IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications
Smart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively...
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2021-01-01
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Online Access: | https://ieeexplore.ieee.org/document/9361372/ |
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doaj-995124c2649440308b2263cfce95b1792021-03-30T15:06:00ZengIEEEIEEE Access2169-35362021-01-019304043040710.1109/ACCESS.2021.30575279361372IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health ApplicationsQingxue Zhang0https://orcid.org/0000-0001-7125-7928Vincenzo Piuri1Edward A. Clancy2Dian Zhou3Thomas Penzel4Wenchuang Walter Hu5Cardiovascular Research Center, Harvard University, Cambridge, MA, USADepartment of Computer Engineering, University of Milan, Milan, ItalyDepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USADepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USAInterdisciplinary Center of Sleep Medicine, Charite University Hospital, 10117, Berlin, GermanyDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USASmart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively sense individuals’ health information and intelligently learn from its high-level health insights. These two factors are also closely coupled. For example, to enhance the signal quality, a sensing array requires advanced information learning techniques to fuse the information, and to enrich medical insights in mobile health monitoring, we need to combine “multimodal signal processing and machine learning techniques” and “nonintrusive multimodality sensing methods.” In new smart health application exploration, challenges arise in both information sensing and learning, especially their areas of interaction.https://ieeexplore.ieee.org/document/9361372/ |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu |
spellingShingle |
Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications IEEE Access |
author_facet |
Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu |
author_sort |
Qingxue Zhang |
title |
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications |
title_short |
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications |
title_full |
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications |
title_fullStr |
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications |
title_full_unstemmed |
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications |
title_sort |
ieee access special section editorial: advanced information sensing and learning technologies for data-centric smart health applications |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Smart health is bringing vast and promising possibilities on the road to comprehensive health management. Smart health applications are strongly data-centric and, thus, empowered by two key factors: information sensing and information learning. In a smart health system, it is crucial to effectively sense individuals’ health information and intelligently learn from its high-level health insights. These two factors are also closely coupled. For example, to enhance the signal quality, a sensing array requires advanced information learning techniques to fuse the information, and to enrich medical insights in mobile health monitoring, we need to combine “multimodal signal processing and machine learning techniques” and “nonintrusive multimodality sensing methods.” In new smart health application exploration, challenges arise in both information sensing and learning, especially their areas of interaction. |
url |
https://ieeexplore.ieee.org/document/9361372/ |
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