Biometric Signals-Based Artificial Intelligence Technologies for Health Assessment
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 3915
Special Issue Editors
Interests: pattern recognition; signal processing; data-driven machine learning methods; speech, speaker and language recognition; biometrics (2D and 3D face and voice); crypto-biometrics (including privacy preserving biometrics); human–machine interaction; detection and assessment of neurodegenerative diseases from biometric signals
Interests: machine learning; deep learning; pattern recognition; modeling behavioral and physiological human data; human activity and gesture recognition; handwriting and voice analysis; human mobility analysis; biometrics; human–computer interaction; detection and assessment of neurodegenerative diseases from biometric signals
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Special Issue Information
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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Keywords
- biometrics for identification and authentication
- e-health assessment based on biometric signals and digital biomarkers
- neurodegenerative diseases, medical imaging, stress, depression, and emotions
- face, voice, gait, handwriting, ECG, and EEG
- machine leaning/deep learning
- convolutional neural networks, recurrent neural networks, and transformers
- transfer learning
- interpretability and explainability
- evaluation metrics
- artificial intelligence and ethics and privacy
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