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Mobile Health pp 349–360Cite as

Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities

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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.

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Notes

  1. 1.

    An initiative proposed by U.S. President Barack Obama in his 2015 State of the Union address.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Peter J. Polack Jr., Kaya de Barbaro, Minsuk Kahng, Shang-Tse Chen & Duen Horng Chau

  2. Western Washington University, Bellingham, WA, USA

    Moushumi Sharmin

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  1. Peter J. Polack Jr.
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Correspondence to Peter J. Polack Jr. .

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Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

    James M. Rehg

  2. Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA

    Susan A. Murphy

  3. Department of Computer Science, University of Memphis, Memphis, Tennessee, USA

    Santosh Kumar

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Polack, P.J., Sharmin, M., de Barbaro, K., Kahng, M., Chen, ST., Chau, D.H. (2017). Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_18

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