Sensors 2013, 13(11), 15613-15632; doi:10.3390/s131115613
Article

Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Júnior 1,* email, Júnior 1,* email, 2email, 1email and 3email
Received: 11 September 2013; in revised form: 12 October 2013 / Accepted: 12 October 2013 / Published: 15 November 2013
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.
Keywords: sensors; inferential neural network; nonlinear identification; neonatal incubator; certification procedure
PDF Full-text Download PDF Full-Text [2948 KB, uploaded 21 June 2014 10:16 CEST]

Export to BibTeX |
EndNote


MDPI and ACS Style

de Araújo, J.M., Júnior; de Menezes, J.M.P., Júnior; Moura de Albuquerque, A.A.; da Mota Almeida, O.; Ugulino de Araújo, F.M. Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network. Sensors 2013, 13, 15613-15632.

AMA Style

de Araújo JM, Júnior, de Menezes JMP, Júnior, Moura de Albuquerque AA, da Mota Almeida O, Ugulino de Araújo FM. Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network. Sensors. 2013; 13(11):15613-15632.

Chicago/Turabian Style

de Araújo, José M., Júnior; de Menezes, José M.P., Júnior; Moura de Albuquerque, Alberto A.; da Mota Almeida, Otacílio; Ugulino de Araújo, Fábio M. 2013. "Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network." Sensors 13, no. 11: 15613-15632.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert