Data visualization and interaction on smartwatch small screens

In this thesis, I will investigate the optimization of a small display while presenting graphic data such as line charts. Due to the small screen of smartwatches presenting the data could be challenging. To overcome these challenges, I will propose two techniques, Compression and Simplification, to improve the visualization techniques on smartwatches. The last part of this thesis is about interaction with visualization techniques on smartwatches. I will show how interacting with the smartwatch bezel and using some specific parts of the smartwatch display can be helpful to interact with various types of charts and graphs on smartwatches. In the first part of my thesis, I propose G-Sparks, a compact visual representation of glanceable line graphs for smartwatches. My exploration primarily considered the suitable compression axes for time-series charts. In a first study, I examine the optimal line-graph compression approach without compromising perceptual metrics, such as slope or height detection. I evaluated compressions of line segments, the elementary unit of a line graph, along the x-axis, y-axis, and xy-axes. Contrary to intuition, I find that condensing graphs yield more accurate reading of height estimations than non-compressed graphs, but only when these are compressed along the x-axis. Building from this result, I study the effect of an x-axis compression on users' ability to perform "glanceable" analytic tasks with actual data. Glanceable tasks include quick perceptual judgements of graph properties. Using biometric data (heart rate), I find that shrinking a line graph to the point of representing one data sample per pixel does not compromise legibility. As expected, such type of compression also has the effect of minimizing the needed amount of flicking to interact with such graphs. From the results, I offer guidelines to application designers needing to integrate line charts into smartwatch apps. Multiple embedded sensors enable smartwatch apps to amass la