The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process.

A computational framework to support the treatment of bedsores during COVID‑19 difusion / DI MARTINO, Ferdinando; Orciuoli, Francesco. - In: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING. - ISSN 1868-5137. - (2022). [10.1007/s12652-022-03886-x]

A computational framework to support the treatment of bedsores during COVID‑19 difusion

ferdinando di martino;
2022

Abstract

The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process.
2022
A computational framework to support the treatment of bedsores during COVID‑19 difusion / DI MARTINO, Ferdinando; Orciuoli, Francesco. - In: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING. - ISSN 1868-5137. - (2022). [10.1007/s12652-022-03886-x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/886730
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