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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
User-Sensitive Mobile Interfaces: Accounting for Individu...
Tiago Guerreiro · 2014-02-05 · via cs.HC updates on arXiv.org

Mobile phones pervade our daily lives and play ever expanding roles in many contexts. Their ubiquitousness makes them pivotal in empowering disabled people. However, if no inclusive approaches are provided, it becomes a strong vehicle of exclusion. Even though current solutions try to compensate for the lack of sight, not all information reaches the blind user. Good spatial ability is still required to make sense of the device and its interface, as well as the need to memorize positions on screen or keys and associated actions in a keypad. Those problems are compounded by many individual attributes such as age, age of blindness onset or tactile sensitivity which often are forgotten by designers. Worse, the entire blind population is recurrently thought of as homogeneous (often stereotypically so). Thus all users face the same solutions, ignoring their specific capabilities and needs. We usually ignore this diversity as we have the ability to adapt and become experts in interfaces that were probably maladjusted to begin with. This adaptation is not always within reach. Interaction with mobile devices is highly visually demanding which widens this gap amongst blind people. It is paramount to understand the impact of individual differences and their relationship with demands to enable the deployment of more inclusive solutions. We explore individual differences among blind people and assess how they are related with mobile interface demands, both at low (e.g. performing an on-screen gesture) and high level (text-entry) tasks. Results confirmed that different ability levels have significant impact on the performance attained by a blind person. Particularly, otherwise ignored attributes like tactile acuity, pressure sensitivity, spatial ability or verbal IQ have shown to be matched with specific mobile demands and parametrizations.