The case for human-centric personal analytics

The rich context provided by smartphones has enabled many new context-aware applications. However, these applications still need to provide their own mechanisms to interpret low-level sensing data and generate high-level user states. In this paper, we propose the idea of building a personal analytics (PA) layer that will use inputs from multiple lower layer sources, such as sensor data (accelerometers, gyroscopes, etc.), phone data (call logs, application activity, etc.), and online sources (Twitter, Facebook posts, etc.) to generate high-level user contextual states (such as emotions, preferences, and engagements). Developers can then use the PA layer to easily build a new set of interesting and compelling applications. We describe several scenarios enabled by this new layer and present a proposed software architecture. We end with a description of some of the key research challenges that need to be solved to achieve this goal.