Machine Learning and Biomedical Sensors

Dear Colleagues,

The increase in the information collected by an ever-growing number of biomedical sensors connected to the internet, together with the availability of a network of services recording any biological, physical, clinical and other kinds of data, has made the soil fertile for the significant use of machine learning (ML). It has become possible to use the various biomedical devices in the most diverse environments (at the depths of the seas, high altitudes, in space) in hospitals as well at home or outdoors, opening up new scenarios that were not previously conceivable. In this context, machine learning has shown great potential and might provide solutions to many issues concerning life on our planet. This Topic aims to present the most recent and innovative solutions leveraging the interplay between biomedical sensors and machine learning. Advanced and modern data-driven methods and learning approaches are sought to correlate and understand heterogeneous data in providing accurate classifications and predictions. The perspective opened by pervasive and edge computing should be properly transferred to the biomedical domain by devising novel activity monitoring and physiological computing paradigms. From the point of view of innovative sensing technologies, new transducers coupled with embedded computing for obtaining smart and possibly miniaturized or minimally invasive devices should be investigated. In such a panorama, the role of machine learning and artificial intelligence, more generally, should be adequately understood together with the issues related to their perception. In addition, user acceptance and privacy issues are important aspects to be assessed in real experimentation, which is necessary for clinical validation of the proposed technological solutions. The Topic, through its participating journals, is therefore seeking contributions that explore multifaced aspects of the convergence between biomedical sensors and machine learning: from fundamental elements related to computing over sensor networks and federated learning to innovative sensing principles and technologies for smart devices, from clinical experimentation and validation in healthcare scenarios to general application in ambient assisted living, contextually with the concurrent assessment of privacy and user acceptance factors.

• Human physiology & physiological computing
• Multimedia data analysis
• Digital signal and image processing
• Computer vision in biomedical sensing
• New materials and approaches for smart biomedical sensors
• Artificial intelligence over networks of biomedical sensors
• Protocols and middleware for smart biomedical sensors
• Sensors for Active and Healthy Ageing
• Internet of Biomedical Things (IoBT)
• Pilot studies and clinical validation
• User experience and acceptance of Artificial Intelligence in biomedical sensors
• Privacy and security issues
• Big Data
• Teleassistance & telemedicine
• Signals analysis & statistics methods

Dr. Massimo Martinelli
Dr. Davide Moroni
Prof. Dr. Aleš Procházka
Topic Editors