SF-LG: Space-Filling Line Graphs for Visualizing Interrelated Time-Series Data on Smartwatches

Abstract

Multiple embedded sensors enable smartwatch apps to amass large amounts of interrelated time-series data simultaneously, such as heart rate, oxygen levels or steps walked. Visualizing multiple interlinked datasets is possible on smartphones but remains challenging on small smartwatch displays. We propose a new technique, the Space-Filling Line Graph (SF-LG), that preserves the key visual properties of time-series graphs while making available space on the display to augment such graphs with additional information. Results from our first study (N=30) suggest that, while SF-LG makes available additional space on the small display, it also enables effective (i.e. quick and accurate) comprehension of key line graph tasks. We next implement a greedy algorithm to embed auxiliary information in the most suitable regions on the display. In a second study (N=27), we find that participants are efficient at locating and linking interrelated content using SF-LG in comparison to two baselines approaches. We conclude with guidelines for smartwatch space maximization for visual displays.

In Summary

Data visualizations on smartwatches are often limited by the small smartwatch screen size. To both reduce the complexity of time-series data, and to optimize the screen real estate usage for other related information we propose SF-LG (Space Filling Line Graphs).

Key Findings

• We share a novel graph simiplification algorithm, SF-LG, which also enables efficient embedding of auxiliary data into space surrounding the simplified graph
• SF-LG significantly improves on non-simplified and PIP methods in time needed for max and min detection, value detection, trend detection, and participant preference
• Using the created space created by SF-LG we propose usage scenarios for additional information such as details-on-demand, multi-level detailing, and comparative analysis

In More Detail

Please review our full paper (linked above) for study details, methodologies, and complete results.