Rethinking healthcare for individuals through personalized pervasive technology
Title and abstracts due: CLOSED
Full Manuscripts due: CLOSED
Publication date: July-September 2020
The goal of personalized pervasive health is to continuously tailor prevention, medical diagnosis, and treatment interventions to each individual, in order to maximize quality of life. Personal differences affect how an individual responds to an intervention or medication, and early risk identification and mitigation can improve health. To optimize individual health, ideally all relevant data are being integrated, including genetic, imaging, behavioral, and environmental data. Increasingly, ubiquitous technologies are being used to collect behavioral, physiological, and exposure data from individuals related to their health and wellbeing, such as sleeping habits, daily routines, or socialization frequency. Such monitoring data can be used to develop targeted interventions or medications that are designed using the behavioral, and even the genetic, profile of a patient. Pervasive computing technology can be used to analyze how an individual responds to a treatment, e.g., whether and how quickly a patient returns to baseline mobility after surgery, or side effects of a medication causing excessive daytime sleepiness. The key components of pervasive health that are beginning to enable the vision of personalized medicine include pervasive monitoring technologies, novel data analysis methods, and effective human-computer interfaces to persuade behavior change.
This special issue intends to provide a comprehensive view on innovative pervasive computing methods, ubiquitous technology, data-based inference algorithms, as well as evaluation studies, all related to personalized health. Methods addressing personalization technology and computing are particularly welcome; examples of such methods are individually fitted devices, personally adapted statistical pattern recognition models, personalized behavior change interventions, or models and technology integrating pervasive technology and computing in personalized patient pathways. In addition, contributions are sought on novel techniques that connect pervasive health and artificial intelligence, as well as approaches that derive and exploit digital patient models in the context of pervasive computing. All investigations must include thorough evaluations of their approaches and methods. In particular, we solicit contributions that focus on the following and related aspects of personalized pervasive health:
The guest editors invite original and high-quality submissions addressing any aspect of this field, as long as the connection to pervasive computing is clear and central to the paper. Review or summary articles—for example, critical evaluations of the state of the art, or an insightful analysis of established and upcoming technologies—may be accepted if they demonstrate academic rigor and relevance.
Articles submitted to IEEE Pervasive Computing should not exceed 6,000 words, including all text, the abstract, keywords, bibliography, biographies, and table text. The word count must include 250 words for each table and figure. References should be limited to at most 20 citations (40 for survey papers). Authors are encouraged, but not required, to use a template for submission (accepted articles will ultimately be typeset by magazine staff for publication).
Note that the magazine always welcomes submissions into its regular queue that cover the role of computing in the physical world – as characterized by visions such as the Internet of Things and Ubiquitous Computing. Topics of interest are, e.g., hardware design, sensor networks, mobile systems, human-computer interaction, industrial design, machine learning, data science, but also societal issues including privacy and ethics. Simply select the “Regular” option when submitting at the submission site (submissions are possible at any time; no need for prior abstract by email).
Oliver Amft, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
Jesus Favela, Centro de Investigación Científica y de Educación Superior de Ensenada, Mexico
Stephen Intille, Northeastern University, U.S.A
Vassilis Kostakos, University of Melbourne, Australia
Mirco Musolesi, University College London and The Alan Turing Institute, U.K.
