© 2017

Mobile Health

Sensors, Analytic Methods, and Applications

  • James M. Rehg
  • Susan A. Murphy
  • Santosh Kumar

Table of contents

  1. Front Matter
    Pages i-xl
  2. mHealth Applications and Tools

    1. Front Matter
      Pages 1-1
    2. Santosh Kumar, James M. Rehg, Susan A. Murphy
      Pages 3-6
    3. Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor et al.
      Pages 7-33
    4. Saeed Abdullah, Elizabeth L. Murnane, Mark Matthews, Tanzeem Choudhury
      Pages 35-58
    5. Shawna N. Smith, Andy Jinseok Lee, Kelly Hall, Nicholas J. Seewald, Audrey Boruvka, Susan A. Murphy et al.
      Pages 59-82
    6. David H. Gustafson, Fiona McTavish, David H. Gustafson Jr., Scott Gatzke, Christa Glowacki, Brett Iverson et al.
      Pages 83-99
    7. Mahbubur Rahman, Nasir Ali, Rummana Bari, Nazir Saleheen, Mustafa al’Absi, Emre Ertin et al.
      Pages 121-143
  3. Sensors to mHealth Markers

    1. Front Matter
      Pages 145-145
    2. Santosh Kumar, James M. Rehg, Susan A. Murphy
      Pages 147-150
    3. Edison Thomaz, Irfan A. Essa, Gregory D. Abowd
      Pages 151-174
    4. Abhinav Parate, Deepak Ganesan
      Pages 175-201
    5. Yan Wang, Mahdi Ashktorab, Hua-I Chang, Xiaoxu Wu, Gregory Pottie, William Kaiser
      Pages 203-218
    6. Hrishikesh Rao, Mark A. Clements, Yin Li, Meghan R. Swanson, Joseph Piven, Daniel S. Messinger
      Pages 219-238
    7. Eric C. Larson, Elliot Saba, Spencer Kaiser, Mayank Goel, Shwetak N. Patel
      Pages 239-264
    8. Ju Gao, Siddharth Baskar, Diyan Teng, Mustafa al’Absi, Santosh Kumar, Emre Ertin
      Pages 289-312
    9. Zachary S. Ballard, Aydogan Ozcan
      Pages 313-342
  4. Markers to mHealth Predictors

    1. Front Matter
      Pages 343-343

About this book


This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field.
The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions). Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organization of the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course.


mobile health wearable sensors mobile computing health data analytics low-power sensing and computing behavioral medicine health interventions mHealth chronic diseases and conditions mental health machine learning data mining reinforcement learning control systems engineering just-in-time adaptive interventions fitness trackers

Editors and affiliations

  • James M. Rehg
    • 1
  • Susan A. Murphy
    • 2
  • Santosh Kumar
    • 3
  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of StatisticsUniversity of MichiganAnn ArborUSA
  3. 3.Department of Computer ScienceUniversity of MemphisMemphisUSA

About the editors

James M. Rehg is a Professor of Computer Science at the Georgia Institute of Technology where he directs the Center for Behavioral Imaging and co-directs the Computational Perception Lab. Dr. Rehg’s research focuses on computer vision, machine learning, and mobile health, with an emphasis on the analysis of video captured by wearable cameras for mobile health applications. He was the lead PI on an NSF Expedition to develop computational methods for measuring, modeling, and analyzing social and communicative behavior, with applications to developmental disorders such as autism. He is currently the Deputy Director of the NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), where he leads the Data Science Research Core.

Susan Murphy is the H.E. Robbins Distinguished University Professor of Statistics at the University of Michigan. Dr. Murphy’s research focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of adaptive interventions (e.g. treatment policies). She currently works, as part of the MD2K team and other interdisciplinary teams, to develop clinical trial designs and learning algorithms in mobile health. She is a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, President-Elect of the Bernoulli Society, a member of the US National Academy of Science, a member of the US National Academy of Medicine and a 2013 MacArthur Fellow.

Santosh Kumar is a Professor of Computer Science at the University of Memphis where he holds the Lillian & Morrie Moss Chair of Excellence. Dr. Kumar’s research focusses on mobile health, with an emphasis on developing computational models to infer human health and behavior such as stress, conversation, smoking, and drug use from wearable sensor data. He is director of the NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), that involves over 20 scientists from in computing, engineering, behavioral science, and medicine. He was named one of America’s ten most brilliant scientists under the age of 38 by Popular Science in 2010.

Bibliographic information