Exploratory visual analytics of mobile health data: Sensemaking challenges and opportunities

Peter J. Polack, Moushumi Sharmin, Kaya de Barbaro, Minsuk Kahng, Shang Tse Chen, Duen Horng Chau

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

With every advancement in mHealth sensing technology, we are presented with an abundance of data streams and models that enable us to make sense of health information we record. To distill this diverse and ever-growing data into meaningful information, we must first develop tools that can represent data intuitively and are flexible enough to handle the special characteristics of mHealth records. For example, whereas traditional health data such as electronic health records (EHR) often consist of discrete events that may be readily analyzed and visualized, mHealth entails sensor ensembles that generate continuous, multivariate data streams of high-resolution and often noisy measurements. Drawing from methodologies in machine learning and visualization, interactive visual analytics tools are an increasingly important aid to making sense of this complexity. Still, these computational and visual techniques must be employed attentively to represent this data not only intuitively, but also accurately, transparently, and in a way that is driven by user needs. Acknowledging these challenges, we review existing visual analytic tools to identify design solutions that are both useful for and adaptable to the demands of mHealth data analysis tasks. In doing so, we identify open problems for representing and understanding mHealth data, suggesting future research directions for developers in the field.

Original languageEnglish (US)
Title of host publicationMobile Health
Subtitle of host publicationSensors, Analytic Methods, and Applications
PublisherSpringer International Publishing
Pages349-360
Number of pages12
ISBN (Electronic)9783319513942
ISBN (Print)9783319513935
DOIs
StatePublished - Jul 12 2017

Fingerprint

Telemedicine
Health
Drawing (graphics)
Electronic Health Records
Learning systems
Visualization
mHealth
Technology
Sensors

ASJC Scopus subject areas

  • Medicine(all)
  • Computer Science(all)

Cite this

Polack, P. J., Sharmin, M., de Barbaro, K., Kahng, M., Chen, S. T., & Chau, D. H. (2017). Exploratory visual analytics of mobile health data: Sensemaking challenges and opportunities. In Mobile Health: Sensors, Analytic Methods, and Applications (pp. 349-360). Springer International Publishing. https://doi.org/10.1007/978-3-319-51394-2_18

Exploratory visual analytics of mobile health data : Sensemaking challenges and opportunities. / Polack, Peter J.; Sharmin, Moushumi; de Barbaro, Kaya; Kahng, Minsuk; Chen, Shang Tse; Chau, Duen Horng.

Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, 2017. p. 349-360.

Research output: Chapter in Book/Report/Conference proceedingChapter

Polack, PJ, Sharmin, M, de Barbaro, K, Kahng, M, Chen, ST & Chau, DH 2017, Exploratory visual analytics of mobile health data: Sensemaking challenges and opportunities. in Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, pp. 349-360. https://doi.org/10.1007/978-3-319-51394-2_18
Polack PJ, Sharmin M, de Barbaro K, Kahng M, Chen ST, Chau DH. Exploratory visual analytics of mobile health data: Sensemaking challenges and opportunities. In Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing. 2017. p. 349-360 https://doi.org/10.1007/978-3-319-51394-2_18
Polack, Peter J. ; Sharmin, Moushumi ; de Barbaro, Kaya ; Kahng, Minsuk ; Chen, Shang Tse ; Chau, Duen Horng. / Exploratory visual analytics of mobile health data : Sensemaking challenges and opportunities. Mobile Health: Sensors, Analytic Methods, and Applications. Springer International Publishing, 2017. pp. 349-360
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