The potential of Augmented Reality (AR) to support industrial processes has been demonstrated in several studies. While there have been first investigations on user related issues in the long-duration use of mobile AR systems, to date the impact of theses systems on physiological and psychological aspects is not explored extensively. We conducted an extended study in which 19 participants worked 4 hours continuously in an order picking process with and without AR support. Results of the study comparing strain and work efficiency are presented and open issues are discussed.

In this paper we report on our ongoing studies around the application of augmented reality methods to support the order picking process of logistics applications. Order picking is the gathering of goods out of a prepared range of items following some customer orders. We named the visual support of this order picking process using head-mounted displays ldquopick-by-visionrdquo. This work presents the case study of bringing our previously developed pick-by-vision system from the lab to an experimental factory hall to evaluate it under more realistic conditions. This includes the execution of two user studies. In the first one we compared our pick-by-vision system with and without tracking to picking using a paper list to check picking performance and quality in general. In a second test we had subjects using the pick-by-vision system continuously for two hours to gain in-depth insight into the longer use of our system, checking user strain besides the general performance. Furthermore, we report on the general obstacles of trying to use HMD-based AR in an industrial setup and discuss our observations of user behaviour.

Augmented Reality (AR) uses computer-generated virtual information to enhance the user’s information access. While numerous previous studies have demonstrated the large potential of AR to improve industrial processes by enhancing product quality and reducing production times it is still unclear if and how long term usage of such AR technology produces stress and strain. This paper presents an approach to use the analysis of Heart Rate Variability to objectively measure current user strain during different work tasks. Results of a user study comparing strain during an AR supported and a non-AR supported work task in a laboratory setting are presented and discussed.