Conference Proceeding Article
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.
Human centric contexts, Personal analytics
Software and Cyber-Physical Systems
WPA'14: Proceedings of the 2014 ACM Workshop on Physical Analytics: June 16, 2014, Bretton Woods, NH
City or Country
LEE, Youngki and BALAN, Rajesh Krishna.
The Case for Human-Centric Personal Analytics. (2014). WPA'14: Proceedings of the 2014 ACM Workshop on Physical Analytics: June 16, 2014, Bretton Woods, NH. 25-29. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2657
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