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

Abstract

According to the World Health Organization, between 2000 and 2050, the proportion of the world's population over 60 will double, from 11% to 22%. In absolute numbers, this age group will increase from 605 million to 2 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.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

“…This reduction in class complexity enables autoencoders to also be used for pre-processing prior to binary classification problems. DL networks in the form of autoencoders have been used to assess the activities of daily living of older adults using electricity consumption data [ 80 ], to assess mobility and fall risk [ 81 , 82 ], and to prevent adverse drug reactions using electronic health records [ 83 ].…”

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

“…From the 92 ([1-10], [11][12][13][14][15][16][17][18][19][20]) articles reviewed we quantified the algorithms implemented in the various articles as illustrated in Table 2, even though some researchers used multiple combinations of the algorithms. Identifying the algorithms and how it is implemented, will help in assessing its strength and weaknesses and the overall accuracy level of their model.…”

“…We try to examine and assess how the appliance anomaly detection techniques could be deployed in the current global markets and perhaps assessed the difficulties associated with the implementations. The electronics, electrical and in general the digital market is growing at a robust rate and anomaly detections will play an integral part in its growth [13,29]. Active utilities and energy companies' providers are not left out in providing appliance anomaly detections and energy monitoring solutions.…”

An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm

“…The occurrence of electricity theft has led to abnormal loads on station users [1], posing challenges and pressures to power supply quality and grid management [2][3][4]. Therefore, the study of abnormal electricity consumption diagnosis is of great significance for optimizing load distribution, identifying potential problems, and improving the efficiency and safety of the power system.…”