Collaborative robots (cobots) are a category of robots designed to work together with humans. By combining the fortes of the robot, such as precision and strength, with the dexterity and problem-solving ability of the human, it is possible to accomplish tasks that cannot be fully automated and improve the production quality and working conditions of employees [6], [16]. This article presents the results of the ClaXon project, which studies and implements interactions between humans and cobots in factories. The project has led to the integration of a cobot in the Audi car manufacturing plant in Brussels, Belgium.

Background: Populations globally are ageing, resulting in higher incidence rates of chronic diseases. Digital health platforms, designed to support those with chronic conditions to self-manage at home, offer a promising solution to help people monitor their conditions and lifestyle, maintain good health, and reduce unscheduled clinical visits. However, despite high prevalence rates of multimorbidity or multiple chronic conditions, most platforms tend to focus on a single disease. A further challenge is that despite the importance of users actively engaging with such systems, little research has explored engagement. Objective: The objectives of this study are to design and develop a digital health platform, ProACT, for facilitating older adults self-managing multimorbidity, with support from their care network, and evaluate end user engagement and experiences with this platform through a 12-month trial. Methods: The ProACT digital health platform is presented in this paper. The platform was evaluated in a year-long proof-of-concept action research trial with 120 older persons with multimorbidity in Ireland and Belgium. Alongside the technology, participants had access to a clinical triage service responding to symptom alerts and a technical helpdesk. Interactions with the platform during the trial were logged to determine engagement. Semistructured interviews were conducted with participants and analyzed using inductive thematic analysis, whereas usability and user burden were examined using validated questionnaires. Results: This paper presents the ProACT platform and its components, along with findings on engagement with the platform and its usability. Of the 120 participants who participated, 24 (20%) withdrew before the end of the study, whereas 3 (2.5%) died. The remaining 93 participants actively used the platform until the end of the trial, on average, taking 2 or 3 health readings daily over the course of the trial in Ireland and Belgium, respectively. The participants reported ProACT to be usable and of low burden. Findings from interviews revealed that participants experienced multiple benefits as a result of using ProACT, including improved self-management, health, and well-being and support from the triage service. For those who withdrew, barriers to engagement were poor health and frustration when technology, in particular sensing devices, did not work as expected. Conclusions: This is the first study to present findings from a longitudinal study of older adults using digital health technology to self-manage multimorbidity. Our findings show that older adults sustained engagement with the technology and found it usable. Potential reasons for these results include a strong focus on user-centered design and engagement throughout the project lifecycle, resulting in a platform that meets user needs, as well as the integration of behavior change techniques and personal analytics into the platform. The provision of triage and technical support services alongside the platform during the trial were also important facilitators of engagement. International Registered Report Identifier (IRRID): RR2-10.2196/22125

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. 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. 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. 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. 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. This research informs companies about the optimal implementation of exoskeletons by improving the determinants of acceptance among their workers.

We study the perception of speckle by human observers in a laser projector based on a 40 persons survey. The speckle contrast is first objectively measured making use of a well-defined speckle measurement method. We statistically analyse the results of the user quality scores, revealing that the speckle perception is not only influenced by the speckle contrast settings of the projector, but it is also strongly influenced by the type of image shown. Based on the survey, we derive a speckle contrast threshold for which speckle can be seen, and separately we investigate a speckle disturbance limit that is tolerated by the majority of test persons.