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.

通过使用非侵入式摄像头传感器监控个体的坐姿和放松活动，本研究旨在检测衰弱迹象，并通过建立贝叶斯网络模型特征、活动和健康状况之间的关系，确定最适合观察变化的时间尺度。研究结果表明，在日常水平上区分模拟的衰弱的准确率为 0.97，而一些精细的行为特征和活动，包括非主导上半身运动速度和规模，以及静止分布和 300 秒的时间窗口，则被发现最为有效。然而，鉴于无法确定适用于所有参与者的通用优化特征和活动，建议使用个体特定模型。