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

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

1 Electrical Engineering Course, Federal University of Piauí (UFPI), 64049-550, Teresina, Piauí, Brazil 2 Department of Electrical Engineering, Federal University of Ceará (UFC), 60020-181, Fortaleza, Ceará, Brazil 3 Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (UFRN), 59078-900, Natal, Rio Grande do Norte, Brazil
* Authors to whom correspondence should be addressed.
Received: 11 September 2013; in revised form: 12 October 2013 / Accepted: 12 October 2013 / Published: 15 November 2013
(This article belongs to the Special Issue Sensors for Globalized Healthy Living and Wellbeing)
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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

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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.

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