ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

• Main Authors: Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu, Elke Rundensteiner
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Visual Informatics
• Subjects: Interactive visual analytics; Circadian rhythms; Smartphone-sensed data
• Online Access: http://www.sciencedirect.com/science/article/pii/S2468502X21000322

Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian Rhythms), and the degree of regularity of sleep and physical activity have important health ramifications. Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns. As human behavior is complex and smartphone data is voluminous with many channels (sensor types), it can be challenging to make meaningful observations, detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions. Prior work has largely utilized computational methods such as machine and deep learning approaches, which while accurate, are often not explainable and present few actionable insights on HBR patterns or causes. To assist analysts in the discovery and understanding of HBR patterns, disruptions and causes, we propose ARGUS, an interactive visual analytics framework. As a foundation of ARGUS, we design an intuitive Rhythm Deviation Score (RDS) that analyzes users’ smartphone sensor data, extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.​ This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs. ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state (screen locked, charging), and user-provided or smartphone-inferred ground truth information. This array of visualization overlays in ARGUS enables​ analysts to gain a more comprehensive picture of HBRs, behavioral patterns and deviations from regularity. The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing. To demonstrate its utility and generalizability, two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.