Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data

Abstract

According to the World Health Organization, between (Formula presented.) and (Formula presented.), the proportion of the world's population over (Formula presented.) will double, from (Formula presented.) to (Formula presented.). In absolute numbers, this age group will increase from (Formula presented.) million to (Formula presented.) billion in the course of half a century. It is a reality that most of them prefer to live alone, so it is necessary to look for mechanisms and tools that will help them to improve their autonomy. Although in recent years, we have been living in a veritable explosion of domotic systems that facilitate people's daily lives, it is also true that there are not many tools specifically aimed at this sector of the population. The aim of this paper is to present a potential solution to the monitoring of activity of daily living in the least intrusive way for people. In this case, anomalous patterns of daily activities will be detected by analysing the daily consumption of household appliances. People who live alone usually have a pattern of daily behaviour in the use of household appliances (coffee machine, microwave, television, etc.). A neuronal model is proposed for the detection of abnormal behaviour based on an autoencoder architecture. This solution will be compared with a variational autoencoder to analyse the improvements that can be obtained. The well-known dataset called UK-DALE will be used to validate the proposal.