ARGUS: Interactive Visual Analytics Framework for the Discovery of Disruptions in Bio-Behavioral Rhythms

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dc.contributor.authorMansoor, Hamiden_US
dc.contributor.authorGerych, Walteren_US
dc.contributor.authorBuquicchio, Lukeen_US
dc.contributor.authorAlajaji, Abdulazizen_US
dc.contributor.authorChandrasekaran, Kavinen_US
dc.contributor.authorAgu, Emmanuelen_US
dc.contributor.authorRundensteiner, Elkeen_US
dc.contributor.editorKerren, Andreas and Garth, Christoph and Marai, G. Elisabetaen_US
dc.date.accessioned2020-05-24T13:51:57Z
dc.date.available2020-05-24T13:51:57Z
dc.date.issued2020
dc.description.abstractHuman Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles and their regularity have important health ramifications. Smartphones can sense HBRs by gathering and analyzing data from built-in sensors, which provide behavioral clues. The multichannel nature (multiple sensor streams) of such data makes it challenging to pin-point the causes of disruptions in HBRs. Prior work has utilized machine learning for HBR classification but has not facilitated deeper understanding or reasoning about the potential disruption causes. In this paper, we propose ARGUS, an interactive visual analytics framework to discover and understand HBR disruptions and causes. The foundation of ARGUS is a Rhythm Deviation Score (RDS) that extracts a user's underlying 24-hour rhythm from their smartphone sensor data and quantifies its irregularity. ARGUS then visualizes the RDS using a glyph to easily recognize disruptions in HBRs, along with multiple linked panes that overlay sensor information and user-provided or smartphone-inferred ground truth as supporting context. This framework visually captures a comprehensive picture of HBRs and their disruptions. ARGUS was designed by an expert lead goal-and-task analysis. To demonstrate its generalizability, two different smartphone-sensed datasets were visualized using ARGUS in conjunction with expert feedback.en_US
dc.description.sectionheadersAnalytics and Evaluation
dc.description.seriesinformationEuroVis 2020 - Short Papers
dc.identifier.doi10.2312/evs.20201043
dc.identifier.isbn978-3-03868-106-9
dc.identifier.pages25-29
dc.identifier.urihttps://doi.org/10.2312/evs.20201043
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20201043
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectVisualization
dc.subjectVisualization systems and tools
dc.subjectVisualization application domains
dc.subjectVisual analytics
dc.titleARGUS: Interactive Visual Analytics Framework for the Discovery of Disruptions in Bio-Behavioral Rhythmsen_US
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