent WMSDs. This goal can be achieved using observational techniques. Due to its simplicity and efficiency, Rapid Upper Limb Assessment (RULA) is one of the observational procedures most used by safety/ergonomic professionals in industrial settings [6; 8; 9]. The main reason for this lies in the possibility of performing quick and reliable assessments of the upper body [10; 11]. Thus, based on the angle of the worker's joints, the grand score, or overall numerical score [1], reflects the degree of postural load on a musculoskeletal system. This is ascertained based on the relative locations of the parts of the body. A final score is determined using specific algorithms and may be used to appraise potential dangers associated with an activity. However, RULA and other observational techniques have two significant drawbacks. First, experienced assessors are required, so these methods may not be the most cost-effective option. Second, the final score may be subjected to inconsistency brought about by the subjectivity of the evaluators [12]. Therefore, since these methods rely on observational techniques that require an ergonomic analyst to perform observation of the work in real time or from a recorded video [13], they can be affected by human error, producing results with low consistency and repeatability. These limits can be reduced or eliminated using advanced technologies [14; 15]. Specifically, automated data collection and analysis may be possible using a new class of data-driven applications. In this context, technological advancements in hardware sensors and machine learning (ML) offer new opportunities for ergonomics. From this perspective, refs. [16; 17] used inclinometers and accelerometers in their study, while [18] used simple RGB color cameras. The authors of [19] used sensors and video capture methods to observe human operators and gather information during a task and a process and for use in error analysis. Yet, these intrusive direct-measurement and wearable technologies may limit or impact the development of work activities [18; 20]. Despite these potential limits, modern technologies based on computer vision (CV) and machine learning (ML) enable the accurate recognition and analysis of human posture. This type of analysis can help in identifying ergonomic observation-based risk and choosing the relevant assessment methods. In this research area, a number of authors [15; 21; 22; 23] have used CV systems as color and depth devices (RGB-D) to analyze ergonomics. However, up-to-date CV-based approaches do not yet meet the requirements for the correct management of complexities associated with real-world environments, such as uneven lighting and occlusion [25; 26].