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Data Quality in Mobile Sensing Datasets for Pervasive Healthcare

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

Abstract

Mobile sensing is becoming a popular approach for inferring patterns of activity and behavior to determine how they affect health and wellbeing. This data-driven approach has the potential to become a major tool in the field of epidemiology, aimed at determining the causes of disease in populations, as well as motivating behavior change. These sensing technologies are generating large datasets that demand significant processing and data management resources. Studies in mobile sensing for healthcare have motivated the creation of large, complex datasets with information opportunistically gathered from distributed sensors in mobile devices. In this chapter, we discuss some of the architectural challenges regarding data gathering in this distributed data-intensive environment such as the healthcare industry, as well as issues regarding the organization and sharing of the large amounts of data collected. Some of these issues include the heterogeneity of the devices, diversity of sensors used, and the need for data provenance when integrating datasets from diverse studies. We highlight that assessing data quality is of paramount importance for conducting longitudinal studies and building on historical knowledge as new data become available. Finally, we identify future research topics in the growing field of mobile sensing and its application to healthcare and wellbeing. We discuss aspects of data curation, data quality, and data provenance, and we provide suggestions on how these challenges could be addressed in the near future.

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Notes

  1. 1.

    A sensing campaign is a data collection campaign following a research protocol. A sensing campaign can involve tens or thousands of participants carrying one or more mobile devices, which opportunistically collect data from the user or her surroundings, or ask the user to carry out certain task (e.g., take a picture when feeling sad or answer a question).

  2. 2.

    https://www.nih.gov/research-training/allofus-research-program.

  3. 3.

    https://dmponline.dcc.ac.uk/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, CICESE, Ensenada, Mexico

    Netzahualcóyotl Hernández, Jesús Favela & Layla Michán

  2. Department of Computing and Design, Sonora Institute of Technology, Ciudad Obregon, Mexico

    Luis A. Castro

  3. Facultad de Ciencias, UNAM, Mexico City, Mexico

    Layla Michán

  4. Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey

    Bert Arnrich

Authors
  1. Netzahualcóyotl Hernández
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  2. Luis A. Castro
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  3. Jesús Favela
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  4. Layla Michán
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  5. Bert Arnrich
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Corresponding author

Correspondence to Netzahualcóyotl Hernández .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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Hernández, N., Castro, L.A., Favela, J., Michán, L., Arnrich, B. (2017). Data Quality in Mobile Sensing Datasets for Pervasive Healthcare. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-58280-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58279-5

  • Online ISBN: 978-3-319-58280-1

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