학술논문

IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications
Document Type
article
Source
IEEE Access, Vol 9, Pp 30404-30407 (2021)
Subject
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Language
English
ISSN
2169-3536
Abstract
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.