Measuring the Impact of Objects' Physicalization, Avatar Appearance, and their Consistency on Pick-and-Place Performance in Augmented Reality

Illustration of a user holding a can under different conditions of objects physicalization and hand appearance studied in this research. In (a) and (b) the user holds a real can, while in (c) and (d) they holds a virtual -non-physicalized -can. Moreover, in (a) and (c) the hand is displayed with its real appearance, while in (b) and (d) it is overlapped with a virtual avatar.

Abstract-Augmented Reality (AR) is a growing technology that enables interaction with both virtual and real objects. However, in order to support the future development of efficient and usable AR interactions, there is still a lack of systematic knowledge establishing basic interaction performance across different conditions. Therefore, in this paper, we report a user study measuring the impact of objects' physicalization (object's set composed of (i) virtual, (ii) real, or (iii) a composite mix of real and virtual objects) and hand appearance (hand's appearance displayed as (i) the real hand, (ii) an avatar, or (iii) dynamically adapting to the surrounding objects' physicalization) on the speed performance of a pick-and-place task. Overall, our results reveal that objects' physicalization plays a significant role in interaction performance, with the more real objects in a set the better the performance. Moreover, our results also suggest that pick-and-place interaction performances are mostly unaffected by the hand appearance. Interestingly, we also observed that interactions with real objects were less efficient as the object condition required the user to alternate between interactions with virtual and real objects (object condition (iii)), which provides novel insights into an important -mostly AR-specific -factor to consider for designing future AR interactions. Taken together, our results provide a rich characterization of different factors influencing different phases of a pick-and-place interaction, which could be employed to improve the design of future AR applications.