Parameters Modelling and Fuzzy Control System of Neonatal ...

SETIT 2009
5 th International Conference: Sciences of Electronic,
Technologies of Information and Telecommunications
March 22-26, 2009 – TUNISIA
Parameters Modelling and Fuzzy Control
System of Neonatal Incubators
Pierre ELE * , Jean Bosco MBEDE ** and Edouard ONDOUA ***
* ENSP of University of Yaoundé I and IUT of University of Douala Cameroon
pierre_ele@yahoo.fr
** Department of Physics of University of Yaoundé I
jbmbede@neuf.fr
*** ENSP of University of Yaoundé I
eondoua@yahoo.fr
Abstract: Incubators are of great interest for some newborns, especially if they are weak, low-birth-weight, sick,
preterm…Some parameters are to be monitored and their accuracy remains an important matter. Temperature and
humidity remain the most important. This work is focused on the control problem of these parameters. The paper
presents a model of heat exchange between the newborn and its environment and a robust fuzzy control algorithm.
Digital simulation results show that the designed system has good output accuracy for a high dynamic in the input
range. A hardware design to host the system and potential practical applications are also presented.
Key words: neonatal incubator, modelling, fuzzy control, heat transfer.
INTRODUCTION
Since about 160 years, the incubators have been
used to create and maintain a comfortable and
healthful hydrothermal environment for low-birthweight,
sick or preterm newborn babies. The
conception of these devices has evolved in the time
and their goals. Currently, most of them have to keep
humidity and temperature in optimal ranges set by
medical staff. Reasons are the followings.
The newborn baby has all the capabilities of a
mature homeotherm, but the range of environmental
temperature over which an infant can operate
successfully is severely restricted. The infant has
several disadvantages in terms of thermal regulation.
An infant has a relatively large surface area, poor
thermal insulation, and a small amount of mass to act
as a heat sink. The newborn has little ability to
conserve heat by changing posture and no ability to
adjust its own clothing in a response to thermal stress.
Responses may also be hindered by illness or adverse
conditions such as hypoxia (below normal levels of
oxygen). An appropriate thermal environment
decreases the rate of preterm infants’ morbidity and
mortality. A reduction of 22% or more in the mortality
rate was found when neonates were nursed in
incubators with carefully air controlled temperature. In
studying a control system an interesting question is to
determine what variables can be considered as input,
that is, which stimuli control heat production?
Adamsons, Gandy and James [2] find that the rate of
oxygen consumption of the newborn infant is
predominantly a function of the temperature gradient
between body surface and the environment, rather
than absolute values of either deep body or surface
temperature. If heat production were controlled by
heat flow, a flow sensor would be necessary, or a
sensor to measure the temperature difference between
skin and environment.
Furthermore, another media of heat exchange
between the neonate and its environment is the water
loss through the skin and by respiration. When the
incubator air temperature is constant, an increase in
the air relative humidity (RH) value reduces the skin
cooling and increases the body heat storage. Air RH
values close to 65% prevents excessive body water
loss and improves the maintenance of body
temperature. The evaporation rate when the air is at
60% RH is approximately 40% lower than that
observed at a lower relative humidity (e.g. 40%) [11].
Therefore, some incubators have active or passive
systems to control the internal humidity. In [3, 4, 6],
schemes built to deal with this issue are described.
Some other studies investigating physical processes in
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incubators have carried out numerical or experimental
techniques [5, 8, 9]. The major objectives of these
studies are heat losses of the neonate, temperature
distribution, air flow, humidity control and a better
insight of the thermal interactions between the neonate
and its surrounding environment. In this paper, the
problem of heat transfer modelling is tackled, in the
perspective of robust temperature control.
The rest of the paper is organized as follows. In the
next section, the description of the process with
relevant signals is presented. The thermal
characterisation of the neonate and its environment is
complex and not well understood. But, they determine
the actual temperature in the incubator. A
mathematical heat transfer model is presented in the
section two. Our model is combining passive heat
losses and active heat production both from internal
(infant) and external sources. A robust control system
is presented in the third section, followed in section
four by simulation results validating our approach. We
then present potential applications of our system.
Finally, conclusions are addressed with attention
paid to neurofuzzy control or energy efficiency as
future work.
1. Process description
1.1. Neonate’s room
It has been drawn in the figure 1 the simplified cross
section of the chamber where babies are laid down.
d
w
Figure 1. Room for babies. Arrows indicate heating
air flow.
Walls are in plexiglas or such kind of material. A
mattress is sketched inside the room. The room is not
hermetic. Many small holes are provided for air
admission and expelling. There are also holes to
connect other monitoring apparatus to the baby in the
incubator. Room dimensions are approximately:
Wall width d between 5 and 10 mm
Height h between 40 and 80 cm
Width w between 50 and 100 cm
Length l between 70 and 150 cm
The control system will be located at the bottom of
the room. Sensors are placed on air inlets and outlet or
tapped on the infant skin.
h
1.2. Signals description
1.2.1 Temperature
Temperature is kept on a fixed value that medical
staff can choose around values between 27 and 39°C.
The precision around a setpoint Ts is ±δ where δ≤0.5
°C. The temperature must be kept in these bounds
though air is being renewed. If the temperature falls
out of the bounds, an alarm should sound and/or
display. Temperature is considered as the most critical
parameter. Particularly, for preterm neonates, the
internal control mechanisms of temperature are not
well developed as in an adult and the survival depends
on external control. For improving reliability, heating
with resistors and redundant temperature sensing are
proposed. For more universality of our system, the
range of controlled temperature will be enlarged, as
air, skin and core temperature (to name a few) will be
controlled.
1.2.2 Humidity
The simplest way to moisten the room air is to
force (by a fan) the air flux passing above a water tank
before it goes through holes to babies’ room. The
adjustment of the air-water contact surface leads to the
hygrometric regulation. The value can be set to a point
between 44 and 95%. Levels recommended in the
literature are located between 65 and 90%. As till to
now, this parameter needs not high precision, its
adjustment is done sometimes manually. Humidity is
linked to temperature. But the important point is to
make sure that the fan is running. Otherwise, an alarm
must sound. The alarm sounds also if water level in
the tank is too low.
1.2.3 Oxygen
Oxygenation can be obtained without a particular
effort with the air circulation. And holes are on the
walls of the room. Oxygen consumption measurement
can also inform on the metabolic heat production.
1.2.4 Breathing signal
The matter is that it happens for baby to "forget"
breathing (apnoea). The outcome of this might be the
death. One must then be sure the baby is breathing
normally. Expelled and incoming air to lungs have
different temperatures: thus, as a simple and low cost
sensing system, current/potential difference variations
across a thermistance located at the baby nose can be
monitored. But the two temperatures can be very close
and their difference is a time varying phenomenon.
Moreover, baby’s movements constitute artifacts
sources that can be superimposed to the signal. Preprocessing
is useful to avoid false decision. An alarm
is also needed here if the breathing is going wrong.
1.2.5 Other signals
Miscellaneous other signals, particularly from the
baby body (weight, baby temperature,
electrophysiological signals as ECG or EMG…) are
now generally sensed and processed by powerful
external equipments. They are foreseen in our system,
but are not our concern for the moment
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Digital signal processing to be done includes but is
not limited to filtering for all these parameters to get
ride of noise, compression algorithm to monitor some
parameters, as memory space will be narrow for this
low cost system
2. Heat exchange modelling
The flow of the air and position of circuit elements
are sketched below in figure 2.
Air pushed
by the fan
Water
tank
Heating
resistor
Electric
Power
Figure 2. Air flow circuit.
The metabolic heat generated by the baby is either
stored or dissipated to the surrounding environment
due to conduction, radiation, convection, and
evaporation of water from the skin and respiratory
track (see on figure 3).
Figure 3. Heat losses sources.
Temper
ature
sensor
However, since the heat transfer processes of this
study are considered in a steady state, accumulation
terms have not been taken into account. An energy
balance equation can therefore be written as on
equation (1).
Q M + Q H = Q con + Q rad + Q cov + Q evp (1)
Q M is the metabolic heat production term and Q H
the external stimuli is generated by the input energy
source (often a heating resistor as sketched on figure 2
above).
QM can be obtained empirically by the formula (2)
written below [1].
Q M = 3.815 ·VO2 + 1.232 ·VCO2 (kcal/L) (2)
VO2: oxygen consumption
VCO2: carbon dioxide production
Babies
room
Q M can be calculated also by the formula (3).
Q M 0.0522mp 1.64 (3)
Outside
m is the baby weight (kg) and p is the baby’s age
(days).
QH which can be electrical energy is determined
using relation (4).
QH = Mae Ca (Tai-Te) (4)
Mae, Ca and Te are the mass, the specific heat and
the temperature of the external air entering the
incubator. This is the input air arriving on the tank
(see on the block diagram of figure 2). Tai is the
temperature of air entering the chamber (after the
heating block of figure 2).
The right hand terms of equation (1) can be
determined as below.
A - Q con Conduction heat loss
Conduction heat loss between infant and mattress
can be neglected. This is a reasonable assumption
following Wheldon [7] who states that the rate of heat
transfer by conduction is small for a baby lying on a
foam mattress.
B - Q evp Evaporation heat loss
Q evp = m v h fg + Qcov-lu (5)
In the equation (5), m v is the rate of evaporation
from the body (kg/s) and h fg is the enthalpy of
vaporisation of water (kJ/kg). Qcov-lu which is the
convective heat loss in the lungs is proportional to the
difference between core (deep body) and ambient air
temperature.
Qcov-lu = MaCa(Tcr-Ta) (6)
Ma and Ca are the mass and the specific heat of
the intake air respectively.
C - Q cov Convective heat loss
The convective heat exchange is proportional to
the temperature difference between skin temperature
Ts and ambient one Ta, to hc (W/m 2 °C) and to the
area A as shown by equation (7).
Q cov = hc(Ts – Ta)A (7)
D - Q rad Radiative heat loss
Similarly the radiative heat loss can be formulated
as follows in (8) [7]
Q rad = σε(Ts 4 – Tr 4 )A (8)
In relation (8), σ is the Stefan-Boltzmann constant
and is measured in watts per meter squared per degree
Celsius power 4, ε is the emissivity and Tr is the mean
temperature (°C) calculated from the incubator.
The energy balance equation must be completed
by the heat transfer analysis energy equation [7-8]
below.
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dT
∇ ( k∇T)
= ρc
(9)
dt
In the equation (9), T is the temperature (K), k is
the thermal conductivity (W/mK), ρ is the density
(kg/m 3 ), c is the specific heat (W/kgK), and t is time
(s). The derivative on the right-hand side of equation
is the total derivative:
dT ∂T
∂T
∂T
∂T
∂T
= + ux
+ uy
+ uz
= + u∇T
(10)
dt ∂t
∂x
∂y
∂z
∂t
There, u x , u y , and u z , are the velocity components
of vector u in the x-, y-, z-direction, respectively (m/s).
Since only steady-state problems are studied in this
work, the first term on the right-hand side of equation
vanishes.
The above equations are complemented by the
continuity and momentum equations [21], namely
u 0 (11)
ρ du = F − ∇p
+ µ ∇
2 u
dt
(12)
In (12), p is the pressure (N/m2), F represents the
body force term which in the present case has only a
vertical component Fz = g in the z-direction
(N/m2), g is gravity acceleration (m/s2) and µ is the
dynamic viscosity (Ns/m2).
The Boussinesq approximation was adopted for the
buoyancy term in equation (12). Thus, density takes
the usual form in equation (13).
= T T 0 )) (13)
is the thermal expansion coefficient (1/K), T0
and represent the operating parameters.
3. Fuzzy control system
As an implantation of a control system, the
constraints are that the system must be simple, cheap,
versatile and universal for multiple usages, compact
and reliable. In the other hand, complexity of the
system is such that some uncertainties/assumptions are
not taken into account in models. Singh [10] choice is
a microcontroller with a classical control. Here also, a
microcontroller hardware feedback system can be
proposed, but, with a fuzzy processing algorithm
approach to ensure robustness. Zadeh [15] has
summarised fuzzy logic as a body of concepts and
techniques for dealing with imprecisions, information
granulation, approximate reasoning and computing
with words.
While many different control system algorithms or
control loops exist (e.g. “on/off”, linear or fractional
order control), it is important to chose one well suited
to the application and system requirements. PID
(proportional integral derivative) controllers have been
long known for being effective in control and
regulation of thermal systems. Each of the terms,
“proportional”, “integral” and “derivative” refers to
one of the three basic elements of a PID controller.
Each of these elements performs a different task and
has a different effect on the function of a system. The
system can be described mathematically through
general equation (11) where E(t) is the error, K P , K I
and K D are the proportional, integral and derivative
constants respectively and C(t) is the control output of
the system.
C(t)
t
dE(t)
= K P E(t) + K I∫ E(t)dt + K D
(14)
dt
0
A PID controller attempts to control temperature at
some value T set by looking at the current error, past
error and predicting future error. The output accuracy
depends strongly of the accuracy of the model. Beside
this drawback, a risk of computations overhead for the
18F452 microcontroller also exists. The fuzzy
approach is then proposed.
The 2 inputs variables are temperature and
moisture. Membership functions of triangular shape
were tried with satisfaction. For temperature linguistic
variable considered with n sets, we have the figure 4
representation.
-1
µ degree of
membership
Figure 4. Type of membership functions used.
This standard universe [-1, 1] is postprocessed to
[20°C, 40°C] range.
Inferences were based on set of rules we built. The
Mamdani-type inference was used. The
defuzzification process choice is the most widely used
centre of gravity (centroid) one [15].
4. Simulation results
A sample of simulations curves of the output
signal (temperature or humidity) is given below in he
figure 5.
1
T °C
1
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limit is exceeded and when spare parts no longer exist
or have never existed on the market. Obsolete
apparatus or equipments donated to developing
countries are good examples. Trying to find the
defective electronic component in the control system
is not always applicable. Thus very expensive types of
equipment are thrown away. Note the defective
intelligent part weights just a small percentage of the
overall cost of the equipment.
Upper
Lower
Figure 5. Sample curves showing the control quality
of parameters: temperature (upper) and humidity
(lower).
Output is tracking with a very good accuracy the
setpoint. This is particularly true for temperature. This
is also the case in the literature where tolerances are
around 0.1 and 0.5 degree respectively for temperature
and RH. We are thus optimistic for the actual
implantation.
But, with less than n=4 membership functions, we
had instability. For a set of rules between 8 and 10, we
noticed not a great modification in the accuracy.
5. Potential practical applications
Incubators are often out of service because of a
breakdown of the electronic part, while the guarantee
The technical contribution based on this work and
presented here is an alternative robust, compact,
simple, universal and low cost system that can replace
the original part assist maintenance services and
medical staff in sanitarian structures. The robustness
allows using this for different kinds of neonatal
incubators and probably for eggs incubators where we
are dealing with the same major parameters but to be
adjusted in other ranges. Temperature and moisture
are also the parameters considered while investigating
thermal comfort in buildings. And this is another
potential application.
A hardware of such a closed-loop system designed
around a Microchip 18F452 microcontroller [13] is
following. This integrated circuit contains in a single
40 pins (DIL or PLCC) package:
- 1 RISC (Reduced Instruction Set Computer) CPU
- 32 kilo bytes of flash memory; they will be used for
programs storage
- 256 bytes of EEPROM; they will retain some
parameters as set points)
- 1536 bytes for scratch RAM, stack…
- 34 I/O lines; some will be used for control, alarms,
displaying and communications purpose
- 4 timers for counting and time measurements. They
will be of great interest for the software cycling,
generation and handling of interruptions
- 2 PWM modules; one will be used for the control of
the temperature
- 8 channels, 10 bits A/D converter; they will be used
for parameters acquisition
- 1 watch dog to recover from program break down.
Such a single integrated circuit allows the
fulfilment of many conditions (simple, cheap,
versatile, compact and reliable).
After completing the clock system by adding an
external crystal and 2 capacitors, one has an in situ
programmable system working at 40 MHz. The in situ
programmable characteristic is very valuable for
future extensions. This ensures versatility and
universality.
We have drawn the block diagram of the hardware
below in figure 6.
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newborn human infant”, J. Pediatrics, vol 66, pp. 495-
508, 1965.
[3] M. F. Amorim, “Contribution à la Conception et au
Developpement d'un Nouvel Incubateur: Système de
Contrôle d'Humidité et Monitorage Cardio-respiratoire”,
PhD thesis, Université Technologie de Compiègne,
1994.
[4] D. Bouattoura, P. Villon and G. Farges, “Dynamic
programming approach for newborn's incubator
humidity control”, IEEE Trans. on Biomedical Eng.
45(1), pp. 48-55, 1998.
Figure 6. Hardware of the processing and closed-loop
control system.
For security and reliability issues, some parameters
are picked up redundantly by at least 2 sensors. This
will be the case for temperature and breathing.
The computer used for programs development and
downloading to the system is not represented in the
figure.
6. Conclusion
A simple alternative system for detection, digital
processing of an incubator parameters has been
presented. A mathematical model was developed for a
better insight of heat transfer phenomena. Before, we
had a stalemate where our proposal is a useful
solution. New aspects include universality, versatility
and the possibility to sense and process parameters
usually left to external powerful equipment linked to
incubator. The accuracy expected for targeted
parameters values will be higher, since computations
potential available let to robust fuzzy control which is
not used in this kind of system till to now.
For the future, an important question is whether a
neuro-fuzzy and fuzzy-GA based approaches are
useful. Our expectations should be satisfied. But, if
not, thanks to the in situ programming capability of
the system, the synergy provided by the combination
of fuzzy and neural systems can easily be introduced.
Another future development of this work can be
focused on the energy efficiency of the incubators
control.
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