According to the World Health Organization, between and , the proportion of the world's population over will double, from to . In absolute numbers, this age group will increase from million to 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.

中文翻译：

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变分自编码器使用电力消耗数据检测老年人行为异常

根据世界卫生组织的数据，在和之间，世界人口的比例将翻一番，从到。在绝对数字上，这个年龄组将从百万增加到在半个世纪的过程中达到十亿。他们中的大多数人更喜欢独居是一个现实，因此有必要寻找有助于他们提高自主性的机制和工具。尽管近年来，我们生活在促进人们日常生活的家庭系统的真正爆炸式增长中，但也确实没有多少专门针对这一人群的工具。本文的目的是提出一种潜在的解决方案，以对人们的干扰最小的方式监测日常生活活动。在这种情况下，将通过分析家用电器的日常消耗来检测日常活动的异常模式。独居者在使用家用电器（咖啡机、微波炉、电视等）方面通常有一种日常行为模式。提出了一种神经元模型，用于基于自动编码器架构检测异常行为。该解决方案将与变分自动编码器进行比较，以分析可以获得的改进。名为 UK-DALE 的著名数据集将用于验证该提案。