Low Cost Artificial Ventilator Embedding Unsupervised Learning for Hardware Failure Detection
| Autor: | Marzetti, Sebastián, Peyronnet, Pierre-Alexandre, Barthélemy, Florent, Gies, Valentin, Barchasz, Valentin, Delaey-Langlois, Thomas, Peloux, Daniel, Arlotto, Philippe, Barthélemy, Hervé |
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| Přispěvatelé: | Institut des Matériaux, de Microélectronique et des Nanosciences de Provence (IM2NP), Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Délégation générale de l'armement (DGA), Ministère de la Défense, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Machine Learning
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Embedded Learning Breathing Machine Unsupervised Learning K-Means [SDV.IB]Life Sciences [q-bio]/Bioengineering [INFO.INFO-ES]Computer Science [cs]/Embedded Systems Open source Embedded system Artificial Ventilator [SPI.TRON]Engineering Sciences [physics]/Electronics |
| Zdroj: | IEEE Circuits and Systems Magazine-New Series IEEE Circuits and Systems Magazine-New Series-, Institute of Electrical and Electronics Engineers, 2021, 21 (3), pp.73-79. ⟨10.1109/MCAS.2021.3092539⟩ IEEE Circuits and Systems Magazine-New Series-, 2021, 21 (3), pp.73-79. ⟨10.1109/MCAS.2021.3092539⟩ |
| ISSN: | 1531-636X |
| DOI: | 10.1109/MCAS.2021.3092539⟩ |
| Popis: | International audience; In this paper, a less than $200 artificial ventilator that can be used against COVID-19 pandemic is presented. Using low-cost easy-to-find materials, it has been designed for helping developing countries where supplies for building new medical equipment are limited. It complies with medical requirements, allowing to monitor and adjust ventilation parameters such as tidal volume, maximum intra-lung pressure and breath rate. Even if this ventilator is low cost, focus has been placed on improving its global reliability. Using low-cost recycled materials may lead to mechanical failures, this potential drawback is addressed with an intelligent embedded hardware failure detector implemented inside the microcontroller. Using K-means optimized algorithm, it learns in a short time normal operation corresponding to the couple formed by a given ventilator setup and a patient. In case of a mechanical breakdown, an alert is generated to inform medical staff. First, mechanical, electrical and software architectures of the system are presented, then hardware failure detection algorithm is detailed. Finally, test results done at IRBA using an artificial lung are discussed. The overall project has been published as an open source one on GitHub: https://github.com/iutgeiitoulon/ArtificialVentilator. |
| Databáze: | OpenAIRE |
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