Life expectancy in the UK has increased since the 19th century. As of 2019, there are just under 12 million people in the UK aged 65 or over, with close to a quarter living by themselves. Thus, many families and carers are looking for new ways to improve the health and care of older people. Passive sensors such as infra-red motion and plug sensors have had success as a noninvasive way to help the older people. These provide a series of categorical sensor events throughout the day. Modeling this categorical dataset can help to understand and predict behavior. This article proposes a method to model the probability a sensor will trigger throughout the day for a household whilst accounting for the prior data and other sensors within the home. We present our results on a dataset from Howz, a company helping people to passively identify changes in their behavior over time.

中文翻译：

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使用被动传感器技术对老年人的家庭活动进行建模和预测

自 19 世纪以来，英国的预期寿命有所增加。截至 2019 年，英国 65 岁或以上的人口不到 1200 万，其中近四分之一独自生活。因此，许多家庭和护理人员正在寻找新的方法来改善老年人的健康和护理。红外线运动和插头传感器等被动传感器作为帮助老年人的非侵入性方法已取得成功。这些提供了全天的一系列分类传感器事件。对该分类数据集建模有助于理解和预测行为。本文提出了一种方法来模拟传感器在一天中为家庭触发的概率，同时考虑家庭中的先前数据和其他传感器。我们在 Howz 的数据集上展示我们的结果，