Overview
- Provides a comprehensive overview of the major topics in sensing, analytics, and mobile computing which are critical to the design and deployment of mHealth systems
- Enables researchers and practitioners who are entering the mHealth field to obtain a complete introduction to research and practice in this emerging area
- Written by leading experts in the mHealth field from a diverse set of disciplines and backgrounds
- Includes supplementary material: sn.pub/extras
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Table of contents (26 chapters)
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mHealth Applications and Tools
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Sensors to mHealth Markers
Keywords
- 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
About this book
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 organizationof 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.
Editors and Affiliations
About the editors
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 onclinical 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
Book Title: Mobile Health
Book Subtitle: Sensors, Analytic Methods, and Applications
Editors: James M. Rehg, Susan A. Murphy, Santosh Kumar
DOI: https://doi.org/10.1007/978-3-319-51394-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-51393-5Published: 26 July 2017
Softcover ISBN: 978-3-319-84639-2Published: 01 August 2018
eBook ISBN: 978-3-319-51394-2Published: 12 July 2017
Edition Number: 1
Number of Pages: XL, 542
Number of Illustrations: 28 b/w illustrations, 100 illustrations in colour
Topics: Health Informatics, Artificial Intelligence, Statistics for Life Sciences, Medicine, Health Sciences, Data Mining and Knowledge Discovery, Health Informatics, Computer Communication Networks