Augmented Reality (AR) is a promising technology for assistance and training in work environments, as it can provide instructions and feedback contextualised. Not only, but especially impaired workers can benefit from this technology. While previous work mostly focused on using AR to assist or train specific predefined tasks, "general purpose" AR applications, that can be used to intuitively author new tasks at run-time, are widely missing.

In the context of picking and assembly tasks, assistance systems based on Augmented Reality (AR) can help finding target objects and conducting correct actions. The aim is to develop guiding and action assistance techniques for smart glasses, which are easily understandable not only for workers, but also for impaired and elderly people.

An important use-case of augmented reality-based assistance systems is supporting users in search tasks by guiding their attention towards the relevant targets. This has been shown to reduce search time and errors, such as wrongly picked items or false placements. The optimization of attention guiding techniques is thus one area of research in augmented assistance. In this paper, we address the problem of attention guiding in domains with occluded targets. We propose and evaluate a variant of a line-based approach and show that it improves upon two existing approaches in a newly designed evaluation scenario.

The localization of objects or locations in an environment is an essential task relevant for many work processes. An augmented reality (AR)-based assistance system may support users by guiding their attention towards the relevant targets. This will reduce the time needed for visual search and reduce errors, such as wrongly picked items or false placements. The design of proper attention guiding techniques is thus one area of research in augmented assistance. We developed a number of new attention guiding techniques and evaluated them in several experiments together with recent or classic existing techniques in varying picking scenarios. In our current work, we adapted a standardized assembly scenario to a more complex environment including occlusions to provide a scenario supporting reproducibility of our results. In the research demo, a number of visual and acoustic guiding techniques can be tested and combined. The demonstration is implemented on the Microsoft HoloLens.

With the launch of ultra-portable systems, mobile eye tracking finally has the potential to become mainstream. While eye movements on their own can already be used to identify human activities, such as reading or walking, linking eye movements to objects in the environment provides even deeper insights into human cognitive processing.

While display quality and rendering for Head-Mounted-Displays (HMDs) has increased in quality and performance, the interaction capabilities with these devices are still very limited or relying on expensive technology. Current experiences offered for mobile HMDs often stick to dome-like looking around, automatic or gaze-triggered movement, or flying techniques. We developed an easy to use walking-in-place technique that does not require additional hardware to enable basic navigation, such as walking, running, or jumping, in virtual environments. Our approach is based on the analysis of data from the inertial unit embedded in mobile HMDs. In a first prototype realized for the Samsung Galaxy Gear VR we detect steps and jumps. A user study shows that users novice to virtual reality easily pick up the method. In comparison to a classic input device, using our walking-in-place technique study participants felt more present in the virtual environment and preferred our method for exploration of the virtual world.

For validly analyzing human visual attention, it is often necessary to proceed from computer-based desktop set-ups to more natural real-world settings. However, the resulting loss of control has to be counterbalanced by increasing participant and/or item count. Together with the effort required to manually annotate the gaze-cursor videos recorded with mobile eye trackers, this renders many studies unfeasible.

For solving complex tasks cooperatively in close interaction with robots, they need to understand natural human communication. To achieve this, robots could benefit from a deeper understanding of the processes that humans use for successful communication. Such skills can be studied by investigating human face-to-face interactions in complex tasks. In our work the focus lies on shared-space interactions in a path planning task and thus 3D gaze directions and hand movements are of particular interest.