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Behavioral Biometrics for eHealth and Well-Being

Submission Deadline:  31 September 2020

Submission Deadline: 31 September 2020

IEEE Access invites manuscript submissions in the area of Behavioral Biometrics for eHealth and Well-Being.

Artificial Intelligence (AI) is changing the healthcare industry from many perspectives. A very challenging issue deals with the development of non-intrusive AI technologies that could be integrated into everyday activities, thus allowing continuous health state monitoring and enabling automatic warnings when a dangerous change is predicted. Behavioral biometrics play a crucial role within this challenge. Behavioral biometrics, such as speech, handwriting, gait, etc. can be used to quantify human physiology, pathophysiological mechanisms, and actions. The final acquired signal is a mixture of at least four components:

  • The physical one, which enables the user to make the action (e.g. mouth, lips, tongue, etc.);
  • The cognitive one, which deals with mental abilities (learning, thinking, reasoning, remembering, problem-solving, decision-making, and attention);
  • The learned one, which deals with culture, habits, personalization, etc.;
  • The contingent contour one, which deals with the acquisition device, the emotional state, the specific task to be performed, etc.

It is evident that disease at its early stage, as well as during its course, could affect one or more of these components. Behavioral biometrics in eHealth seek solutions to diagnose, assess, and monitor diseases that are measurable just when the patient performs an action. This action could be walking, talking, writing or typing on a touchscreen, and many more. Behavioral biometrics also deal with the way the human being responds to natural and social events around her/him and emotions. The adoption of non-intrusive behavioral biometrics techniques in the set of daily activities would be pervasive: the user would be asked to do what she/he already does normally. The output of these systems could be provided to doctors, thus helping them in a deep disease inspection. At the same time these technologies could be directly adopted by doctors. These aspects are extremely important for the development of Computer Aided Diagnosis (CAD) tools. Nevertheless, specific behavioral biometrics tasks and activities could be planned to support rehabilitation activities.

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in behavioral biometrics for e-health and well-being. The goal is that it provides an opportunity to gain a significantly better understanding of the field’s current developments and future direction.

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

  • Signal processing techniques
  • Pattern Recognition techniques
  • Computer Vision techniques
  • Artificial Intelligence techniques
  • Continuous learning and recognition
  • Acquisition tools, procedures and protocols
  • Biometrics data mining
  • Wearable and non-intrusive sensors
  • Brain signals analysis for disease and emotional states recognition
  • Eye movement analysis for disease recognition
  • Face analysis for disease and emotional state recognition
  • Gait analysis for disease and emotional state recognition
  • Handwriting analysis for disease and emotional state recognition
  • Keystroke dynamics for disease and emotional state recognition
  • Sleep analysis for disease and emotional state recognition
  • Speech analysis for disease and emotional state recognition
  • Biometric data and clinical data fusion
  • Multiple behavioral biometrics
  • Development of complete CAD systems
  • Real-time health alerts and long-term health trend analytics
  • Applications

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

 

Associate Editor:  Donato Impedovo, University of Bari Aldo Moro, Italy

Guest Editors:

    1. Thurmon Lockhart, Arizona State University, United States
    2. Jiri Mekyska, Brno University of Technology, Czech Republic
    3. Bijan Najafi, Baylor College of Medicine, United States
    4. Toshihisa Tanaka, Tokyo University of Agriculture and Technology, Japan

 

Relevant IEEE Access Special Sections:

  1. Data-Enabled Intelligence for Digital Health
  2. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
  3. Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams


IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: donato.impedovo@uniba.it.