| Summary: | The world is experiencing an unprecedented explosion in the number of smart devices and mobile apps that are available to users. In the health and fitness domains, many of the devices and technologies in the market place are restricted to proprietary platforms, typically working in isolation with fixed hardware settings. Consequently, an important challenge is to investigate techniques for combining and analyzing data encapsulated by these ubiquitous technologies. In this paper, we introduce an intelligent middleware—Device Nimbus—to meet this challenge. The middleware supports the integration of distributed and heterogeneous mobile sensor data, enabling both context and predictive analysis. We describe a minimum viable product of Device Nimbus and report the results of preliminary tests spanning multiple data sources focused on fitness apps, illustrating the efficacy of the middleware.
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