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

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.

https://doi.org/10.1145/3447526.3472040

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}
}

Authors

Ali Neshati

Ali Neshati

Alumni
Fouad Alallah

Fouad Alallah

PhD Student
Bradley Rey

Bradley Rey

Alumni
Dr. Yumiko Sakamoto

Dr. Yumiko Sakamoto

Senior Research Associate at University of British Columbia
Pourang Irani

Pourang Irani

Professor
Canada Research Chair
at University of British Columbia Okanagan Campus