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Research has identified applications of handheld-based VR, which utilizes handheld displays or mobile devices, for developing systems that involve users in mixed reality (MR) without the need for head-worn displays (HWDs). Such systems can potentially accommodate large groups of users participating in MR. However, we lack an understanding of how group sizes and interaction methods affect the user experience. In this paper, we aim to advance our understanding of handheld-based MR in the context of multiplayer, co-located games. We conducted a study (N = 38) to understand how user experiences vary by group size (2, 4, and 8) and interaction method (proximity-based or pointing-based). For our experiment, we implemented a multiuser experience for up to ten users. We found that proximity-based interaction that encouraged dynamic movement positively affected social presence and physical/temporal workload. In bigger group settings, participants felt less challenged and less positive. Individuals had varying preferences for group size and interaction type. The findings of the study will advance our understanding of the design space for handheld-based MR in terms of group sizes and interaction schemes. To make our contributions explicit, we conclude our paper with design implications that can inform user experience design in handheld-based mixed reality contexts. 

As the influence of social robots in people’s daily lives grows, research on understanding people’s perception of robots including sociability, trust, acceptance, and preference becomes more pervasive. Research has considered visual, vocal, or tactile cues to express robots’ emotions, whereas little research has provided a holistic view in examining the interactions among different factors influencing emotion perception. We investigated multiple facets of user perception on robots during a conversational task by varying the robots’ voice types, appearances, and emotions. In our experiment, 20 participants interacted with two robots having four different voice types. While participants were reading fairy tales to the robot, the robot gave vocal feedback with seven emotions and the participants evaluated the robot’s profiles through post surveys. The results indicate that (1) the accuracy of emotion perception differed depending on presented emotions, (2) a regular human voice showed higher user preferences and naturalness, (3) but a characterized voice was more appropriate for expressing emotions with significantly higher accuracy in emotion perception, and (4) participants showed significantly higher emotion recognition accuracy with the animal robot than the humanoid robot. A follow-up study ([Formula: see text]) with voice-only conditions confirmed that the importance of embodiment. The results from this study could provide the guidelines needed to design social robots that consider emotional aspects in conversations between robots and users.