Model-driven prototyping support for pervasive healthcare applications

Pervasive healthcare applications aim at improving habitability by assisting individuals in living autonomously. To achieve this goal, data on an individual's behavior and his or her environment (often collected with wireless sensors) is interpreted by machine learning algorithms; their decision finally leads to the initiation of appropriate actions, e.g., turning on the light. Developers of pervasive healthcare applications therefore face complexity stemming, amongst others, from different types of environmental and vital parameters, heterogeneous sensor platforms, unreliable network connections, as well as from different programming languages. Moreover, developing such applications often includes extensive prototyping work to collect large amounts of training data to optimize the machine learning algorithms. In this chapter the authors present a model-driven prototyping approach for the development of pervasive healthcare applications to leverage the complexity incurred in developing prototypes and applications. They support the approach with a development environment that simplifies application development with graphical editors, code generators, and pre-defined components.