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Mobile Health pp 121–143Cite as

mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data

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Abstract

Mobile sensor data collected in the natural environment are subject to numerous sources of data loss and quality deterioration. This may be due to degradation in attachment, change in placement, battery depletion, wireless interference, or movement artifacts. Identifying and fixing the major sources of data loss is critical to ensuring high data yield from mobile sensors. This chapter describes a systematic approach for identifying the major sources of data loss that can then be used to improve mobile sensor data yield.

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Notes

  1. 1.

    Please see software repository at https://md2k.org.

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Acknowledgements

The authors thank Dr. David H. Epstein from NIDA Intramural Program for his extensive editing on an earlier draft of this chapter. The authors also thank Motohiro Nakajima and Andrine M. Lemieux from University of Minnesota for their contribution to the smoking cessation study and their edits to the chapter draft. The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01CA190329, R01MD010362, and R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative, and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative. We also acknowledge support of the NIDA Intramural Research Program for their support of the NIDA study.

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Authors and Affiliations

  1. University of Memphis, Memphis, TN, USA

    Md. Mahbubur Rahman, Nasir Ali, Rummana Bari, Nazir Saleheen & Santosh Kumar

  2. University of Minnesota Medical School, Duluth, MN, USA

    Mustafa al’Absi

  3. The Ohio State University, Columbus, OH, USA

    Emre Ertin

  4. NIDA IRP, Baltimore, MD, USA

    Ashley Kennedy & Kenzie L. Preston

Authors
  1. Md. Mahbubur Rahman
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  2. Nasir Ali
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  9. Santosh Kumar
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Correspondence to Mahbubur Rahman .

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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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Rahman, M. et al. (2017). mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-51394-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51393-5

  • Online ISBN: 978-3-319-51394-2

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