arXiv Machine Learning By Houhao Liang, Kresimir Friganovic, Joanne Kua, Noor Hafizah Ismail, Su Su, Bryan Yijia Tan, Navrag B. Singh, Panos Mavros

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

Read the original on arXiv Machine Learning →

arXiv:2608. 14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults.

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arXiv Computer Vision
Aug 25

Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement

arXiv:2608.23531v1 Announce Type: new Abstract: Older adults recovering after lower-limb fracture or hip replacement may experience complex recovery trajectories. Most of the time, these clinical asp...

By Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan
arXiv Machine Learning
Sep 17

Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?

The study evaluates whether vision‑language models (VLMs) can reliably assess sidewalk accessibility attributes—effective width, longitudinal slope, cross slope, and pavement condition—from pedestrian‑level images. Using sampling‑based conformal prediction on 514 images from Seoul, the authors find that calibrated models achieve nominal 90% coverage, but only effective width yields informative estimates; other attributes remain too uncertain for compliance assessment. The work also demonstrates that raw sampling dispersion is not a trustworthy uncertainty measure without calibration and releases annotated images with ground‑truth measurements.

By Seung Jae Lieu, Diego Morra, Chiara Cadoni, Wonseop Song, Martina Mazzarello, Carlo Ratti