Low Cost Artificial Ventilator Embedding Unsupervised Learning for Hardware Failure Detection [Society News]
Sebastián Marzetti, Pierre-Alexandre Peyronnet, Florent Barthelemy, Valentin Giès, Valentin Barchasz, Thomas Delaey-Langlois, Daniel Peloux, Philippe Arlotto, H. Barthélemy · IEEE Circuits and Systems Magazine · 2021
In this paper, a less than $200artificial ventilator that can be used againstCOVID-19pandemic is presented. Using low-cost easyto-find materials, it has been designed for helping developing countries where supplies for building new medical equipments 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 set-up 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.