Monitoring Simulated Physical Weakness Using Detailed Behavioral Features and Personalized Modeling

TL;DR

This study develops a privacy-preserving, real-time behavioral monitoring system using detailed features and personalized models to detect simulated physical weakness in healthy adults, aiding early health issue detection.

Contribution

It introduces a novel approach combining detailed behavioral features, personalized modeling, and Bayesian Networks for detecting subtle physical weakness signs.

Findings

Personalized models achieved 97% accuracy in distinguishing weak from normal days.

Non-dominant upper-body motion features are highly effective within a 300-second window.

No universal features or activities were optimal across all participants.

Abstract

Aging and chronic conditions affect older adults' daily lives, making the early detection of developing health issues crucial. Weakness, which is common across many conditions, can subtly alter physical movements and daily activities. However, these behavioral changes can be difficult to detect because they are gradual and often masked by natural day-to-day variability. To isolate the behavioral phenotype of weakness while controlling for confounding factors, this study simulates physical weakness in healthy adults through exercise-induced fatigue, providing interpretable insights into potential behavioral indicators for long-term monitoring. A non-intrusive camera sensor is used to monitor individuals' daily sitting and relaxing activities over multiple days, allowing us to observe behavioral changes before and after simulated weakness. The system captures fine-grained features related to body motion, inactivity, and environmental context in real time while prioritizing privacy. A Bayesian Network models the relationships among activities, contextual factors, and behavioral indicators. Fine-grained features, including non-dominant upper-body motion speed and scale, together with inactivity distribution, are most effective when used with a 300-second window. Personalized models achieve 0.97 accuracy at distinguishing simulated weak days from normal days, and no universal set of optimal features or activities is observed across participants.