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G-Sparks: Glanceable Sparklines on Smartwatches

Optimizing the use of a small display while presenting graphic data such as line charts is challenging. To tackle this, we propose GSparks, a compact visual representation of glanceable line graphs for smartwatches. Our exploration primarily considered the suitable compression axes for time-series charts. In a first study we examine the optimal line-graph compression approach without compromising perceptual metrics, such as slope or height detections. We evaluated compressions of line segments, the elementary unit of a line graph, along the x-axis, y-axis, and xyaxes. Contrary to intuition, we 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, we 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 bio-metric data (heart rate), we 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 our results, we offer guidelines to application designers needing to integrate line charts into smartwatch apps.

http://graphicsinterface.org/proceedings/gi2019/gi2019-23/

Ali Neshati, Yumiko Sakamoto, Launa Leboe-Mcgowan, Jason Leboe-Mcgowan, Marcos Serrano, Pourang Irani. G-Sparks: Glanceable Sparklines on Smartwatches (regular paper). Dans : Graphics Interface (GI 2019), Kingston, Canada, 28/05/19-31/05/19, 2019

Bibtext Entry

@inproceedings{Neshati:2019:10.20380/GI2019.23,
author = {Neshati, Ali and Sakamoto, Yumiko and Leboe-McGowan, Launa C and Leboe-McGowan, Jason and Serrano, Marcos and Irani, Pourang},
title = {G-Sparks: Glanceable Sparklines on Smartwatches},
booktitle = {Proceedings of Graphics Interface 2019},
series = {GI 2019},
year = {2019},
issn = {0713-5424},
isbn = {978-0-9947868-4-5},
location = {Kingston, Ontario},
numpages = {9},
doi = {10.20380/GI2019.23},
publisher = {Canadian Information Processing Society},
keywords = {Smartwatch visualization, small screen, line graph, compression methods, spark lines},
}

Authors

Ali Neshati

Ali Neshati

PhD Student
Dr. Yumiko Sakamoto

Dr. Yumiko Sakamoto

Research Associate
Pourang Irani

Pourang Irani

Professor
Canada Research Chair

As well as: Launa C Leboe-McGowan, Jason Leboe-McGowan