Machine Learning Model for Frailty Detectxion using Electric Power Consumption Data from Smart Meter

With the increase of the number of the elderly, healthcare systems to support the daily life and wellbeing of the elderly attracted attention. Especially, frailty syndrome is one of the most significant challenges faced by many countries because of its high association with mortality and hospitalization. Recently, with the progress of ICT (Information and Communication Technology), many frailty detection models which use sensors were proposed. However, many of them require very high costs caused by the installation and management of sensors. Therefore, the objective of this study is to propose a machine learning-based frailty detection model using only electric power consumption data from smart meter, which uses no other devices such as sensors. Also, we examined the feasibility of our model through a case study, in which we have conducted on 24 elderly people. As a result of a cast study, we could detect frailty with 82% accuracy, 77% precision, 84% recall, and 80% f-score for a 2-class classification problem (frailty or non-frailty). The results of our study show that more elderly people can receive frailty diagnoses through smart meters. Moreover, since frailty is a reversible condition that could be restored to a healthy status with early and appropriate intervention, our model has potential to extend the healthy expectancy of the elderly.