AALFlow: A Model-Driven Approach for the Integration of Internet of Things Heterogeneous Solutions for Ambient Assisted Living

Abstract

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.