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Collaborative robots or co-bots are a category of robots that are designed to work together with humans. By combining the strength of the robot such as precision and strength with the dexterity and problem-solving ability of the human, it is possible to achieve tasks that cannot be fully automated and improve the production quality and working conditions of workers. This paper presents the results of the ClaXon project which aims to study and implement interactions between humans and collaborative robots in factories. The project has led to the integration of a co-bot in the car manufacturing production plant of Audi Brussels in Belgium. Proofs of concepts were realized to study multimodal perceptions for human-robot interaction. The project addressed technical challenges regarding the introduction of collaborative robots on the factory floor. Social experiments were conducted with factory workers to assess the social acceptance of co-bots and study the interactions between the human and the robot.

Shoulder exoskeletons potentially reduce overuse injuries in industrial settings including overhead work or lifting tasks. Previous studies evaluated these devices primarily in laboratory setting, but evidence of their effectiveness outside the lab is lacking. The present study aimed to evaluate the effectiveness of two passive shoulder exoskeletons and explore the transfer of laboratory-based results to the field. Four industrial workers performed controlled and in-field evaluations without and with two exoskeletons, ShoulderX and Skelex in a randomized order. The exoskeletons decreased upper trapezius activity (up to 46%) and heart rate in isolated tasks. In the field, the effects of both exoskeletons were less prominent (up to 26% upper trapezius activity reduction) while lifting windscreens weighing 13.1 and 17.0 kg. ShoulderX received high discomfort scores in the shoulder region and usability of both exoskeletons was moderate. Overall, both exoskeletons positively affected the isolated tasks, but in the field the support of both exoskeletons was limited. Skelex, which performed worse in the isolated tasks compared to ShoulderX, seemed to provide the most support during the in-field situations. Exoskeleton interface improvements are required to improve comfort and usability. Laboratorybased evaluations of exoskeletons should be interpreted with caution, since the effect of an exoskeleton is task Manuscript

Objective The aim of this study is to test the unified theory of acceptance and use of technology (UTAUT) model for explaining the intention to use exoskeletons among industrial workers. Background Exoskeletons could help reduce physical workload and risk for injuries among industrial workers. Therefore, it is crucial to understand which factors play a role in workers’ intention to use such exoskeletons. Method Industrial workers ( N = 124) completed a survey on their attitudes regarding the use of exoskeletons at their workplace. Using partial least squares (PLS) path modeling, the UTAUT model and a revised version of the UTAUT model were fitted to these data. Results The adapted UTAUT model of Dwivedi et al. (2017) was able to explain up to 75.6% of the variance in intention to use exoskeletons, suggesting a reasonable model fit. Conclusion The model fit suggests that effort expectancy (how easy it seems to use an exoskeleton) plays an important role in predicting the intention to use exoskeletons. Social influence (whether others think workers should use exoskeletons) and performance expectancy (how useful exoskeletons seem to be for work) play a smaller role in predicting the intention to use. Applications This research informs companies about the optimal implementation of exoskeletons by improving the determinants of acceptance among their workers.

Quality of Experience (QoE) has recently gained recognition for being an important determinant of the success of new technologies. Despite the growing interest in QoE, research into this area is still fragmented. Similar -but separate -efforts are being carried out in technical as well as user oriented research domains, which are rarely communicating with each other. In this paper, we take a multidisciplinary approach and review both user oriented and technical definitions on Quality of Experience (including the related concept of User Experience). We propose a detailed and comprehensive framework that integrates both perspectives. Finally, we take a first step at linking methods for measuring QoE with this framework.

OBJECTIVE The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique. METHODS Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality. RESULTS The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63). CONCLUSIONS Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.