## 1 Introduction
However, to perform realistic and reliable simulations, they require accurate information related to the kinematic of the worker's body (posture) [19]. Motion capture systems can be used to collect such data accurately and quantitatively. However, the most reliable systems commercially available, i.e., motion capture sensor-based (e.g., Xsense [20], Vicon Blue Trident [21]) and marker-based optical systems (e.g., Vicon Nexus [22], OptiTrack [23]) have important drawbacks, so that their use in real work environments is still scarce [9]. Indeed, they are expensive in terms of cost and setup time and have a limited application in a factory environment due to several constraints, ranging from lighting conditions to electro-magnetic interference [24]. Therefore, their use is currently limited to laboratory experimental setups [25], while they are not easy to manage on the factory shop floor. In addition, these systems can also be intrusive as they frequently require the use of wearable devices (i.e., sensors or markers) positioned on the workers' bodies according to proper specification [25] and following specific calibration procedures. These activities require the involvement of specialized professionals and are time consuming, so it is not easy to carry them out in real working conditions, on a daily basis. Moreover, marker-based optical systems greatly suffer from occlusion problems and need the installation of multiple-cameras, which could rarely be feasible in a working environment where space is always limited, and so they cannot be optimally placed. In the last few years, to overcome these issues, several systems based on computer vision and machine learning techniques have been proposed. The introduction on the market of low-cost body-tracking technologies, based on RGB-D cameras, such as the Microsoft Kinect(r), has aroused great interest in many application fields such as: gaming and virtual reality [26], healthcare [27; 28], natural user interfaces [29], education [30] and ergonomics [31; 32; 33]. Being an integrated device, it does not require calibration. Several studies evaluated its accuracy [34; 35; 36] and evaluated it in working environments and industrial contexts [37; 38]. Their results suggest that Kinect may successfully be used for assessing the risk of the operational activities, where very high precision is not required, despite errors depending on the performed posture [39]. Since the acquisition is performed from a single point of view, the system suffers occlusion problems, which can induce large error values, especially in complex motions with auto-occlusion or if the sensor is not placed in front of the subject, as recommended in [36]. Using multiple Kinects can only partially solve these problems, as the quality of the depth images degrades with the number of Kinects concurrently running, due to IR emitter interference problems [40]. Moreover, RGB-D cameras are not as widely and cheaply acceptable as RGB, and their installation and calibration on the workspace is not a trivial task, because of ferromagnetic interference that can cause significant noise in the output [41]. In a working industrial environment, having motion capture working with standard RGB sensors (such as those embedded in smartphones or webcams) can represent a more viable solution. Several systems have been introduced in the last few years to enable real-time human pose estimation from video streaming provided by RGB cameras. Among them, OpenPose, developed by researchers of the Carnegie Mellon University [42; 43], represents the first real-time multi-person system to jointly detect the human body, hands, face, and feet (137 keypoints estimation per person: 70-keypoints face, 25-keypoints body/foot and 2x21-keypoints hand) on a single image. It is an open-source software, based on Convolutional Neural Network (CNN) found in the OpenCV library [44] initially written in C++ and Caffe, and freely available for non-commercial use.