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

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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.

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          Most cited references 38

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              Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

              The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems ideally should spend the least possible amount of time in their calibration. An autocalibration algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity, as accurately as possible. This paper describes a new autocalibration methodology for nonlinear intelligent sensors based on artificial neural networks, ANN. The methodology involves analysis of several network topologies and training algorithms. The proposed method was compared against the piecewise and polynomial linearization methods. Method comparison was achieved using different number of calibration points, and several nonlinear levels of the input signal. This paper also shows that the proposed method turned out to have a better overall accuracy than the other two methods. Besides, experimentation results and analysis of the complete study, the paper describes the implementation of the ANN in a microcontroller unit, MCU. In order to illustrate the method capability to build autocalibration and reconfigurable systems, a temperature measurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.
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                Author and article information

                Affiliations
                [1 ] Electrical Engineering Course, Federal University of Piauí (UFPI), 64049-550, Teresina, Piauí, Brazil; E-Mails: josemenezesjr@ 123456ufpi.edu.br (J.M.P.M.J.); otacilio@ 123456ufpi.edu.br (O.M.A.)
                [2 ] Department of Electrical Engineering, Federal University of Ceará (UFC), 60020-181, Fortaleza, Ceará, Brazil; E-Mail: alberto.alexandre@ 123456gmail.com
                [3 ] Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (UFRN), 59078-900, Natal, Rio Grande do Norte, Brazil; E-Mail: meneghet@ 123456dca.ufrn.br
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: medeiros_eng@ 123456yahoo.com.br ; Tel.: +55-86-3237-1555.
                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                November 2013
                15 November 2013
                : 13
                : 11
                : 15613-15632
                24248278 3871086 10.3390/s131115613 sensors-13-15613
                © 2013 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

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