## 1 Introduction  
Nowadays, reducing the risk of musculoskeletal diseases (MSDs) for workers of manufacturing industries is of paramount importance to reduce absenteeism on work, due to illnesses related to bad working conditions, and to improve process efficiency in assembly lines. One of the main goals of Industry 4.0 is to find solutions to put workers in human-suitable working conditions, improving the efficiency and productivity of the factory [1]. However, we are still far from this goal, as shown in the 2019 European Risk Observatory Report [2]: reported working-related MSDs are decreasing but remain too high (58% in 2015 against 60% in 2010), and if we consider the working-class aging, these reports can only get worse. According to the European Commission's Ageing Report 2015 [3], the employment rate of people over-55 will reach 59.8% in 2023 and 66.7% by 2060, because many born during the \"baby boom\" are getting older, life expectancy and retirement age are increasing, while the birth rate is decreasing. Solving this problem is of extreme importance. Working demand usually does not change with age, while this cannot be said for working capacity: in aging, physiological changes in perception, information processing and motor control reduce work capacity. The physical work capacity of a 65-year-old worker is about half that of a 25-year-old worker [4]. On the other hand, age-related changes in physiological function can be dampened by various factors including physical activity [5], so work capability is a highly individual variable. In this context, industries will increasingly need to take the human variability into account and to predict the workers' behaviors, going behind the concept of \"the worker\" as a homogeneous group and monitoring the specific work-related risks more accurately, to implement more effective health and safety management systems to increase factory efficiency and safety. To reduce ergonomic risks and promote the workers' well-being, considering the characteristics and performance of every single person, we need cost-effective, robust tools able to provide a direct monitoring of the working postures and a continuous ergonomic risk assessment along with the work activities. Moreover, we need to improve the workers' awareness about ergonomic risks and define the best strategies to prevent them. Several studies in ergonomics suggested that providing workers with ergonomic feedback can positively influence the motion of workers and decrease hazardous risk score values [6; 7; 8]. However, this goal still seems far from being achieved due to the lack of low-cost, continuous monitoring systems to be easily applied on the shop floor. Currently, ergonomic risk assessment is mainly based on postural observation methods [9; 10], such as: National Institute for Occupational Safety and Health (NIOSH) lifting equation [11], Rapid Upper Limb Assessment (RULA) [12], Rapid Entire Body Assessment (REBA) [13], Strain Index [14], and Occupational Repetitive Action (OCRA) [15]. They require the intervention of an experienced ergonomist who observed workers' actions, directly or by means of video recordings. Data collection required to compute the risk index is generally obtained through subjective observation or simple estimation of projected joint angles (e.g., elbow, shoulder, knee, trunk, and neck) by analyzing videos or pictures. This ergonomic assessment results to be costly and time-consuming [9], highly affects the intra- and inter-observer results variability [16] and may lead to low accuracy of such evaluations [17]. Several tools are available to automate the postural analysis process by calculating various risk indices, to make ergonomic assessment more efficient. They are currently embedded in the most widely used computer-aided design (CAD) packages (e.g., CATIAL-DELMIA by Dassault Systemes, Pro/ENGINEER by PTC Manikin or Tecnomatix/Jack by Siemens) and allow detailed human modeling based on digital human manikins, according to an analytical ergonomic perspective [18].