Search arXivSearch

arXiv · 2502.13421

Virtual Encounters of the Haptic Kind: Towards a Multi-User VR System for Real-Time Social Touch

Abstract

Physical touch, a fundamental aspect of human social interaction, remains largely absent in real-time virtual communication. We present a haptic-enabled multi-user Virtual Reality (VR) system that facilitates real-time, bi-directional social touch communication among physically distant users. We developed wearable gloves and forearm sleeves, embedded with 26 vibrotactile actuators for each hand and arm, actuated via a WiFi-based communication system. The system enables VR-transmitted data to be universally interpreted by haptic devices, allowing feedback rendering based on their capabilities. Users can perform and receive social touch gestures such as stroke, pat, poke, and squeeze, with other users within a shared virtual space or interact with other virtual objects, and they receive vibrotactile feedback. Through a two-part user study involving six pairs of participants, we investigate the impact of gesture speed, haptic feedback modality, and user roles, during real-time haptic communication in VR, on affective and sensory experiences, as well as evaluate the overall system usability. Our findings highlight key design considerations that significantly improve affective experiences, presence, embodiment, pleasantness, and naturalness, to foster more immersive and expressive mediated social touch experiences in VR.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Premankur Banerjee, Jiaxuan Wang, Lauren Tomita, Mia P Montiel, Heather Culbertson. 2025-02-19. Virtual Encounters of the Haptic Kind: Towards a Multi-User VR System for Real-Time Social Touch. https://doi.org/10.1109/whc64065.2025.11123375

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC

VisCanvas: A Node-Based Interface for Exploratory Visualization Authoring with LLMs

Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.

cs.HC