Sensor technologies have gained attention as an effective means to monitor physical and mental wellbeing of elderly. In this study, we examined the possibility of using passive in-home sensors to detect frailty in older adults based on their day-to-day in-home living pattern. The sensor-based elderly monitoring system consists of PIR motion sensors and a door contact sensor attached to the main door. A set of pre-defined features associated with elderly’s day-to-day living patterns were derived based on sensor data of 46 elderly gathered over two different time periods. A series of feature vectors depicting different behavioral aspects were derived to train and test three machine learning algorithms; Logistic Regression, Linear Discriminant Analysis and Naïve Bayes. The best prediction scores yielded by seven features, namely, daytime napping, time in the bedroom, night-time sleep, kitchen activity level, kitchen use duration, in-home transitions and away duration. These features produced an area under the ROC curve of 98%, 79% and 93%, for Logistic Regression, Linear Discriminant Analysis and Naïve Bayes algorithms respectively. The findings of this study provide implications on how a non-intrusive sensor-based monitoring system comprised of a minimum set of sensors coupled with predictive analytics can be used to detect frail elderly.
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This research was supported by the Singapore Ministry of National Development and National Research Foundation under the Land and Livability National Innovation Challenge (L2NIC) Award No. L2NICCFP1-2013-5.
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