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
    2. James M. Rehg, Susan A. Murphy, Santosh Kumar
      Pages 345-348
    3. Peter J. Polack Jr., Moushumi Sharmin, Kaya de Barbaro, Minsuk Kahng, Shang-Tse Chen, Duen Horng Chau
      Pages 349-360
    4. Yu-Ying Liu, Alexander Moreno, Shuang Li, Fuxin Li, Le Song, James M. Rehg
      Pages 361-387
    5. Zhengping Che, Sanjay Purushotham, David Kale, Wenzhe Li, Mohammad Taha Bahadori, Robinder Khemani et al.
      Pages 389-409
    6. Hillol Sarker, Karen Hovsepian, Soujanya Chatterjee, Inbal Nahum-Shani, Susan A. Murphy, Bonnie Spring et al.
      Pages 411-433
  5. Predictors to mHealth Interventions

    1. Front Matter
      Pages 435-435
    2. Susan A. Murphy, James M. Rehg, Santosh Kumar
      Pages 437-441
    3. Wendy Nilsen, Emre Ertin, Eric B. Hekler, Santosh Kumar, Insup Lee, Rahul Mangharam et al.
      Pages 443-453
    4. Daniel E. Rivera, César A. Martín, Kevin P. Timms, Sunil Deshpande, Naresh N. Nandola, Eric B. Hekler
      Pages 455-493
    5. Ambuj Tewari, Susan A. Murphy
      Pages 495-517

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

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-51393-5
  • Online ISBN 978-3-319-51394-2
  • Buy this book on publisher's site