The aim of this article is to know the impact that the different Industry 4.0 technologies have on occupational health and safety risks, with special attention to the new emerging risks generated. To achieve this objective, an analysis of the literature was carried out. It allowed us to design a survey that was answered by 130 managers and/or technicians of pioneering companies in the development of Industry 4.0 technologies. Next, 32 of these projects were selected and a multiple case study was conducted through 37 in-depth interviews. Moreover, other source of information were analysed (project reports, technical reports, websites..). The findings highlight that the analysed technologies (Additive Manufacturing, Artificial Intelligence, Artificial Vision, Big Data and/or Advanced Analytics, Cybersecurity, Internet of Things, Robotics and Virtual and Augmented Reality) help to reduce occupational health and safety risks (physical and mechanical). However, its impact depends on the type of technology and the method of application. Influences in new emerging risks (mainly psychosocial and mechanical) have been detected in all technologies except in Internet of Things. In addition, additive manufacturing, artificial intelligence, machine vision, the internet of things, robotics and virtual and augmented reality help to reduce ergonomic risks and artificial intelligence, big data and cybersecurity psychosocial risks. The results obtained have implications for policy makers, managers, consultants and those in charge of managing occupational health and safety risks in industrial companies.

Virtual facebow record techniques typically record the relationship of a maxillary digital scan to facial landmarks by aligning it to a 3-dimensional face scan. Three-dimensional face scans can be acquired with different facial scanning methods, but the impact of the facial scanning method on the accuracy (trueness and precision) of a virtual facebow record technique remains unclear. The purpose of this in vivo study was to assess the impact of the facial scanning method on the precision under the repeatability conditions (repeatability) of a virtual facebow record technique. Repeatability of the virtual facebow record technique with the following 3 clinical-grade facial scanning methods was determined and compared: a professional handheld scanner based on structured blue light scanning technology (PHS method); an attachment-type 3-dimensional sensor camera connected to a tablet and controlled with a mobile application (3DSC-T method); and a smartphone with an integrated 3-dimensional sensor camera controlled with a mobile application (3DSC-S method). To determine the repeatability of the virtual facebow record technique with each facial scanning method, 8 virtual facebow records of a completely dentate adult with class I occlusion and mesoprosopic facial form were obtained (8×3=24 in total); with these, 8 locations of a maxillary digital scan with respect to a common 3-dimensional face scan were obtained. Repeatability was determined in terms of deviations between located maxillary digital scans, determined, in turn, by calculating the distances between corresponding vertices for each of the possible nonrepeating combinations of pairs of located maxillary digital scans (⁸C₂=28). Finally, the repeatability of the virtual facebow record technique with the different facial scanning methods was compared by using the Welch ANOVA test and the post hoc Games-Howell test (both α=.05). The repeatability of the virtual facebow record technique with PHS, 3DSC-T, and 3DSC-S facial scanning methods resulted in 0.243 ±0.094 mm, 0.437 ±0.171 mm, and 1.023 ±0.399 mm, respectively. Comparison of these results revealed that the facial scanning method had a statistically significant effect on the repeatability of the virtual facebow record technique (P<.001) and that its repeatability was statistically significantly greater with the PHS facial scanning method than with the 3DSC-T and 3DSC-S facial scanning methods and greater with the 3DSC-T facial scanning method than with the 3DSC-S facial scanning method (P<.001 for all pairwise comparisons). This study found that the facial scanning method had a great impact on the repeatability of the virtual facebow record technique and that the virtual facebow record technique was more repeatable with more accurate facial scanning methods.