Abnormal Behavior Detection for Elderly People Living Alone Leveraging IoT Sensors

E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.

I. Introduction

The increasing usage of smart home technologies, combined with machine learning techniques, can provide valuable insights into people’s behavior. As elders feel more comfortable to stay at their home, reducing healthcare costs and continuing their daily activities, Active and Assisted Living (AAL) solutions need to be constantly improving in order to offer independent life for aging population, with the least possible interference in their everyday life.