IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications

• Main Authors: Qingxue Zhang, Vincenzo Piuri, Edward A. Clancy, Dian Zhou, Thomas Penzel, Wenchuang Walter Hu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Online Access: https://ieeexplore.ieee.org/document/9361372/

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.