mDebugger: Assessing and diagnosing the fidelity and yield of mobile sensor data

Authors

Mahbubur M. Rahman, University of Memphis
Nasir Ali, University of Memphis
Rummana Bari, University of Memphis
Nazir Saleheen, University of Memphis
Mustafa al'Absi, University of Minnesota Medical School
Emre Ertin, The Ohio State University
Ashley Kennedy, National Institute on Drug Abuse (NIDA)
Kenzie L. Preston, National Institute on Drug Abuse (NIDA)
Santosh Kumar, University of Memphis

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.

Publication Title

Mobile Health: Sensors, Analytic Methods, and Applications

Recommended Citation

Rahman, M., Ali, N., Bari, R., Saleheen, N., al'Absi, M., Ertin, E., Kennedy, A., Preston, K., & Kumar, S. (2017). mDebugger: Assessing and diagnosing the fidelity and yield of mobile sensor data. Mobile Health: Sensors, Analytic Methods, and Applications, 121-143. https://doi.org/10.1007/978-3-319-51394-2_7