  Aging and chronic conditions affect older adults' daily lives, making early
detection of developing health issues crucial. Weakness, common in many
conditions, alters physical movements and daily activities subtly. However,
detecting such changes can be challenging due to their subtle and gradual
nature. To address this, we employ a non-intrusive camera sensor to monitor
individuals' daily sitting and relaxing activities for signs of weakness. We
simulate weakness in healthy subjects by having them perform physical exercise
and observing the behavioral changes in their daily activities before and after
workouts. The proposed system captures fine-grained features related to body
motion, inactivity, and environmental context in real-time while prioritizing
privacy. A Bayesian Network is used to model the relationships between
features, activities, and health conditions. We aim to identify specific
features and activities that indicate such changes and determine the most
suitable time scale for observing the change. Results show 0.97 accuracy in
distinguishing simulated weakness at the daily level. Fine-grained behavioral
features, including non-dominant upper body motion speed and scale, and
inactivity distribution, along with a 300-second window, are found most
effective. However, individual-specific models are recommended as no universal
set of optimal features and activities was identified across all participants.
