Taming data complexity in lifelogs: exploring visual cuts of personal informatics data

As people continue to adopt technology based self tracking devices and applications, questions arise about how personal informatics tools can better support self tracker goals. This paper extends prior work on analyzing and summarizing self tracking data, with the goal of helping self trackers identify more meaningful and actionable findings. We begin by surveying physical activity self trackers to identify their goals and the factors they report influence their physical activity. We then define a cut as a subset of collected data with some shared feature, develop a set of cuts over location and physical activity data, and visualize those cuts using a variety of presentations. Finally, we conduct a month long field deployment with participants tracking their location and physical activity data and then using our methods to examine their data. We report on participant reactions to our methods and future design opportunities suggested by our work.