A key problem in the adoption of exoskeletons in industry is that workers are incorrectly fitting the device, leading to discomfort and suboptimal functioning of the exoskeleton. Although biomechanical modeling and design optimization strategies have tried to resolve this issue, we propose a user-centric, real-time fitting aid to guide and control the correct fitting process, which has not been achieved in practice. Inspired by previous work that uses exergame-like feedback to instruct a user, we compared augmented reality (AR)-based visual cues to guide the accurate fitting of a passive upper limb exoskeleton. We selected visual cues through a systematic literature review and evaluated their efficacy and usability for different aspects of exoskeleton fitting in a study with sixteen participants. The study outcome suggests a statistically significant preference for a semi-transparent overlay instead of a more abstract arrow-based method. Moreover, the results indicate high usability and satisfaction with our approach, improved user acceptance, and potentially enhanced fitting accuracy. These findings advance understanding of the viability of exergame-like real-time guidance as a means to increase exoskeleton acceptance and adoption in industrial settings.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.