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.

Publisher Link

https://doi.org/10.1145/3447526.3472040

Citation

Ali Neshati, Fouad Alallah, Bradley Rey, Yumiko Sakamoto, Marcos Serrano, and Pourang Irani. 2021. SF-LG: Space-Filling Line Graphs for Visualizing Interrelated Time-series Data on Smartwatches. In Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction (MobileHCI '21). Association for Computing Machinery, New York, NY, USA, Article 5, 1–13. DOI:https://doi.org/10.1145/3447526.3472040. Honourable Mention Award

Bibtext Entry

@inproceedings{10.1145/3447526.3472040,
author = {Neshati, Ali and Alallah, Fouad and Rey, Bradley and Sakamoto, Yumiko and Serrano, Marcos and Irani, Pourang},
title = {SF-LG: Space-Filling Line Graphs for Visualizing Interrelated Time-Series Data on Smartwatches},
year = {2021},
isbn = {9781450383288},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3447526.3472040},
doi = {10.1145/3447526.3472040},
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.},
booktitle = {Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction},
articleno = {5},
numpages = {13},
keywords = {graph simplification, time-series data, Smartwatches, line graph, data visualization},
location = {Toulouse & Virtual, France},
series = {MobileHCI '21}
}