Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications

Submission Deadline:  28 February 2019

IEEE Access invites manuscript submissions in the area of Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications.

Smart health is bringing vast promising possibilities on the way to pervasive 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 afterwards intelligently learn from it 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, and especially their interaction areas.

This Special Section in IEEE Access invites academic and industrial experts to make their contributions to information sensing and learning in smart health systems. Studies are expected to build new bridges on many gaps between human subjects and their health insights, leveraging information sensing and learning technologies, such as physiological sensing, motion sensing, multimodal signal processing, health data representation techniques, machine learning, deep learning, data mining, computing platforms, and other related techniques. These technologies are required to build a whole data flow from humans to the health insights we pursue. This Special Section will allow readers to identify advancements, challenges and new opportunities in information sensing and learning for emerging smart health applications.

The topics of interest include, but are not limited to:

  • Physiological sensing: Heart ECG, Brain EEG, Muscle EMG, Optical PPG, Bioelectrical Impedance, etc.
  • Motion and activity sensing: movement, daily activities, behavior, etc.
  • Multichannel biomedical signal sensing and processing
  • Multimodal biomedical signal sensing and processing
  • Motion artifacts suppression techniques in wearable health monitoring
  • Data representation techniques for smart health data
  • Machine/deep learning from smart health data
  • Transfer learning applied to smart health applications with limited data
  • Time series analysis techniques using deep learning
  • Biomedical image and image processing
  • Platforms for computing intensive and/or low power smart health applications
  • Human-computer interaction for assisted living
  • Mobile health and precision medicine applications
  • Medical decision support systems
  • Wearable feedback systems for rehabilitation

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:  Qingxue Zhang, Harvard University, USA

Guest Editors:


  1. Vincenzo Piuri, University of Milan, Italy
  2. Edward A. Clancy, Worcester Polytechnic Institute, USA
  3. Dian Zhou, University of Texas at Dallas & Fudan University, USA & China
  4. Thomas Penzel, Charite University Hospital, Germany
  5. Walter Hu, University of Texas at Dallas & One-Chip Co., Ltd., USA & China
  6. Liang Peng, Huawei Silicon Valley Center, USA

Relevant IEEE Access Special Sections:


  1. Mobile Multimedia for Healthcare
  2. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions
  3. Human-Centered Smart Systems and Technologies

IEEE Access Editor-in-Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: