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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
A Wearable Virtual Touch System for Cars
Gowdham Prabhakar, Priyam Rajkhowa, Pradipta Biswas · 2021-06-10 · via cs.HC updates on arXiv.org

In automotive domain, operation of secondary tasks like accessing infotainment system, adjusting air conditioning vents, and side mirrors distract drivers from driving. Though existing modalities like gesture and speech recognition systems facilitate undertaking secondary tasks by reducing duration of eyes off the road, those often require remembering a set of gestures or screen sequences. In this paper, we have proposed two different modalities for drivers to virtually touch the dashboard display using a laser tracker with a mechanical switch and an eye gaze switch. We compared performances of our proposed modalities against conventional touch modality in automotive environment by comparing pointing and selection times of representative secondary task and also analysed effect on driving performance in terms of deviation from lane, average speed, variation in perceived workload and system usability. We did not find significant difference in driving and pointing performance between laser tracking system and existing touchscreen system. Our result also showed that the driving and pointing performance of the virtual touch system with eye gaze switch was significantly better than the same with mechanical switch. We evaluated the efficacy of the proposed virtual touch system with eye gaze switch inside a real car and investigated acceptance of the system by professional drivers using qualitative research. The quantitative and qualitative studies indicated importance of using multimodal system inside car and highlighted several criteria for acceptance of new automotive user interface.