Ni Qin, García Hernando Ana Belén, de la Cruz Iván Pau
Departamento de Ingeniería Telemática y Electrónica, Universidad Politécnica de Madrid, Carretera de Valencia km. 7, Madrid 28031, Spain.
Sensors (Basel). 2015 May 14;15(5):11312-62. doi: 10.3390/s150511312.
Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of "activity" as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people's independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.
智能家居中的人类活动检测是发达国家快速老龄化人口进行非侵入式健康监测的基础之一。该领域的大多数工作都将“活动”概念作为构建医疗监测或环境辅助生活等应用的基石。识别特定活动的过程包括选择合适的传感器集、对其提供的原始数据进行正确的预处理以及使用这些信息进行学习/推理。如果传感器的选择和数据处理方法执行不当,整个活动检测过程可能会失败,从而导致整个应用程序失败。与此相关的是,本综述的主要贡献如下:第一,我们对智能家居场景中针对老年人独立生活的主要活动进行了分类,以及它们的特征描述和形式化的上下文表示;第二,我们对适用于检测上述活动的传感器和数据处理方法进行了分类。我们的目的是在这些较低层次的技术方面帮助研究人员和开发人员,而这些方面对于整个应用程序的成功至关重要。