The global aging population has led, over the last few years, to the proliferation of Ambient Assisted Living (AAL) systems. These include Internet of Things (IoT)-based systems for monitoring, activity recognition, fall detection and even indoor location systems. The wide variety of existing systems highlights the interest in AAL as a tool to improve the life of elderly and dependent people. However, such variety is also an issue for data integration, which in turn challenges the development of more advanced functionalities for their care. To solve this issue, this paper proposes a model-driven approach that facilitates the creation of information flows to combine several existing IoT solutions for AAL systems. For this purpose, we present a metamodel that allow to represent information flows for heterogeneous AAL systems. In addition, a graphical tool to facilitate the design of such flows along with a mechanism to automatically transform the models into executable code is also provided. Finally, a use case oriented to monitoring elderly and dependent people routine is presented. Thus, we contribute to facilitating the integration of heterogeneous applications based on information flows to build more complex systems that improve real-time monitoring and care of the elderly and dependent people.

中文翻译：

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AALFlow：一种模型驱动的方法，用于集成环境辅助生活的物联网异构解决方案


过去几年，全球人口老龄化导致环境辅助生活 (AAL) 系统的激增。其中包括基于物联网 (IoT) 的监控、活动识别、跌倒检测甚至室内定位系统。现有系统的多样性凸显了 AAL 作为改善老年人和受抚养人生活的工具的兴趣。然而，这种多样性也是数据集成的一个问题，这反过来又挑战了为他们的护理开发更先进的功能。为了解决这个问题，本文提出了一种模型驱动的方法，该方法有助于创建信息流，以结合 AAL 系统的几种现有物联网解决方案。为此，我们提出了一个元模型，允许表示异构 AAL 系统的信息流。此外，还提供了促进此类流程设计的图形工具以及自动将模型转换为可执行代码的机制。最后，提出了一个旨在监测老年人和受抚养人日常活动的用例。因此，我们致力于促进基于信息流的异构应用程序的集成，以构建更复杂的系统，从而改善对老年人和受抚养人的实时监控和护理。