Dear Colleagues,

Monitoring cardiac and respiratory conditions is of paramount importance for managing patients and individuals at risk for health deterioration. Observing patients’ cardiac health, hemodynamic balance, and respiratory stability is a matter of routine care in various hospital settings in order to use in early warning systems and to trigger timely intervention by health-care professionals. These same parameters are also of importance to assess the health status in remote and home monitoring settings.

Novel wearable sensors based on electrical, inertial, optical, inductive, or ultra-sound technology, to name a few, are offering access to unprecedented data, thanks to their non-invasive and unobtrusive design. This new stream of data, however, needs to be carefully processed so as to extract high-level clinical information for communication and interpretation by other systems or for supporting clinical decisions made by the end user. Machine learning and deep learning technologies represent a key resource to generate algorithms that are able to process patient data and provide insight into the right form and type of data for clinical interpretation.

In this Special Issue, we invite contributions addressing the development and validation of wearable sensors and algorithms to measure activity, as well as cardiac, hemodynamic, and respiratory health, in humans. Our goal is to gather scientific contributions in the area of novel measurements of health and innovative computational methods for deterioration detection.

Prof. Alberto Giovanni Bonomi
Dr. Jens Muehlsteff
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Patient monitoring
• Early warning score
• Health risk stratification
• Vital signs
• Cardiac arrhythmia
• Activity recognition
• Hemodynamic instability
• Respiratory health
• Home monitoring