arXiv:2512.08518v2 Announce Type: replace-cross
Abstract: Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in human-human interactions, their applicability to interactions with humanoid robots remains unexplored. In this study, we investigate user comfort with the robot “Ameca” across four experimentally controlled distances (0.5 m to 2.0 m) using mobile eye-tracking and subjective reporting (N=19). We evaluate multiple machine learning and deep learning models to estimate comfort based on gaze features. Contrary to previous human-human studies where Transformer models excelled, a Decision Tree classifier achieved the highest performance (F1-score = 0.73), with minimum pupil diameter identified as the most critical predictor. These findings suggest that physiological comfort thresholds in human-robot interaction differ from human-human dynamics and can be effectively modeled using interpretable logic.
Randomized controlled trial to evaluate an app-based multimodal digital intervention for people with type 2 diabetes in comparison to a placebo app
IntroductionThis multi-center, parallel-group randomized controlled trial evaluated the app-based intervention mebix, developed by Vision2b GmbH in Germany, for people with type 2 diabetes compared to