Mobile Health
Sensors, Analytic Methods, and Applications
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Beschreibung
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 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.
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
Autor*in
James M. Rehg
Themen in »Mobile Health«
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