Previous research on eye-hand coordination training systems has investigated user performance on a wall, 2D touchscreens, and in Virtual Reality (VR). In this paper, we designed an eye-hand coordination reaction test to investigate and compare user performance in three different virtual environments (VEs) – VR, Augmented Reality (AR), and a 2D touchscreen. VR and AR conditions also included two feedback conditions – mid-air and passive haptics. Results showed that compared to AR, participants were significantly faster and made fewer errors both in 2D and VR. However, compared to VR and AR, throughput performance of the participants was significantly higher in the 2D touchscreen condition. No significant differences were found between the two feedback conditions. The results show the importance of assessing precision and accuracy in eye-hand coordination training and suggest that it is currently not advisable to use AR headsets in such systems.

Navigation and selection are critical in very large virtual environments, such as a model of a whole city. In practice, many VR applications require both of these modalities to work together. We compare different combinations of two navigation and two selection methods in VR on selection tasks involving distant targets in a user study. The aim of our work is to discover the trade-off between navigation and selection techniques and to identify which combination leads to better interaction performance in large virtual environments. The results showed that users could complete the task faster with the fly/drive method and traveled less, compared to the teleportation method. Additionally, raycasting exhibited a better performance in terms of time and (less) distance traveled, however, it significantly increased the error rate for the selection of targets.

An important challenge in smart environments is how to manipulate the smart objects. Although mobile applications are typically used for controlling a smart environment, no previous study has evaluated the users performance in manipulating smart objects under different environmental complexities. This article presents an experimental comparison between three different selection techniques 3-D, 2-D, and physical user interfaces (UIs). We evaluate these techniques across two levels of environment complexity measuring 51 participants timing data and errors. Our results indicate that the 3-D UI is superior for task completion time and error, and the 2-D UI is not a better solution than the physical UI when the environment is not complex. The results also show the importance of considering the environment complexity in choosing the proper UI.