Biometric Signals-Based Artificial Intelligence Technologies for Health Assessment

Dear Colleagues,

Biometrics, in the scientific and the industrial circles, refers to technology consisting in identifying or authenticating people based on their biometric signals, i.e., their physiological and behavioral traits, such as their voice, face, iris, handwriting, gait, etc. This field has been the subject of research for decades, and technological solutions have been deployed at a large scale for access control in airports, government facilities, schools, etc. The main factors behind these success stories are twofold: the availability of large biometric datasets, and the systematic application of effective machine learning and, more recently, deep learning models for classification.

Driven by their success in the security context, more recently, researchers have started to investigate the harnessing of biometric signals in the e-health context, with the objective of developing digital biomarkers that can serve as an aid to diagnose medical conditions or for detecting a health status, such as stress or depression. These endeavors, however, are facing several challenges associated with the lack of sufficient training data related to e-health, the need for decision understandability by the stakeholders, and the optimal specificity and sensitivity required for these tools to be usable in practice. As a result, the artificial intelligence techniques considered in the e-health field should, among others, be based on sound transfer learning from the security context to the health one, be as interpretable as possible, and be assessed according to sound evaluation metrics.

This Special Issue aims at gathering recent interdisciplinary research advancements in artificial intelligence algorithms, particularly advanced machine/deep learning methods that harness biometric signals to develop tools for aiding diagnosis and, in general, to assess an individual’s health status. The Special Issue seeks to bring together academics, physicians, and industry professionals to contribute to and discuss the latest research and innovations in this field.

Dr. Dijana Petrovska-Delacrétaz
Prof. Dr. Mounim A. El Yacoubi
Guest Editors

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