arXiv Computer Vision By Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan

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

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arXiv Machine Learning
Aug 27

DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning

DeMMO is an interpretable framework that models longitudinal digital mobility outcomes (DMOs) across multiple diseases and outcomes using multi-task learning. It introduces a cross-disease, cross-outcome relation-learning mechanism that learns signed relationships from longitudinal DMO coefficient matrices, allowing selective information sharing even when disease cohorts lack shared participants. Evaluated on the Mobilise‑D dataset, DeMMO outperforms nine strong baselines and identifies reliable longitudinal DMO patterns for clinical validation.

By Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi, Po Yang
arXiv Machine Learning
Sep 18

Smart Insole Human Activity Recognition for Continuous Monitoring in Elderly Care

The paper introduces a wireless smart insole that uses 16 pressure sensors and a six‑axis IMU to detect sitting, standing, walking, and unstable walking. Data from 15 healthy adults were processed in overlapping windows and evaluated with participant‑independent cross‑validation, achieving macro‑F1 scores of 0.954–0.980 using Histogram‑Based Gradient Boosting. A compact 1D‑CNN performed similarly but did not significantly outperform the gradient‑boosting model.

By Edwin Rios, Antony Garcia, Fengpei Yuan, Xinming Huang
Hugging Face Trending Papers
Jul 26

Markerless Motion Capture in Routine Clinical Upper Limb Assessments: Validity and Insights Beyond Ordinal Scoring

The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity and specificity. We evaluated whether artificial-intelligence (AI)-based markerless motion capture (MMC), embedded into ARAT assessments during clinical routine, accurately reconstructs upper limb movement and yields valid, objective kinematic metrics carrying clinically meaningful information beyond the ordinal score.

arXiv Machine Learning
Aug 18

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

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.

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