Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Submission Deadline:  31 August 2019

IEEE Access invites manuscript submissions in the area of Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts.

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, evidence-based medical decision support etc. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely-coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data, and nonlinearly convert the patterns to high level medical insights.

This Special Section in IEEE Access invites academic and industrial experts to make their contributions to smart health big data, empowered by biomedical sensing and computational intelligence technologies. Studies are expected to connect the human body, data, and applications, establish an end-to-end information flow, and convert big data to big impacts. Crucial technologies include wearable sensing, in-home sensing, personal health record establishment, biomedical signal processing, deep learning, big data mining, pattern recognition, and other related techniques. This Special Section will allow readers to identify advancements, challenges and new opportunities in cutting-edge smart health big data innovations.

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

  • Wearable sensing for big data: bio-potential sensing, behavioral sensing, optical sensing, ultrasonic sensing, flexible sensing, emerging wearable imaging, etc.
  • In-home sensing for big data: on-bed sensing, sleep quality sensing, activity sensing, mobility sensing, fall detection, rehabilitation monitoring, etc.
  • Personal health big data: cardiac monitoring, cardiopulmonary monitoring, brain function monitoring, mobility monitoring, life style monitoring, etc.
  • Signal quality enhancement in wearable big data sensing
  • Biomedical signal processing for smart health big data
  • Feature extraction and critical feature selection from smart health big data
  • Automatic feature mining using deep learning from smart health big data
  • Dimension reduction for effective learning from smart health big data
  • Time series, image and unstructured data fusion
  • Data mining from large databases for pattern and correlation finding
  • Knowledge discovery in smart health big data
  • Telemedicine, internet of medical things, mobile health, remote health monitoring
  • Precision medicine exploration based on smart health big data
  • Medical relevant insight learning from long-term health records
  • Real-time health alerts and long-term health trend analytics
  • Sleep quality monitoring and analytics with smart health big data
  • Human-computer interaction for rehabilitation and assisted living
  • Lifestyle changing empowered by digital health technologies
  • Smart health big data to empower clinical trials

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, Indiana University-Purdue 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. Hui Zheng, Harvard University & Massachusetts General Hospital, USA


Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  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:
  Prof. Derek Abbott, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: