An adaptable and extensible mobile sensing framework for patient monitoring

Smartphone apps with self-monitoring and sensing capabilities can help in disease prevention; however, such context-aware applications are difficult to develop, due to the complexities of sensor data acquisition, context modeling, and data management. To ease the development of mHealth and Telemedicine apps, we developed the Mobile Sensing Framework (MSF), which dynamically installs device appropriate context sensing plug-ins that provide a wealth of information about users´ mental and physical states. The MSF automatically collects information about incoming/outgoing/missed calls; apps usage; sound pressure levels; light sensor values; movement data (e.g., step count); location; heart rate; etc. The MSF also includes a searchable object-based persistence layer, which is capable of rapidly serializing and de-serializing detected context data. Collected data are stored securely in the phone´s database, where they can be retrieved by applications for local analysis, remote monitoring, and alert generation. We developed a fully operational prototype of the MSF platform that was validated using several Android-based devices. This paper presents an overview of our approach along with a description of the experiments conducted using the MSF prototype.