ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF

MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A

NEONATAL INCUBATOR

Alberto A. M. Albuquerque, Arthur P. S. Braga, Bismark C. Torrico and Otac´ılio M. Almeida

Department of Electrical Engineering, Federal University of Ceara, Fortaleza, CE, Brazil

Keywords:

Artiﬁcial Neural Network, Neonatal Incubator, Temperature and Humidity Estimation.

Abstract:

This paper seeks to estimate through Artiﬁcial Neural Networks the future behavior of temperature and hu-

midity inside an incubator. This goal is motivated by the observation that the model-based predictive control

is an interesting alternative for the generation of control signals of a neonatal incubator since: (i) it seeks

to optimize a performance criterion that considers the future behavior of this controller, and (ii) restrictions

may be imposed on future control signals. These two features can make more safe and comfortable the mi-

croclimate inside the device for the newborn: variables such as temperature and humidity can be better kept

within the limits of technical standards such as the NBR IEC 601-2-19 and its amendment No. 1, NBR IEC

60601-2-19-2000. However, one predictive model of the process to be controlled must ﬁrst be obtained. The

obtained neural model has accuracy in predicting the incubator behavior one time step forward compatible

with the technical standard, and it is ready to be applied in a predictive control structure.

1 INTRODUCTION

A neonatal incubator is an important electro-medical

equipment used in a neonatal care unit to assist the

care of premature infants or newborns with some kind

of diseases (Barbosa and Oliveira, 2002). At Brazil,

in 2007, approximately 50% of deaths of newborns

under one year old occurred in the ﬁrst 27 days after

birth, according to the Brazilian Ministry of Health

(Datasus Minist´erio da Saude, 2009), period during

which the incubators are one of the most important

tools to reduce the risks of mortality and diseases. The

newborn incubator provides an ideal microclimate in

order to minimize newborn’s heat and water losses,

which is vital for the survival of premature or criti-

cally ill infants, through the control of internal tem-

perature and relative humidity (Barbosa and Oliveira,

2002).

Neonatal incubators as well as other electro-

medical equipment suffer loss of calibration over

time, compromising the control system and causing

serious damage to the newborn or even resulting in

the death of this (Barbosa and Oliveira, 2002; Arone,

1993; Farges et al., 1998). The Brazilian technical

norm NBR IEC 601-2-19 and its amendment No. 1,

NBR IEC 60601-2-19 2000, provide speciﬁcations

over neonatal incubators to offer a safe environment

to newborns. These requirements are veriﬁed by per-

forming several tests which include the application of

input signals to the temperature, humidity and air ﬂow

actuators, and recording and analyzing the tempera-

ture, humidity and air ﬂow data in speciﬁc points in-

side the neonatal incubator (ABNT, 2000).

The optimal control of environments variables of

the the neonatal incubator, provides a ideal microcli-

mate to safety newborn development. From the view-

point of control theory, the neonatal incubator can

be seen as a TITO system, coupled, where the tem-

perature and humidity are controlled variables.This

system contains strong nonlinear characteristics, and

its modeling through the phenomenological analysis

of the process is a complex task.From models with

a truer representation of the process, more efﬁcient

controllers can be designed (Camacho and Bordons,

2003), among controllers used in the non-linear pro-

cesses, non-linear predictive controller, is an alterna-

tive. The predictive controller is strongly dependent

on the plant model (Camacho and Bordons, 2003).

The use of identiﬁcation techniques for nonlinear sys-

tems is necessary to ﬁnd an efﬁcient model that rep-

resents the system behavior.

Based on the previous, this work proposes a model

of behavior of the temperature and humidity at points

speciﬁed by the standard (ABNT, 2000).This work

276

A. M. Albuquerque A., P. S. Braga A., C. Torrico B. and M. Almeida O..

ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY INSIDE A NEONATAL INCUBATOR.

DOI: 10.5220/0003791502760281

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 276-281

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Prototype of the incubator.

uses artiﬁcial neural networks (Haykin, 2008) as a

technique for the identiﬁcation of nonlinear systems,

to the identiﬁcation of signals of temperature and hu-

midity in the positions deﬁned by the standard, as

only a single temperature and humidity sensor in-

stalled in the exhaust air of the incubator. The model

is intended to found its future use in a predictive con-

troller, acting as the controller model and also insert-

ing the restrictions provided by the standard output.

This work is organized in the following topics: sec-

tion 2 describes the prototype of the incubator used

in the experiments. The section 3 discusses a review

about artiﬁcial neural networks, in particular the mul-

tilayer perceptron architecture. The 5th section are

shown the tests and the results obtained and the sec-

tion 6 tells about the conclusions and ﬁnal comments.

2 THE INCUBATOR PROTOTYPE

The incubator prototype built in the GPAR/DEE/UFC

(Research Group in Robotics and Automation) re-

search laboratory is shown in Figure 1.

The prototype is divided into two basic parts: the

supporting structure with aluminum rods and wood

planks and the acrylic dome with porthole windows

similar to commercial models of incubators. In

the supporting structure, just below the dome, is an

acrylic duct forming an air circulation system of the

incubator, where are installed a resistive heater and a

cooler on opposite ends of the circuit. Below the air

circulation system are the boards of control systems

of heating, ventilation and relative humidity. Also be-

low the ventilation system is located a commercial hu-

midiﬁer adapted in moisture control. The control sys-

tem of the incubator can be divided into 3 subsystems

of control: temperature control system, control sys-

tem of moisture and ventilation system. The systems

of control of temperature and humidity are connected

to an acquisition board of National Instruments

(NI) to connect these two systems with Matlab

Figure 2: Spatial distribution of sensors inside the incuba-

tor.

software. The ventilation system operates indepen-

dent of the acquisition card, being that this was ad-

justed to maintain a constant ventilation speed of ap-

proximately 0.35 m/s, in accordance with the stan-

dard NBR IEC 601-2-19. The interior of the dome

is thermally isolated through a cork board of 24mm

thickness. Temperature and humidity sensors were in-

stalled in the interior of the dome following the provi-

sions in standard NBR IEC 601-2-19 and in the air of

the dome. Figure 2 shows the arrangement of sensors

on the board of cork. The sensors at positions B, C,

D, E, are temperature sensors LM35 (Semicondutors,

2000), while the sensors A and F are temperature and

humidity of type SHT75 (Sensirion, 2010).

The sensors A, B, C, D and E remain inside the in-

cubator during the tests, only being removed from the

dome to the normal operation of incubator because

sensors occupy the positions where the newborn is

placed. The sensor F, which is not speciﬁed by the

standard (ABNT, 2000), remains during the tests and

the normal operation of the incubator. From measure-

ments of the sensor F, performed during calibration, it

is estimated a dynamic model of temperature and hu-

midity in the interior of the dome. Through the model

we intend to evaluate, during normal operation of the

incubator, if there was deterioration in the calibration

of the incubator. In the next section describes neu-

ral network used to perform the construction of the

model of the signals of temperature and humidity.

3 MULTILAYER PERCEPTRON

NEURAL NETWORK

In this paper, it is used a neural network MLP (Multi-

layer Perceptron) (Haykin, 2008) to estimate the sig-

nals of temperature and humidity in the interior of the

dome’s incubator. The MLP is formed by neurons

distributed on layers that have high connectivity with

layer neurons following in a feed forward conﬁgura-

tion (Figure 3). The propagation of the signals takes

ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY

INSIDE A NEONATAL INCUBATOR

277

Figure 3: Multilayer perceptron network architecture,

(Haykin, 2008) adapted.

place via the input layer to output layer, surrounding

layers of neurons are connected to each other,the con-

nection between two neurons i and j is weighted by a

weight wij. Such parameters should be adjusted in

sense to minimize the error between the value to be

estimated (the training) and the MLP’s output. It is

shown below an algorithm to perform the adjustments

(the backpropagation algorithm) (Haykin, 2008):

The backpropagation algorithm.

1. Initialize the weights with small values arbi-

trary or random.

2. Randomly choose an input pattern.

3. Propagate the signal through the network.

4. Calculate δ

in the output layer.

= g

′

· (h

) · [d

− y

]

where h

represents the net input to the i

unit in the I

h layer, and g’ is the derivative

of the activation function.

5. Compute the deltas for the preceding layers

by propagating the errors backwards;

= g

′

· (h

)

∑

j+1

· δ

6. Update the weights using ∆w = α· δ

· y

7. Go to step 2 and repeat for the next pattern

until the error in the output layer is below a

prespeciﬁed threshold or a maximum num-

ber of interactions is reached.

(Haykin, 2008)

This algorithm can be divided into two steps: the

propagation step forward and step of back propa-

gation. In propagating forward, the training stan-

dards are presented the network with no change in

the weights of the connections; the signals are calcu-

lated individually neuron by neuron, ﬂowing of layer

in layer to produce a result in the outputlayer. This re-

sult is then compared with the desired output, and the

error is calculated. The second step is called back-

propagation: the error is calculated from the output

layer and back propagated by intervening layers until

the input layer and the weights of the connections are

modiﬁed as the error is propagated backward. Exam-

ples of training set are presented the network until it

reached a stop criterion is satisﬁed. Two stop criteria

normally used are (Haykin, 2008):

• The norm of the gradient vector of the error sur-

face in relation to the vector of weights has a suf-

ﬁciently small value.

• The minimum value for the mean quadratic error

be reaching, choosing this criterion does not guar-

antee that the algorithm reaches this value.

4 THE PREDICTIVE CONTROL

PROBLEM FORMULATION

The model-based predictive control reﬂects human

behavior that it is thought to lead to the best predicted

output over same limited horizon. To get this, on

internal model of the process in question should be

used. Hance a predictive control law has the follow-

ing components:

1. The control low depends on predicted behavior

and input/output.

2. The output prediction are computed using a pro-

cess model and constraints mutualities.

3. The current input is determined by optimizing

a predictive performance index subjected to in-

put/output constraints.

4. The control input is updated at every sampling in-

stant.(The receding horizon strategy)

According to (Rossiter, 2004), to solve the constraint

optimization predictive control correspond to solve

the following problem :

min

∆U

s.t C∆U − dk

where ∆U is the control increments.

This is known as quadratic programming(QP)

problem for witch solvers are easy to ﬁnd (Rossiter,

2004). Its necessary to remember that:







∆U

−∆U

U − Lu

k−1

−U − Lu

k−1

Y − QDU

←

− PY

←

−Y − QDU

←

− PY

←







Where ∆U and ∆U are the control increments maxi-

mum and minimum limits respectively. Y and Y are

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

278

0 500 1000

100

Signal Aplied to Resistence

Time (min)

%Max Power

0 500 1000

100

Signal Aplied to Nebulizer

Time (min)

%Max Power

Figure 4: Signals applied to nebulizer and warm resistance.

set of temperatures and humidity inside neonatal incu-

bator. Whose models are inferred by proposed neural

network.

Y =





where T

with i=(A,B,C,D,E) is the standard temper-

ature value according to ﬁgure 2 and U is standard

humidity value.

5 EXPERIMENTAL RESULTS

For the MLP training, four experiments were made

and the sensors’ measures were stored.The ﬁgure 4

shown the signals applied in the warm resistance and

nebulizer.The signal applied on the resistance is the

percentage of duty cycle where the max duty repre-

sents a tension of 220V AC over the resistance and

the signal applied to nebulizer represents the percent-

age ﬂow of water vapor emitted by the nebulizer.

The data was acquired through an acquisition

board of National Instruments

and Matlab

Each experiment in the incubator prototype (Section

3) had mean duration of 210 minutes, with the sensors

positioned in concordance the NBR IEC 60601-2-19.

The dates of these four experiments were joined and

reorganized randomly and 70% are used for training

and 30% was used for validation of neural network.

The signals of temperature and humidity in the air exit

after the application of signal in the incubator’s actu-

ators is shown in ﬁgure 5. Network training is done

off-line, ie, is performed with the data obtained in ex-

periments carried out previously. The stopping cri-

terion used for training the network, was to obtain a

minimum mean square error or the execution of a total

of 1000 epochs, whichever comes ﬁrst. The value of

the target MSE(Mean Square Error) was 0.001, how-

ever this target was never reached. The MLP network

0 500 1000

Temperature in the air exit

Time (min)

ºC

0 500 1000

Humidity in the air exit

Time (min)

RH%

Figure 5: Signals measured of air exit temperature and hu-

midity.

uses sigmoid activation functions in hidden layer neu-

rons and linear function in output layer’s neurons. Af-

ter numerous tests with different number of neurons

in the hidden layer, the 4-8-6 MLP had the best re-

sults when compared the performance index MSE.

The mean learning time of the net was 120 seconds

with one hundred epochs, after this number of epochs

the performance index not change. The weights of

neural network after the training are presented below:

ILW =







6, 95 5, 05 0, 70 2, 47

−0, 30 0, 13 −0, 07 −0, 11

0, 89 −1, 60 −1, 06 1, 47

0, 01 −0, 56 0, 01 0, 02

−1, 07 −0, 65 0, 06 0, 24

−5, 81 −1, 44 2, 34 7, 47

−0, 55 −1, 04 −1, 79 −5, 24

−1, 98 −1, 05 −1, 23 1, 29







HLW







−0.13 −0.45 −0.52 −0.30 −0.36 0. 33

−4.25 −6.96 −3.58 −3.74 −9.44 −0.98

0.22 0.26 0.28 0.21 0. 14 −0.05

0.44 0.45 0.49 0.46 0. 46 −1.76

−0.81 −0.76 −0.82 −0.83 −0.71 −0.07

−0.05 −0.05 −0.03 −0.05 −0.02 0. 14

−0.18 −0.23 −0.25 −0.20 −0.10 0. 22

−0.15 −0.14 −0.21 −0.17 −0.03 0. 04







Where ILW is the matrix weights of input layer

and HLW is the matrix weights of hidden layer.

5.1 Results of training

In the Figure 6 shown the worst result of training of

the temperature signal and the humidity signal. The

inputs of the artiﬁcial neural network are the signal of

temperature in the air exit (Sensor F - SHT75 - Figure

2), the signal of humidity in the air exit (Sensor F -

SHT75 - Figure 2), the signal applied to the heating

element and the signal applied in the nebulizer. The

targets of the network are the signals of temperature

measured in the positions A, B, C, D and E (shown

in Figure 2 and the signal of humidity measured in

position E. The mean square error was used as the

performance index.

ARTIFICIAL NEURAL NETWORKS IN THE ESTIMATION OF MEASURES OF TEMPERATURE AND HUMIDITY

INSIDE A NEONATAL INCUBATOR

279

8810 8820 8830

Sensor C

Time(s)

Temperature ºC

Real

0 5000 10000 15000

−1.5

−1

−0.5

0.5

1.5

Error −Sensor C

Time(s)

Error

Figure 6: Comparisons, and error, between MLP outputs

and the training data of temperature (worst case).

TA TB TC TD TE HE

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Mean Square Error

MSE

Figure 7: The mean squared error (MSE) between the signal

of network and signal real for each sensor (training set).

Figure 7 summarizes the MLP performance with

the mean squared error for each of the six considered

sensor. The trained network successfully learned the

mapping between the signals and desired output of the

training set, as seen by low mean square error values

that were in the order of 0.18.

5.2 Results of validation

The remaining 30% of data were used to validate the

trained network. The mean squared error between

network’s outputs and the desired outputs are shown

in Figure 8.

As one can be see, the mean squared error of the

validation set shows values close to 0.08 for the tem-

perature sensors, and the value of 0.16 for the humid-

ity sensor. Figure 9 shows curves obtained with the

validation set. Since the standard NBR IEC 60601-

2-19 requires that the value of temperature do not

varies above0.5

◦

C during one hour of service, and the

value of relative humidity do not differ 10% of the de-

ﬁned by standard, the model errors in some points are

above the standard requirements but the purpose of

the model is just to give an indication that the sensor

TA TB TC TD TE HE

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Mean Square Error

MSE

Figure 8: The mean squared error (MSE) between the sig-

nals of network and real signals for each sensor (validation

set).

3260 3270 3280 3290 3300

Sensor C

Time(s)

Temperature ºC

Real

0 2000 4000 6000

−1.5

−1

−0.5

0.5

1.5

Error −Sensor C

Time(s)

Error

Figure 9: Comparisons, and error, between MLP outputs

and the validation data of temperature (worst case)

measures the equipment are suffering a breakdown

and that a more precise test calibration must be done.

Therefore the model is useful to determine when the

incubator is out of the norm prescriptions, alerting the

operator which the equipment needs to be sent for

maintenance.

6 CONCLUSIONS

The MLP network developed in this work, have suc-

cess in the estimation of model of the temperature and

humidity of the neonatal incubator, enabling a model

where is possible formulate a predictive control prob-

lem with constraints to satisfy the standard speciﬁca-

tions. As future work we intend to apply another neu-

ral network topologies (ex. RBF, SVM, SOM), and

other non linear mapping techniques such Kalman ﬁl-

tering and NARMAX models in order to have a map-

ping of signals with greater accuracy, and the devel-

opment of a prototype device that is attached to the

incubator to check in real time with the models devel-

oped. As future work is required to develop a predic-

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

280

tive controller to apply the model achieved in this ar-

ticle, and to study new topologies of neural networks

to perform a comparative performance between the

models estimated on these topologies in the applica-

tion of predictive control.

ACKNOWLEDGEMENTS

The authors are thankful to FUNCAP (Fundac¸˜ao

Cearense de Apoio ao Desenvolvimento Cient´ıﬁco e

Tecnol´ogico) for the ﬁnancial support.

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INSIDE A NEONATAL INCUBATOR

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