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Precise Temperature Measurement for Increasing the Survival of Newborn Babies in Incubator Environments


Abstract and Figures

Precise temperature measurement is essential in a wide range of applications in the medical environment, however the regarding the problem of temperature measurement inside a simple incubator, neither a simple nor a low cost solution have been proposed yet. Given that standard temperature sensors don't satisfy the necessary expectations, the problem is not measuring temperature, but rather achieving the desired sensitivity. In response, this paper introduces a novel hardware design as well as the implementation that increases measurement sensitivity in defined temperature intervals at low cost.
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Sensors 2014, 14, 23563-23580; doi:10.3390/s141223563
ISSN 1424-8220
Precise Temperature Measurement for Increasing the Survival
of Newborn Babies in Incubator Environments
Robert Frischer 1, Marek Penhaker 1, Ondrej Krejcar 2,*, Marian Kacerovsky 3
and Ali Selamat 4
1 Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and
Computer Science, VSB-Technical University of Ostrava, 17. Listopadu 15, Ostrava Poruba 70833,
Czech Republic; E-Mails: (R.F.); (M.P.)
2 Center for Basic and Applied Research, Faculty of Informatics and Management,
University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
3 Biomedical Research Center, University Hospital Hradec Kralove, Hradec Kralove 50003,
Czech Republic; E-Mail:
4 Faculty of Computing, Universiti Teknologi Malaysia and UTM-RDA Center of Excellence,
UTM Johor Bahru, Johor 81310, Malaysia; E-Mail:
* Author to whom correspondence should be addressed; E-Mail:;
Tel.: +420-777-484-280.
External Editor: Vittorio M.N. Passaro
Received: 12 August 2014; in revised form: 22 October 2014 / Accepted: 1 December 2014 /
Published: 8 December 2014
Abstract: Precise temperature measurement is essential in a wide range of applications in
the medical environment, however the regarding the problem of temperature measurement
inside a simple incubator, neither a simple nor a low cost solution have been proposed yet.
Given that standard temperature sensors don’t satisfy the necessary expectations, the
problem is not measuring temperature, but rather achieving the desired sensitivity. In
response, this paper introduces a novel hardware design as well as the implementation that
increases measurement sensitivity in defined temperature intervals at low cost.
Keywords: temperature measurement; incubator; sensitivity increasing; A/D converter
Sensors 2014, 14 23564
1. Introduction
Considering the measurement of physical parameters is mostly applicable in industrial and medical
environments, a significant emphasis is placed on potential health risk for patients, particularly in the
medical field. Although measuring and testing in incubators for newborn babies are practical, these
applications lead to the problem with the need for a very precise temperature measurement inside the
incubator. If the measurement and following temperature control fail, a newborn baby could die in a
few minutes [1,2].
Preterm delivery (PTD) which is defined by the World Health Organization as delivery occurring
at less than 37 gestational weeks or before 259 days [3], is the primary cause of perinatal mortality and
associated with up to 75% of long-term perinatal morbidity, such as cerebral palsy, developmental delay,
retinopathy of prematurity, and other conditions. Despite progress in perinatal medicine, knowledge
about risk factors and mechanisms related to this pregnancy complication, PTD rates are still between
5% and 9% in Europe and in other developed countries, while in the USA, this rate was increased to
12.7% in 2005 [4–6].
Premature newborns need extra monitoring, treatment and care since they are more vulnerable than
the newborns delivered on the expected date and can have serious health problems. One of the most
common features of the care for premature newborns is the usage of incubators that provide stable,
appropriate temperature and humidity. This has substantial importance since heat production requires
oxygen consumption and usage of glucose. Moreover, persistent hypothermia may lead to metabolic
acidosis, hypoglycemia, decreased surfactant production, increased caloric requirements, and if
chronic, impaired weight gain.
In addition to premature newborns, incubators are also used for the following purposes: for newborns
who are too small for their gestational age as well as infants unable to maintain their own temperature with
clothing and wrapping, risk of abnormal heat loss, nutritional problems, large wounds or infection.
Given the problem of temperature measurement, although there are many sensors which can
measure temperature, each has advantages and disadvantages. These sensors transform temperature
into resistance or voltage, but measurement accuracy is always a main concern and objective. When
designing a device it is necessary to obtain accurate temperature readings and to select a proper sensor
type. Resistive-based sensors such as thermistors are relatively cheaper and temperature can be
retrieved simply. On the other hand, accuracy is thus lower and each sensor should be calibrated to
obtain significant data. Voltage-based sensors (thermocouples) are much more precise, nonetheless,
since output voltage is very low (a few mV), and amplification errors might be very high. Low offset,
precise operation amplifiers would be mandatory, as these types of sensors are preferred. In this paper a
standard LM35 resistance based sensor is chosen, yet the main disadvantage is the low sensitivity when
measuring human body temperatures. It certainly is possible to use precise operation amplifiers,
however it would be necessary to use an A/D converter with higher resolution. All these conditions
point to high overall complexity and high costs. Basically, the concern of this paper is to propose a
simple, low cost sensor for measuring the temperature of a newborn baby within reasonable accuracy. As
the standard approach to this issue cannot be used, we furthermore developed our novel solution based
on the precise measurement area with aspects of electrical engineering. Finally we describe the idea of
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the solution as well as the design, the hardware implementation and the results that are given at the end
of the article.
2. Problem Definition
As mentioned before, a solution to the problem of measuring a certain quantity in a short range with
reasonable accuracy is not achievable. For instance, when measuring the temperature range from 0 °C
to 200 °C with 10 b A/D, the maximum resolution would be at 0.2 °C assuming full coverage of signal
voltage. As such, the aspect ratio between temperature range and accuracy becomes 1000:1 which is a
fair value. If we use the same circuit to measure a temperature range from 35 °C to 45 °C the final
accuracy would be the same and aspect ratio between temperature range and accuracy will however be
only 50:1, which is actually a poor value so uncertainty is very high due to the utilization of a small
range, as can be seen in Figure 1. Ideally, it is necessary to cover all ranges of the A/D converter in the
temperature range that is required.
Figure 1. Human body temperature range vs. sensor measuring range.
3. Related Work
Several existing solutions are presented in the literature for the precise temperature measurement
problem studied in this paper. Chang et al. [7] proposed a method to measure the temperature precisely
by A/D and therefore adapted the circuit to higher demands. They also created a precise voltage
reference to use the thermal dependence of NMOSFET semiconductor devices. This step is mandatory,
since resistance differences in these types of devices is very small and sensitive to any discontinuities.
As the paper uses a standard OP amp interconnection to amplify temperature data the temperature
range is from 20 °C to 120 °C and accuracy is about 0.55 °C/LSB though this implementation is
inapplicable to human body temperature reading.
Additionally, Huang et al. [8] introduced a method of air temperature measurement by changing the
air features along with the temperature change. More precisely, this method focused on changes of the
sound speed in air dependent upon temperature adaptation. This method calculates the time between
Sensors 2014, 14 23566
outbound signal pulses and inbound responses which depends on temperature, if the distance between
the transmitter and receiver is constant. This unusual method is relatively precise, with an absolute
measurement error of about 0.2 °C and can be used for measuring the temperature in infant incubators.
Although the speed of sound in air is also dependent on relative humidity this paper, however didn’t
involve this dependence in the final formula. Therefore, the total error of the presented method will
obviously be slightly higher than the presented ±0.2 °C, due to humidity fluctuations over time.
Finally, Elsgaard and Jorgensen [9] made a special temperature-gradient incubator (TGI) with
highly precise temperature regulation. This article describes a modern approach to temperature
regulation using Peltier elements which seems very accurate at keeping the temperature in small
volumes at a constant value. Peltier modules are easy to drive, since they are mainly semiconductor
diodes connected in series—parallel manner. As the current passes through, the temperature gradient
appears on opposite sides. Additionally, when using Peltier cells the temperature error, can be as low
as 0.008 °C. An excess demand emerges on temperature sensors due to the temperature gradient in
which the sensors are placed. The authors achieved a measuring accuracy of 0.02 °C using a special,
highly accurate and expensive Tempmaster-100 thermometer and Pt-100 temperature sensors which
are highly precise and expensive resistance-based ones. Given these results, summarized the Table 1, it
is necessary to develop new solutions to meet all requirements since it is crucial to take the other
works compared one step further.
Table 1. Comparison of the existing method for precise temperature measuring.
Article/Method Temperature
Measurement Accuracy
Complexity Price Versatility
NMOS temp. dependence [7] 0.55 °C Moderate Moderate Low
Sound speed method [8] 0.2 °C Very High High Low
Pt-100 and high precision
thermometer [9] 0.02 °C High Very High Low
Our solution 0.2 °C or better Very Low Very Low High
4. Proposed Solution
This section introduces the development of a new device to increase the desired sensitivity while
measuring temperatures between the 25 °C and 42 °C in medical environments. There are plenty of
analog sensors in the market, however, their overall accuracy is also dependent on the input
A/D converter. Even though it is possible to buy an expensive, precise sensor on the market, the
measurement error would be relatively high if the A/D converter is only 8 b or 10 b. The sensitivity of
the sensor is calculated by the following Equation (1):
± =
2 (1)
where Uref is reference voltage level of A/D, usually about 5 V and n is resolution (length of single
converted data). If 10 b A/D is used, the sensitivity would be 4.88 mV which seems to be a sufficient
value, however, if the temperature is represented by a sensor having 10 mV/°C, then we can measure
temperature with an accuracy of around 0.5 °C, which is far from a satisfactory level. Another issue
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which needs to be considered is that human body temperature varies from 25 °C to 42 °C therefore there
is only a 17 °C difference to be measured. If the sensitivity is 0.5 °C, only 34 values would be obtained,
consequently, which corresponds to a 5 b A/D converter. This is an excessive wasting of time in the
measurement hardware that is used for measuring temperature and in order to avoid this problem, another
temperature sensor with better sensitivity and better A/D converter with 16 b or 24 b accuracy could be
used. When using a 24 b A/D converter, the 17 °C scale is covered by 570,425 values which is equivalent
to a 19 b A/D converter without any hardware modification. Given these facts it still seems sufficient, yet
the costs and complexity of the whole solution would be very high.
Considering the facts mentioned above, a hardware modification and adaptation are recommended.
The proposed solution is based on a novel hardware magnifier, which enables modifying the input
temperature signal and adapting it to the traditional A/D converter.
5. Hardware Implementation
One way of increasing sensitivity is to use a more complex A/D converter, however, the overall
complexity and price would not be as appropriate as a new solution. Moreover, a lower reference
voltage could be used for A/D, which would increase the sensitivity yet it would not be significantly
beneficial for this case. Furthermore, another way is to utilize a hardware “magnifier”. However the
desired temperature range is very low as demonstrated in Figure 1. In comparison to the whole scale
(5 V), the resulting voltage is only 170 mV however, and ideally, the reference voltage for A/D should
vary from 250 mV to 420 mV which is very hard to reach.
Even if given A/D converter provides a single solution that obviously wouldn’t be a sound approach,
therefore, changes have to be made from the sensor side. It is necessary to magnify a small voltage
difference to full scale range—0 V~5 V—as shown in Figure 2 and in order to achieve this, a hardware
magnifier was constructed which enables us to enlarge any voltage difference. Not only voltage level
relative to the ground level, but also the floating level which is important for our case, as the
measurement range is shifted or restricted.
Figure 2. Small signal is magnified to full converter’s scale to achieve maximum A/D resolution.
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Mainly, the device consists of three parts; differential op amp with level shifter, negative power
supply and output stage which is made of non-inverting operational amplifier with variable gain.
The input stage consists of op amp (LM358), resistors (R7, R9, R10, R13) and output filter
(R14 + C13) as can be seen in Figure 3. The gain of the input stage is set to 1, which is obvious since
all of the resistors are the same, therefore it is only crucial to follow the measured difference and
convert it to a simple signal relative to the ground signal. The shift of the reference voltage connected
to R10 results in increasing or decreasing voltage therefore, it is possible to set the voltage level
corresponding to a temperature of 25 °C as 0 V. The output voltage swings identically as the source,
but relative to the permanent ground which is not floating and the output filter is only complementary.
The temperature measurement process is a slowly changing system, which also means that no rapid
transients are expected. This filter removes spikes and disturbances which leak from the supply
voltage. Its values are not critical and depend on current conditions. The operational amplifier LM358,
which is a standard low cost amplifier for low frequency applications was selected for further analysis.
Due to the kernel of thermal systems, the temperature drift is usually not so dynamic.
Figure 3. Input signal stage with basic signal adjustments.
One main disadvantage of this op amp is its inability to deliver signals at a full range (0–5 V) or in
other words, it is not a Rail-To-Rail device. Even if it had been such, voltage levels near the ground or
supply voltage would be inaccessible. The supply voltage could be raised above 5 V to easily achieve
the maximum voltage level of 5 V. In order to touch the ground level, it is necessary to use a negative
power supply. Although one or two volts are enough under standard conditions however the negative
supply is not accessible. Zero voltage is very important, since it corresponds to 25 °C therefore, a
simple negative power supply was designed in Figure 4 which consists of IC1 A and IC1B, resistors
(R15, R18, R19, R20), capacitors (C1, C2, C11, C42, C43) and two diodes (D4 and D5).
Figure 4. Negative voltage power supply—charge pump.
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IC1 and the surrounding components make up a simple square generator of about 6 kHz while the
final frequency can be tuned by R15 and C11. The output of this block is a square voltage pattern
covering levels from 0 V to the approximate supply voltage. IC1B is only a power stage boosting
output current which also copies input voltage from negative inputs. Capacitors C42 and C43 are in a
parallel to double the capacity and to half the inner resistance (RESR). If the output of the IC1B is at a
high state, capacitors are charging themselves through D4 where if the output changes, one side of the
capacitors is connected to the ground (pin 7, IC1B), yet this side was previously charged positively.
The second capacitor’s terminal would be now under the ground level which is lowered by voltage at
which capacitors were charged. If a negative voltage level appears, capacitors C1 and C2 are charging
through D5. As a result of this process, a negative voltage level is obtained on these capacitors
therefore this voltage supplies other op amps and creates sufficient difference to represent 0 V levels at
their outputs.
Finally, the signal from the temperature sensor is shifted, however the overall voltage swing is
constant at 170 mV. This swing has to be raised at to value of 5 V so it needs a simple non-inverting
amplifier with gain of 29.41 which is calculated by:
17 (2)
Considering R17 is a trimmer device, which allows fine tuning of target gain, a 10-turn version is
recommended for this case. One more time, the output is computed with a filter to avoid undesired
transients and the input signal is amplified by gain value and delivered to the output. This principle
mentioned above is schematically represented in Figure 5.
Figure 5. Principle of the presented device.
The mechanism we propose is presented In Figure 5. It is used to increase sensitivity when
measuring small temperature differences. The original signal that responds to the temperature
difference of 17 °C (25 °C–42 °C) is obtained from the LM35 sensor device represented by a red
arrow. As seen on the Y axis, 17 °C corresponds to a 170 mV difference, since the LM35 has a
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sensitivity of 10 mV/°C. The absolute voltage level starts at 0.25 V and ends at 0.42 V yet there is an
insignificant difference to convert it to a digital signal, due to the huge conversion error as we
mentioned before. It corresponds to a 5 b A/D converter with a full scope coverage and if it is
necessary to use all the A/D input range, the input signal has to be shifted down to zero level which
corresponds to the violet arrow. That is done by the input stage components in Figure 3. The IC5A with
its surrounding resistor network acts as a differential amplifier with selectable reference voltage level
(VREF label on resistor R10). Resistor R14 and capacitor C13 act as low pass filters which are
optional and serve as filters of any high frequency noise. The output of this stage is a shifted input
signal that has the shifted value inverse proportional to the reference voltage level and if it is lowered,
the output signal is shifted upwards.
The output stage would only amplify the shifted signal, in order to achieve the full voltage scope
to the connected A/D converter represented by a green arrow. The IC5B device is connected as a
common non-inverting amplifier with variable gain (trimmer R17) and the output filter (R16 and C16)
has the same purpose as mentioned before. The main objective is to achieve full voltage scope from
0 V to 5 V yet this is very complicated, once a standard operational amplifier is used. It has a
“no response zone”, therefore it is not possible to reach both extremes—5 V and 0 V—due to the internal
components and physical laws. If the amplifier uses a standard complementary transistor, the N-P-N
junctions will not allow current to pass through them under 0.6 V and increase the approximate V
(Supply voltage) 0.6 V. This state is presented as a dotted line in Figure 5 as “no response zone”. To
avoid this problem, we need to use a negative supply voltage which will make the output transistors go
into a conduction state, then the output can be as low as 0 V. A similar issue occurs on the opposite
voltage level, therefore, one possibility is to increase the supply voltage at least 0.6 V over the 5 V. Higher
voltages are not the problem, since another stabilizer could easily be used. On the other hand, the negative
voltage issue has to be solved in another way. One solution was presented in Figure 4 before.
6. Comparing of Developed Temperature Sensor to Preceding Simulations
Prior to the realization of the temperature sensor, there is a requirement to perform electric
simulations. For this purpose, we used a “LT Spice IV” environment [10] since the first hardware
approach would certainly not cause a deadlock. The simulation was based on the following electric
scheme in Figure 6 that is developed for real tests and proposed as a functional prototype to be tested
for a long period of time inside the incubator.
The electrical scheme in Figure 6 can be divided to four subsections; the first is a differential
amplifier, which is the sum of Sig, Noise and Vref consisting of the resistor network (R1 to R4) and
the operational amplifier (U1). The second subsection basically consists of a low pass filter (R5 and
C1). The third subsection is a non-inverting amplifier (R6, R7, U2) which amplifies the input filtered
signal with a fixed gain (Au). Since we are in the low frequency range, the amplifier’s gain could be
considered as constant. In the case of higher frequency signals, low cost operational amplifiers have
higher signal distortion, higher signal phase shift and higher signal edge distortion. All these
drawbacks are directly match up with op amp parameters like:
Slew Rate (0.3 V/µs),
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Bandwidth (700 kHz while Au = 1),
Open Loop voltage gain and it’s frequency dependence.
Generally speaking, the higher values imply the better op amp, nevertheless bring this implies a
higher price, however the solution we propose is very cheap. The final price of the temperature sensor
is $6 with the PCB, of where the electronic parts cost only $3. The cost is pretty low because of the
characteristics of the target system [11–14]. As mentioned above, these temperature systems have very
long time constants, so the working frequencies are in order of units of hertz and therefore demands on
the quality of electronics parts can be lower. The fourth and final subsection of the electronic scheme
is an output low pass filter (R8 and C2) which has a higher damping factor than the first filter.
Figure 6. Electric scheme of the temperature sensor realization.
There is evidently no way to predict the behavior of a simulated scheme precisely, however in that
case the simulated output is very close to the real operational result due to the very low operational
system time. Since in general temperature systems comprise very slow processes, there are no rapid,
dynamic changes and signal output lies in the static area of operational amplifiers [15]. In Figure 7, the
changes in all important signals as they pass through the developed hardware are presented.
Figure 7. Temperature’s sensor transfer function.
The original signal from the LM35 is represented by a red color curve. For test purposes a parasitic
noise signal was embedded into the signal element. The red curve is not exactly smooth, since it is
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deformed by the added noise. The green curve is the shifted as well as filtered original signal as
described in Section 5. The final temperature signal is defined by blue color, it has the highest slope
and is also passed throughout a simple filter to remove any undesired frequencies. The applied filters
also affect the phase of the signal, yet the time shift is negligible.
The frequency response is presented in Figure 8, where the input signal (the green curve) is affected
by white noise. This simulation proves that the simple filters we used are sufficient and can be used in
future hardware designs. The output signal is relatively clean and anything over 100 Hz cannot seriously
affect the output signal. The filter design and component value remain the same as in the model. The
output signal from the temperature sensor was steady without any undesired spikes and the time delay
between the outputs of the LM35 and the hardware design are insignificant.
Figure 8. Frequency response of the used filters.
7. Testing of the Developed Solution
The device was tested with a real incubator at a faculty hospital. The target temperature range was
from 35 °C to 45 °C which matched the output voltage difference of 170 mV that started at a voltage
level of 350 mV. It means that the output voltage fluctuation was from 350 mV to 520 mV which can
be seen in Figure 5 as the red arrow. This signal is obtained directly from the temperature sensor and
then it leads to the first stage (IC5A on Figure 3). This stage shifts the sensor’s voltage down to the
zero level which could also be seen in Figure 5 as the shifted voltage represented by the violet arrow.
This stage is very important, since this voltage shift means that the zero voltage level presents the
minimum measurement temperature (35 °C). The output of this stage is filtered by the R-C network
(R14 and C13) and results in the averaging of the output signal which is very important since any
stochastic event is attenuated and the R-C network has to be properly set. The presented values are not
critical, because the human body temperature changes very slowly. Mathematically, this network can
be described by:
 =1
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where Vout-mean represents the mean value in the T time interval and vout(t) is the actual voltage before
processing in time t. Any input anomalies which differ from the mean voltage value are converted into
energy increments (decrements) at the filtration capacitor (C13), which could be approximated by:
∆~ =
2∙ (()−
 (4)
It can be easily assumed that any input voltage spike which emerges from time t1 until t2 will lead
to an energy increment in the capacitor. This network also proceeds to transient response on op amp.
The second stage, which corresponds IC5B in Figure 3, only amplifies the modified signal. The
working principle is represented in Figure 5 by the green arrow. Voltage gain Au is 29 which means
that voltage difference of 170 mV multiplied by 29 equals about 5 V. This voltage then covers the full
range of connected A/D and its full scope can be utilized. The final output voltage is also filtered by
the same type of R-C network to avoid any undesired output voltage spikes.
A number of real measurements was conducted and the basic testing of the device was simple.
While increasing system’s temperature, the output voltage on the LM35 and on the device’s output
were monitored. The LM35 voltage output should generate a voltage at a level that can be expressed
by multiplying the input temperature by 10 mV which is represented by the red curve in Figure 9.
Therefore the magnified voltage which is the blue curve in Figure 9 was moved down and the slope
was increased and thus the resulting sensitivity became higher. The original LM35 output can be
mathematically derived as y = 0.01x, while the modified, magnified device’s output can be described
as y = 0.0914x 1.8995. Given the expressions, it can be assumed that the voltage gain was set to the
value 9.14 and a 1.8995 V level shift was applied.
Figure 9. Calibration measurement with real system voltage outputs.
Additionally, the calibration measurement with a referential temperature sensor is presented in Table 2.
The purpose of this measurement is to verify whether the proposed device is working properly. The
temperature slowly rises from its minimum level of 21.5 °C until it reaches 35.3 °C. From Figure 9 it
could be seen that the output signal (blue curve) is moved down by 0.153 V and magnified with gain
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Au = 9.14±0.01. The original output from the LM35 temperature sensor is represented by the red curve.
From the graph it is evident that the device is working properly. The original voltage drift from the
LME sensor is 0.137 V while the drift from the device is 1.25 V.
The real temperature was measured on an accurate measuring device—an ALMEMO 2290-8. Data
from this device were considered as reference points and this particular referential device was used
since it has high accuracy of 0.05%. The output information can be used as a reference in this
particular measurement. The ALMEMO 2290-8 is a specialized data logger with thermocouple inputs
and with the ability to store 100,000 measurement datapoints.
Table 2. Measured data from the device’s output, LM35 output and referential device.
Real Tmp LM35 Tmp A/D Device Output LM35 Output
21.5 °C 21.5 °C 50 0.061035 V 0.214600 V
22.3 °C 22.3 °C 109 0.133057 V 0.222500 V
22.4 °C 22.3 °C 116 0.141602 V 0.223400 V
22.6 °C 22.6 °C 132 0.161133 V 0.225500 V
22.9 °C 22.8 °C 151 0.184326 V 0.228100 V
23.0 °C 23.0 °C 162 0.197754 V 0.229500 V
23.2 °C 23.2 °C 179 0.218506 V 0.231800 V
23.5 °C 23.5 °C 204 0.249023 V 0.235200 V
23.7 °C 23.7 °C 215 0.262451 V 0.236600 V
23.9 °C 23.8 °C 226 0.275879 V 0.238100 V
23.9 °C 23.9 °C 233 0.284424 V 0.239000 V
24.3 °C 24.4 °C 270 0.329590 V 0.244000 V
25.0 °C 25.1 °C 320 0.390625 V 0.250700 V
25.3 °C 25.3 °C 340 0.415039 V 0.253300 V
25.5 °C 25.5 °C 350 0.427246 V 0.254700 V
26.8 °C 26.9 °C 456 0.556641 V 0.268800 V
28.0 °C 28.2 °C 556 0.678711 V 0.282200 V
28.7 °C 29.0 °C 612 0.747070 V 0.289700 V
29.5 °C 29.7 °C 670 0.817871 V 0.297400 V
31.7 °C 31.5 °C 799 0.975342 V 0.314700 V
32.2 °C 32.2 °C 855 1.043701 V 0.322100 V
32.9 °C 32.9 °C 908 1.108398 V 0.329200 V
33.6 °C 33.5 °C 949 1.158447 V 0.334700 V
34.1 °C 34.1 °C 993 1.212158 V 0.340600 V
34.6 °C 34.6 °C 1032 1.259766 V 0.345800 V
35.3 °C 35.2 °C 1078 1.315918 V 0.351900 V
8. Measuring Temperature Profiles in the Incubator
One of the main issues of this project was the comparison of the developed solution with the
original one. Control measurements inside the incubator [16] were performed at the University
Hospital Ostrava and the measuring device was placed inside an incubator instead of a baby. Our first
temperature sensor was located in the same place as a single integrated temperature sensor of the original
incubator which can be seen in Figure 10. Additionally, the measured data are shown in Table 3 below.
Table 4 shows the differences in measured temperatures; the first column shows testing
temperatures vs. original. The second column shows the calculated temperature differences between
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the original (set) temperatures and those measured at the top of incubator—the temperature at position 1.
The third column shows the temperature difference measured at the top (positon 1) and bottom
(position 2—instead of a baby). During the testing process of the developed solution inside the
incubator, our results showed that the temperature set on the incubator is sensed only at the top of
incubator, where the deviation of the output from our developed sensor is 0.1 to 1.4 °C which is not a
very high difference. Next case however is more astonishing given that the newborn baby bed
temperature is significantly lower. The difference of the actual temperature varied in the range from
2.0 to 5.3 °C in Table 3, particularly in the range of temperature values from 36 to 37 ° C, which are
the most frequently used temperatures. In addition the temperature difference was almost 4 °C, which
is a significant difference for the comfort of a newborn baby.
Figure 10. Testing environment inside a real Dräger 8000SC incubator [16]. The
developed temperature sensor is mounted on the top of incubator in the same position as
the original one (indicated as number 1). There is also a second place 2 where the
developed sensor is placed mounted on the same place where a newborn baby is placed
when incubator is in use.
Table 3. Measured temperatures inside the incubator.
Temperature Setting on the
Incubator [°C] Temperature 2 [°C] Temperature 1 [°C]
32 29.55 32.12
33 31.47 33.50
34 32.21 34.31
35 32.92 36.14
36 33.47 37.07
37 34.06 38.02
38 34.58 39.33
39 35.19 40.44
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Table 4. Difference in measured temperatures inside the incubator.
Temperature in Incubator [°C] Δ (torig t1) [°C] Δ (t1 t2) [°C]
32 0.12 2.57
33 0.5 2.03
34 0.31 2.1
35 1.14 3.22
36 1.07 3.6
37 1.02 3.96
38 1.33 4.75
39 1.44 5.25
8.1. Testing of the Temperature Sensor Response Time
We additionally focused on another test concerning the response time of the new temperature
sensor. The sensor was mounted in the package in which it will be used and in a box which routed to
the cable and it was gradually heated by a hot-air blower from 24 °C to 40 °C and then cooled
spontaneously to room temperature. The time between changes of both temperatures were recorded in
1 °C steps, which can be seen in Tables 5 and 6.
From the results it is noticeable that the new sensor displays a very fast response time when the
temperature changes, especially when it is increasing. It gives the value in the desired range after no
more than 1 min, however spontaneous cooling times were significantly longer for both sensors. The
internal sensor is slightly handicapped by being enclosed in boxes, nonetheless it has ability to measure
temperature changes in relatively short periods of time such as 10 s.
Table 5. Time delay of the sensors when the temperature is increasing.
Temperature [°C] Time of Original Sensor [mm:ss] Time of New Sensor [mm:ss]
24 0:00 0:00
25 1:04 0:04
26 1:45 0:07
27 2:20 0:15
28 2:30 0:16
29 2:38 0:17
30 2:51 0:18
31 3:08 0:19
32 3:28 0:20
33 3:49 0:21
34 4:15 0:23
35 4:45 0:25
36 5:16 0:26
37 5:50 0:29
38 6:33 0:32
39 7:16 0:37
40 7:52 0:41
Sensors 2014, 14 23577
Table 6. Time delay of the sensors when the temperature is decreasing.
Temperature [°C] Time of Original Sensor [mm:ss] Time of New Sensor [mm:ss]
40 0:00 0:00
39 1:57 0:16
38 3:37 0:20
37 4:47 0:34
36 5:30 0:47
35 6:35 1:03
34 7:26 1:19
33 9:15 1:38
32 10:20 2:03
31 11:36 2:31
30 13:17 3:05
29 15:50 3:45
28 18:14 4:31
27 19:50 5:30
26 22:10 7:09
25 27:28 10:08
These tables can serve as a tool for estimating the time required for analyzing the actual
functionality of the incubators. Generally a time of 10 min for increasing temperatures and 30 min for
decreasing temperatures can be recommended to get a required temperature to be set correctly.
9. Discussion
Initially, the introduced method was developed to be used in neonatal incubator devices. In these
devices it is very important to measure temperature as precise as possible since the life of the babies
depends on it. The mentioned procedure use a hardware magnifier which is able to increase the final
temperature resolution and utilizes the full range of the connected A/D converter. Other methods
mentioned in the related work section are either too complex, expensive or rigid to be implemented as
default accessories. The achieved accuracy is sufficient to be a primary data acquisition source when
controlling target temperatures in an incubator.
The proposed electronic circuit has very good linearity, since no inductor or capacitive parts are
used in the feedback loop. Therefore, the only nonlinearity that emerged is the thermal float of the
resistors’ resistance, which can be considered as zero for the temperature range and in the fluctuation
used in a medical environment. This is only an instrument in the subsequent control loop, which is
responsible for the precise temperature reading. Other techniques mostly describe whole thermal
control solution, which may not be useful in all conditions.
From the obtained measurement results, the final op amp gain was set to cover the whole A/D input
range which is the first of the two necessary setups and the second setup is concerned with the voltage
shift which responds to particular sensor characteristics. The versatility of the design is the key feature
since with minimum changes, it can be used to increase the measurement sensitivity of relative air
humidity, to optimize output swinge when measuring air flow in the incubator, when increasing the
dynamic range of a light sensor device and so on.
Sensors 2014, 14 23578
10. Conclusions
This paper basically focuses on the problems of temperature sensors when they have to be
connected to an A/D converter. Almost in all the cases the output of the sensor is in a “default” state
without any signal adjustments. This leads to decreasing sensitivity of the sensors and consequently to
wasting A/D capabilities. One of the typical problems is the measurement of temperatures in prenatal
incubators. There is a small range of temperatures, which has to be kept at a stable level according to
the needs of the patient. If it is necessary to measure a narrow range of temperatures with a sensor
which has a bigger temperature range, the total temperature measurement accuracy will drastically
drop, due to small voltage drifts of the sensor’s output.
The proposed matching network will ensure that small temperature/voltage drifts will be properly
matched to the connected A/D and the final accuracy reading will be as high as possible. The presented
matching network can be considered as a hardware voltage magnifier/shifter, therefore we were able to
use the full output voltage swing without any limitations, such as dead zones on op amps border voltage
levels. The developed electronic circuit is easily integrated as a default system into the temperature
control loop in the hospital environment, especially in prenatal incubator control. This could help
stabilize the incubator’s inner temperature more precisely and maximize its effects on the patient.
The work and the contribution were partially supported by the projects: (1) “Biomedical engineer
systems X”, VSB Technical University of Ostrava under the project number SP2014/194; (2) This
article has been elaborated in the framework of the project “Support research and development in the
Moravian-Silesian Region 2013 DT 1—International research teams”, (RRC/05/2013). Financed from
the budget of the Moravian-Silesian Region; (3) “Smart Solutions in Ubiquitous Computing Network
Environments”, Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics
and Management; (4) research grant 01G72 of the University Teknologi Malajsia (UTM) and Ministry
of Science, Technology & Innovations Malaysia; (5) University Hospital Hradec Kralove - Long term
development plan. We also acknowledge support from ENWOX TECHNOLOGIES s.r.o. and from
student Karolina Feberova in testing of developed solution. Last but not least, we acknowledge the
technical language assistance provided by Stanislava Horakova (University of Warwick).
Author Contributions
M.P. and R.F. conceived and designed the experiments; M.P. performed the experiments; R.F.,
O.K., A.S. and M.K. analyzed the data; M.P. and A.S. contributed with analysis tools; R.F., M.P., O.K.
and M.K. wrote the paper.
Conflicts of Interest
The authors declare no conflicts of interest.
Sensors 2014, 14 23579
1. UNICEF Statistics on Infant Mortality Rates. Available online:
child-mortality/under-five (accessed on 20 October 2014).
2. De Araújo Júnior, J.M.; de Menezes Júnior, J.M.P.; de Albuquerque, A.A.M.; da Mota Almeida, O.;
de Araújo, U.F.M. Assessment and Certification of Neonatal Incubator Sensors through an
Inferential Neural Network. Sensors 2013, 13, 15613–15632.
3. Word Health Organisation. The Prevention of Perinatal Mortality and Morbidity; WHO Technical
Report Series, Report 470; WHO: Geneva, Switzerland, 1970.
4. Huddy, C.L.; Johnson, A.; Hope, P.L. Educational and behavioural problems in babies of
32–35 weeks gestation. Arch. Dis. Child Fetal Neonatal Ed. 2001, 85, F23–F28.
5. Wang, M.L.; Dorer, D.J.; Fleming, M.P.; Catlin, E.A. Clinical outcomes of near-term infants.
Pediatrics 2004, 114, 372–376.
6. Goldenberg, R.L.; Culhane, J.F.; Iams, J.D.; Romero, R. Epidemiology and causes of preterm
birth. Lancet 2008, 371, 75–84.
7. Chang, M.H.; Huang, Y.J.; Huang, H.P. Chip Implementation with a Combined Wireless
Temperature. J. Sens. 2011, 11, 10308–10325.
8. Huang, C.F.; Huang, K.N.; Young, M.S.; Huang, S.S.; Chen, R.C.; Lee, K.Y. Design of an
ultrasonic system for temperature measurement in an infant incubator. In Proceedings of the IEEE
EMBS Asian-Pacific Conference on Biomedical Engineering, Kyoto, Japan, 20–22 October 2003;
pp. 296–297.
9. Elsgaard, L.; Jorgensen, L.W. A sandwich-designed temperature-gradient incubator for studies of
microbial temperature responses. J. Microbiol. Methods 2002, 49, 19–29.
10. LT Spice IV Software. Available online: (accessed
on 20 October 2014).
11. Krejcar, O.; Frischer, R. Non Destructive Defects Detection by Performance Spectral Density
Analysis. Sensors 2011, 11, 2334–2346.
12. Krejcar, O.; Frischer, R. Real Time Voltage and Current Phase Shift Analyzer for Power Saving
Applications. Sensors 2012, 12, 11391–11405.
13. Krejcar, O. Mahdal, M. Optimized Solar Energy Power Supply for Remote Wireless Sensors
Based on IEEE 802.15.4 Standard. Int. J. Photoenergy 2012, doi:10.1155/2012/305102.
14. Krejcar, O.; Frischer, R. Smart Intelligent Control of Current Source for High Power LED Diodes.
Microelectron. J. 2013, 44, 307–314.
15. Single Supply Dual, Operational Amplifiers, LM358-D, ON Semiconductor: Denver, CO, USA,
17 October 2013. Available online:
(accessed on 20 October 2014).
Sensors 2014, 14 23580
16. Dräger. Incubator User Manual; Dräger Medizintechnik GmbH: Lubeck, Germany, 1998; p. 56.
English. Available online:
infant_incubators/user_manuals/Drager_Incubator_8000SC_-_User_manual.pdf (accessed on 20
October 2014).
© 2014 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
... Currently, conventional sensors, such as thermocouples, semiconductor sensors, thermistors etc., are used for temperature measurements in the incubator environment, similar to other medical devices [3,[16][17][18][19][20][21]. These sensors must be accurate and fast enough to reduce control errors. ...
... Although these temperature probes are generally firmly attached with an adhesive insulator to the skin of the sick baby, they may come loose. In this case, not only temperature may not be controlled accurately [3,[18][19][20], but also the skin of the baby may injure in removing this tape [3,16]. Placing these sensors too far away from the infant may cause a temperature offset between the measured and the calibrated temperature. ...
... The control panel is used to activate the air temperature mode. In this case, air temperature is measured by the temperature measurement circuit which employs a LM335 IC semiconductor temperature sensor [7,9,19,38,59]. ...
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... The change in resistance Measured by temperature sensor shows the linear relationship with increase in temperature per degree Fahrenheit. With the temperature sensitivity of 0.2 / Fahrenheit which is comparatively better than commercially available sensors [29][30][31]. The temperature and the conductance sensors were moved and placed on forehead, ankle, chest and the values were measured it was observed that the measurement was not much varied as the sensitivity of the temperature and conductance range is proved to be better. ...
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Every year, an increasingly large number of neonatal deaths occur in India. Premature birth and asphyxia are being two of the leading causes of these neonatal deaths. A well-regulated thermal environment is critical for neonatal survival. In the current scenario, it is impossible for the health centers in the rural areas of India to afford a neonatal incubator for every newborn due to its price and transportability. The successful delivery of neonates is hampered in India due to its increasing population along with limited technology and resources. Thus, a prototype of an incubator has been designed that is affordable, transportable, and energy saving for the health centers in the rural regions, with an AI-based decision support system.
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A temperature-gradient incubator (TGI) is described, which produces a thermal gradient over 34 aluminium modules (15x30x5 cm) intersected by 2-mm layers of partly insulating graphite foil (SigraFlex Universal). The new, sandwich-designed TGI has 30 rows of six replicate sample wells for incubation of 28-ml test tubes. An electric plate heats one end of the TGI, and the other end is cooled by thermoelectric Peltier elements in combination with a liquid cooling system. The TGI is equipped with 24 calibrated Pt-100 temperature sensors and insulated by polyurethane plates. A PC-operated SCADA (Supervisory Control And Data Acquisition) software (Genesis 4.20) is applied for temperature control using three advanced control loops. The precision of the TGI temperature measurements was better than +/-0.12 degrees C, and for a 0-40 degrees C gradient, the temperature at the six replicate sample wells varied less than +/-0.04 degrees C. Temperatures measured in incubated water samples closely matched the TGI temperatures, which showed a linear relationship to the sample row number. During operation for 8 days with a gradient of 0-40 degrees C, the temperature at the cold end was stable within +/-0.02 degrees C, while the temperatures at the middle and the warm end were stable within +/-0.08 degrees C (n=2370). Using the new TGI, it was shown that the fine-scale (1 degrees C) temperature dependence of S(o) oxidation rates in agricultural soil (0-29 degrees C) could be described by the Arrhenius relationship. The apparent activation energy (E(a)) for S(o) oxidation was 79 kJ mol(-1), which corresponded to a temperature coefficient (Q(10)) of 3.1. These data demonstrated that oxidation of S(o) in soil is strongly temperature-dependent. In conclusion, the new TGI allowed a detailed study of microbial temperature responses as it produced a precise, stable, and certifiable temperature gradient by the new and combined use of sandwich-design, thermoelectric cooling, and advanced control loops. The sandwich-design alone reduced the disadvantageous thermal gradient over individual sample wells by 56%.
To test the hypothesis that near-term infants have more medical problems after birth than full-term infants and that hospital stays might be prolonged and costs increased. Electronic medical record database sorting was conducted of 7474 neonatal records and subset analyses of near-term (n = 120) and full-term (n = 125) neonatal records. Cost information was accessed. Length of hospital stay, Apgar scores, clinical diagnoses (temperature instability, jaundice, hypoglycemia, suspicion of sepsis, apnea and bradycardia, respiratory distress), treatment with an intravenous infusion, delay in discharge to home, and hospital costs were assessed. Data from 90 near-term and 95 full-term infants were analyzed. Median length of stay was similar for near-term and full-term infants, but wide variations in hospital stay were documented for near-term infants after both vaginal and cesarean deliveries. Near-term and full-term infants had comparable 1- and 5-minute Apgar scores. Nearly all clinical outcomes analyzed differed significantly between near-term and full-term neonates: temperature instability, hypoglycemia, respiratory distress, and jaundice. Near-term infants were evaluated for possible sepsis more frequently than full-term infants (36.7% vs 12.6%; odds ratio: 3.97) and more often received intravenous infusions. Cost analysis revealed a relative increase in total costs for near-term infants of 2.93 (mean) and 1.39 (median), resulting in a cost difference of 2630 dollars (mean) and 429 dollars (median) per near-term infant. Near-term infants had significantly more medical problems and increased hospital costs compared with contemporaneous full-term infants. Near-term infants may represent an unrecognized at-risk neonatal population.
This paper is the first in a three-part series on preterm birth, which is the leading cause of perinatal morbidity and mortality in developed countries. Infants are born preterm at less than 37 weeks' gestational age after: (1) spontaneous labour with intact membranes, (2) preterm premature rupture of the membranes (PPROM), and (3) labour induction or caesarean delivery for maternal or fetal indications. The frequency of preterm births is about 12-13% in the USA and 5-9% in many other developed countries; however, the rate of preterm birth has increased in many locations, predominantly because of increasing indicated preterm births and preterm delivery of artificially conceived multiple pregnancies. Common reasons for indicated preterm births include pre-eclampsia or eclampsia, and intrauterine growth restriction. Births that follow spontaneous preterm labour and PPROM-together called spontaneous preterm births-are regarded as a syndrome resulting from multiple causes, including infection or inflammation, vascular disease, and uterine overdistension. Risk factors for spontaneous preterm births include a previous preterm birth, black race, periodontal disease, and low maternal body-mass index. A short cervical length and a raised cervical-vaginal fetal fibronectin concentration are the strongest predictors of spontaneous preterm birth.