In our daily lives, we are exposed to various stressors, which, even in mild forms, can lead to serious health issues when prolonged. Dance Movement Therapy (DMT) uses bodily movement as psychotherapy to improve health and well-being; however, traditional DMT requires therapists, specific setups, and physical props, limiting daily accessibility. To address this, we conducted a formative study with 10 DMT experts and developed ImmersiveDMT, a VR application that provides real-time visual and auditory feedback in response to users’ movements. A user study with 56 participants examined how visual, auditory, and audiovisual feedback affect expressive movement and stress reduction compared to a non-responsive immersive baseline. Results showed that auditory and audiovisual conditions induced significantly more expressive movement compared to baseline. Both visual and auditory feedback achieved significant stress reduction below baseline (V: 17.1%, p=.004; A: 16.6%, p=.042), while combined audiovisual feedback led to less stress reduction. This research presents a novel approach to accessible daily stress management by integrating VR and DMT, demonstrating new possibilities for self-directed mental health support.

While commercial metaverse platforms offer diverse usergenerated content, they lack effective navigation assistance that can dynamically adapt to users' interests and intentions. Although previous research has investigated on-demand agents in controlled environments, implementation in commercial settings with diverse world configurations and platform constraints remains challenging. We present Navigation Pixie, an on-demand navigation agent employing a loosely coupled architecture that integrates structured spatial metadata with LLM-based natural language processing while minimizing platform dependencies, which enables experiments on the extensive user base of commercial metaverse platforms. Our cross-platform experiments on commercial metaverse platform Cluster with 99 PC client and 94 VR-HMD participants demonstrated that Navigation Pixie significantly increased dwell time and free exploration compared to fixed-route and noagent conditions across both platforms. Subjective evaluations revealed consistent on-demand preferences in PC environments versus context-dependent social perception advantages in VRHMD. This research contributes to advancing VR interaction design through conversational spatial navigation agents, establishes cross-platform evaluation methodologies revealing environment-dependent effectiveness, and demonstrates empirical experimentation frameworks for commercial metaverse platforms.

Unexpected robot encounters in public spaces can cause discomfort for third parties, yet the acceptability of accompanying robots is not yet fully understood. Visual cues strongly influence robot impressions; however, the interaction between explicit visual indicators and individual robot resistance in determining acceptability remains unexplored. We investigated how visual relationship indicators—connection visibility (the physical linkage between the robot and handler) and control visibility (the evident authority of the handler)—influence acceptance based on individuals’ levels of robot resistance. In our experiment, 23 participants encountered a mobile robot under three operation methods: Autonomous (no visual indicators), Joystick (only control visibility), and Leash (both connection and control visibility), with participants divided into high-resistance (n=12) and low-resistance (n=11) groups based on their NARS scores. Results indicate that Leash had the highest acceptability, with high-resistance participants showing significant differences across methods and benefiting from explicit visual indicators, unlike low-resistance participants who were largely unaffected. These findings offer important design implications for accompanying robots in public spaces, suggesting that employing visually explicit relationship indicators is an effective strategy for enhancing acceptability, particularly among individuals with robot resistance.