AWARE : a mobile context instrumentation middleware to collaboratively understand human behavior
Denzil Ferreira · 2013
This thesis presents a mobile instrumentation middleware, AWARE, aimed at facilitating our understanding of human behavior. We demonstrate how to use AWARE to build context-aware applications, collect data, and study human behavior. Mobile phones are resource-constrained and several considerations need to be taken into account to create a research tool that ensures problem-free data collection. AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications. By encapsulating implementation details of sensor data retrieval and exposing the sensed data as higher-level abstractions, researchers spend less time developing software and save more time for doing research and analyzing the collected data, both quantitative and qualitative. This thesis demonstrates AWARE’s use in a number of case studies. These vary in the research methods we have used: prototype-building; large-scale deployment; surveys; interviews; cognitive walkthroughs; heuristic evaluation; laboratory & field studies data logs; Day Reconstruction Method (DRM); and Experience Sampling Method (ESM). Together with these methods, we demonstrate how AWARE helps study human behavior in different research scenarios, such as: enabling human-smartphone awareness, understanding concerns on battery life, modeling the proximity of users to their smartphones, and capturing location sharing concerns. The thesis’ contributions are: the design, implementation and evaluation of a novel mobile instrumentation middleware to facilitate an understanding of human behavior.