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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Safe Walking In VR using Augmented Virtuality
Maurício Sousa, Daniel Mendes, Joaquim Jorge · 2019-11-29 · via cs.HC updates on arXiv.org

New technologies allow ordinary people to access Virtual Reality at affordable prices in their homes. One of the most important tasks when interacting with immersive Virtual Reality is to navigate the virtual environments (VEs). Arguably, the best methods to accomplish this use of direct control interfaces. Among those, natural walking (NW) makes for enjoyable user experience. However, common techniques to support direct control interfaces in VEs feature constraints that make it difficult to use those methods in cramped home environments. Indeed, NW requires unobstructed and open space. To approach this problem, we propose a new virtual locomotion technique, Combined Walking in Place (CWIP). CWIP allows people to take advantage of the available physical space and empowers them to use NW to navigate in the virtual world. For longer distances, we adopt Walking in Place (WIP) to enable them to move in the virtual world beyond the confines of a cramped real room. However, roaming in immersive alternate reality, while moving in the confines of a cluttered environment can lead people to stumble and fall. To approach these problems, we developed Augmented Virtual Reality (AVR), to inform users about real-world hazards, such as chairs, drawers, walls via proxies and signs placed in the virtual world. We propose thus CWIP-AVR as a way to safely explore VR in the cramped confines of your own home. To our knowledge, this is the first approach to combined different locomotion modalities in a safe manner. We evaluated it in a user study with 20 participants to validate their ability to navigate a virtual world while walking in a confined and cluttered real space. Our results show that CWIP-AVR allows people to navigate VR safely, switching between locomotion modes flexibly while maintaining a good immersion.