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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
Heuristic evaluations of back support, shoulder support, ...
Alejandra Martinez, Laura Tovar, Carla Irigoyen Amparan, Karen G · 2024-05-31 · via cs.HC updates on arXiv.org

Occupational exoskeletons promise to reduce the incidence of musculoskeletal injuries; however, we do not know if their designs allow universal use by all workers. We also do not know how easy the tasks of assembling, donning, doffing, and disassembling exoskeletons are. The purpose of our study was to heuristically evaluate a back support, a shoulder support, a handgrip strength support, and a sit-stand exoskeleton for how well they are designed for universal use when assembling, donning, doffing, and disassembling the exoskeleton. Seven evaluators used universal design principles and associated criteria to independently evaluate and rate four exoskeletons when assembling, donning, doffing, and disassembling the devices. The rating scale was a Likert-type scale, where a rating of 1 represented not at all, and a rating of 5 represented an excellent design with respect to the universal design criteria for the task. The results indicate that providing perceptible information to the user, making the design equitable to use for a diverse set of users, making the design simple and intuitive to use with adequate feedback, and designing to prevent user errors, and when errors are made, allowing the user to recover quickly from the errors, were rated poorly. Assembling and donning tasks presented the most challenges.