Published at : 28 Jun 2023
Volume : IJtech
Vol 14, No 4 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i4.5678
Bambang Guruh Irianto | Departement of Electromedical Enginering, Politeknik Kesehatan Kemenkes Surabaya, Jalan Pucang Jajar Tengah No.56, Surabaya 60282, Indonesia |
Anita Miftahul Maghfiroh | Departement of Electromedical Enginering, Politeknik Kesehatan Kemenkes Surabaya, Jalan Pucang Jajar Tengah No.56, Surabaya 60282, Indonesia |
Moh Sofie | STIKES Semarang, Jalan kolonel warsito sugiarto km 2,5 Sadeng GunungPati, Semarang 50222, Indonesia |
Abd Kholiq | Departement of Electromedical Enginering, Politeknik Kesehatan Kemenkes Surabaya, Jalan Pucang Jajar Tengah No.56, Surabaya 60282, Indonesia |
Factors causing premature infant mortality include
the lack of simple care and inadequate equipment such as a baby incubator.
Premature babies are very susceptible to heart disorders, including congenital
heart defects. Congenital heart defects can cause a fetus to be born
prematurely. The current research related to this matter was further conducted,
aiming to develop a baby incubator with an overshoot reduction system
specifically for babies with heart defects that can be monitored remotely using
an IoT system. In this study, the AHT 10 sensor was used for room temperature
sensing in the baby incubator. Temperature control was achieved using a
closed-loop PID system. In this case, the monitoring of the baby's heart rate
employed leads II to tap the heart's electrical signal. Data transmission
consisted of temperature readings, ECG signals, and heart rate. The microdata
was processed into digital data, which were then sent via the Raspberry Pi,
then sent via the internet to access the cloud firebase. After that, the
firebase data were downloaded from an Android system. The performance results
showed that in the temperature test, the error value was below 5%, and the PID
control made can reduce the overshoot temperature by no more than 5%. In addition,
it was also determined that the steady-state error value was 2%. T-Test
statistical test on the ECG signal further obtained a p-value > 0.05.
Furthermore, the data transmission test using IoT did not find data loss when
sending the data, and the minimum speed required for data transmission was 5
kbps. This research further implied that the user or the patient's family could
easily monitor the baby's development anywhere and anytime.
Baby incubator; ECG signal; Heart rate; IoT; Temperature
2.1. System Design and System Control
Figure 1 The design of baby incubator. The input ECG
signal is amplified by an instrument amplifier circuit so that it can be read
by the microcontroller, the instrument output is filtered with HPF and LPF
filters with a bandwidth frequency of 0.05 Hz-100Hz. The signal output will be
displayed on the LCD display and sent via android
The
block diagram of the PID control is described in Figure 2. In this case,
monitoring the baby's heart rate used lead II as the heart's electrical signal
tapping (Utomo, Nuryani, and Darmanto, 2017). The electrode output
from lead II was amplified with an instrument amplifier with a gain of 100
times to eliminate noise interference or other artifacts. A filter was made
according to the heart signal frequency, namely 0.05Hz-100Hz. The
microcontroller output in the form of an electrocardiogram signal and the room
temperature of the baby incubator sent via Resbery Pi from Resbery Pi will be
received by the web server using a firebase. Furthermore, the data were sent to
the Android phone. In this case, the Android display used the MIT App
application, which must be installed first on a cellphone with an android
system.
Figure 2 The
design of PID control, setting the temperature as an input to
the microcontroller then a closed loop system is used in this method to control
the room temperature on the baby incubator by using the AHT10 sensor to sense
the room
The PID control is based on Equation 1 and Equation
2 (Ang, Chong, and Li, 2005):
Where CD(s)
is the response to disturbance, D(s) is the interference effect test.
Figure
4 Temperature test using the Incu Analyzer type INCU 2 Brand Fluke, with
five temperature parameters namely T1, T2, T3, T4 and T5 which are placed as
shown in the picture
Figure 5 Electrocardiograph
signal simulation with Phantom ECG using lead II displayed on the oscilloscope
2.4. Data
Transfer
In
this study, data transmission consisted of temperature readings, ECG signals,
and heart rate. The microdata was processed into digital data, which were then
sent via resbery pi. Resbery data were sent via the internet to access the
cloud firebase, then from the firebase data was downloaded from the android
system as described in Figure 6. In order to test the IoT system, the
researcher set the speed of bps WiFi to determine the speed of data transmitted
to android, with each speed measured 6 times.
In
order to find out the performance of the baby incubator according to the
standard, a calibration test was carried out. The parameters tested were the
setting temperature of 35oC, 36oC, and 37oC
using the INCU Analyzer, as described in Figure 4. Measurements were carried
out 6 times, and then the average calculation was carried out, which later
found the error from the temperature deviation of the baby incubator. The
average calculation was obtained based on Equation 11 (Duvernoy, 2015).
Where x is the data retrieval to, and n is
the number of data retrieval. While the temperature reading error value was
based on Equation 12.
Error
= Mean – standard tool reading (12)
3.1. Temperature
Testing and PID Control
The results of the comparison of
temperature readings on the baby incubator with the INCU analyzer are described
in Figure 7. Based on the description
in Figure 7, it is explained that the average error of reading the temperature
of the baby incubator was compared with the INCU analyzer at temperature
settings of 35oC, 36oC, and 37oC with 5
measurement sensor points with the maximum error value on the reading of the
temperature point T2 which is 36oC ± 0.7oC, namely T1,
T2, T3, T4 and T5 which are described in Figure 4 that is still within the
threshold for use, namely the error value is still below 5% (Widhiada et al., 2019b).
Meanwhile, the PID control overshoot
reduction test with disturbance was carried out at a temperature setting of 37oC,
described in Figure 8.
Where HR is the heart
rate (BPM), while tn+1 is the period time for the peak of R-peak (n+1), and tn
is the period time for the peak of R tn. The results of the ECG heart rate test
(BPM) with Phantom ECG are described in Table 1.
BPM |
MEAN (%) |
SD (%) |
30 |
30.16 |
0.37 |
60 |
60 |
0 |
120 |
120 |
0 |
180 |
180.16 |
0.40 |
240 |
239.66 |
1.36 |
Based on the calculation of the standard deviation in Table 1, the mean value and standard deviation of each BPM setting were obtained. At the BPM 30 setting, the standard deviation value was 0.3727%, while at BPM 60 and 120 settings, the standard deviation was 0%. Furthermore, the BPM 180 setting obtained a standard deviation of 0.4082%, while the BPM 240 setting obtained a standard deviation of 1.3662%. From the average values that have been explained, it is known that the highest average error value is produced by the BPM 240 value with an average value of 239.66 BPM ± 1.36 BPM. It can be concluded that the higher the BPM value, the higher the error value obtained. However, the value generated in this assessment is that this tool is suitable for use according to medical standards [32]. Meanwhile, to find out whether there is a significant difference in the heart rate (BPM) ECG calculation with the BPM setting on the Phantom ECG, a statistical test was carried out using a T-Test with a p-Value > 0.05 indicator as described in Table 2.
Table 2 Testing the difference using the T-Test statistic
Alpha |
0.05 | ||||
std err |
t-stat |
df |
p-value |
t-crit | |
One Tail |
54.73431 |
0.000761 |
6 |
0.499708708 |
1.94318 |
Two Tail |
54.73431 |
0.000761 |
6 |
0.999417415 |
2.446912 |
The results of the
T-Test statistical test in Table 2 showed that there was a one-tail T-Test
result with p-Value = 0.499708708, while in two tails, the p-value was
0.999417415, which means that there was no significant difference because the
p-Value > 0.05 and the error rate was still within the appropriate level for
medical purposes.
3.3. Data Transfer Test
Data transfer testing was carried
out to test whether the sending data was missing data or there was a delay at
the time of delivery. This system uses an IoT system by utilizing an API base
to store data which later be downloaded via android. Therefore, this system was
not affected by the distance that can be accessed from the feed and anywhere as
long as the cellphone was in a WiFi network connection. In this test, the bps
speed setting was conducted to find out the right bps speed for sending data
via android based on IoT. The data transfer test is described in Figure 9.
BPS Speed (kbps) |
Mean Temperature Reading (°C) |
Mean Heartbeat Reading (BPM) |
Transmission Data Delay (Second) |
5 |
36.2 |
80 |
3.65 |
10 |
36.2 |
80 |
3.53 |
20 |
36.2 |
80 |
3.79 |
30 |
36.2 |
80 |
3.65 |
40 |
36.2 |
80 |
3.74 |
50 |
36.2 |
80 |
3.71 |
The
research aims to reduce overshoot in baby incubators using the PID method,
specifically for babies with heart defects, which can be monitored remotely
using the IoT system. After investigating the performance of the PID method for
an infant incubator temperature control system, it was found that the
steady-state error value was 2%. While the results of measurements using the
INCU Analyzer obtained the highest overshoot value at T4 with temperatures
reaching 380C ± 1.30C. It can be concluded that the performance
of the PID control system is very good for reducing temperature overshoot in
baby incubators and is suitable for medical purposes. Then testing was also
carried out on the ECG for heart rate (BPM) on the ECG signal using the T-Test
statistical test, and the results obtained were p-Value > 0.05. From this
value, it shows that this system is feasible to be used for medical purposes
because there is no significant difference in value. Data transmission using
IoT is also tested by changing several bps speeds with a speed setting of 5
kbps – 50 kbps. The results obtained are that there is no data loss when
sending data, and the minimum speed required for data transmission is 5 kbps.
However, there is a delay with an average time of 3.67 seconds in the data
transmission process. From the overall results of measurements and tests
carried out, it can be concluded that the method used is suitable for use
according to medical standards. For further research development, researchers
want to develop central monitoring of baby incubators so that nurses or users
can easily monitor several baby incubators in the NICU room.
Appreciation
is given to the Department of Electromedical Engineering and Research
Management & Innovation Centre, Poltekkes Kemenkes Surabaya, for supporting
this research work.
Abdurrakhman,
A., Soehartanto, T., Hadi, H.S., Toriki, M.B., Widjiantoro, B.L., Sampurno, B.,
2020. Design of Output Power Control Systems Based on Mass Flow Rate Comparison
of Air-Fuel Ratio (AFR) on Dual Fuel Generator Set by Using the PID Control
Method. International Journal of Technology, Volume 11(3), pp. 574–586
Ang, K.H., Chong, G., Li, Y., 2005. PID Control System
Analysis, Design, and Technology. IEEE Transactions on Control Systems
Technology, Volume 13(4), pp. 559–576
Agresara,
M.N., Vyas, D.D., Bhensdadiya, B.S., 2017. System for
Remote Monitoring and Control of Baby Incubator and Warmer. International
Journal of Futuristic Trends in Engineering and Technology, Volume 3(6),
pp.1–6
Ashish, B. 2017. Temperature Monitored IoT Based Smart Incubator. Proceedings
of the International Conference on IoT in Social, Mobile, Analytics and Cloud,
I-SMAC 2017, pp. 497–501
Duvernoy, J., 2015. World Meteorological
Organization (WMO). Guidance on the Computation of Calibration Uncertainties
Fadilla, R., Idhil, A.N.I.I., Anggraini, M.A.P., Dewi, A.K.,
Sanjaya, M.R., Nurrohman, M.Y., 2020. A Multifunction Infant Incubator
Monitoring System with Phototherapy and ESP-32 Based Mechanical Swing. International
Journal of Science, Technology & Management, Volume 1(4), pp. 371–381
Hubsher, J.A., 1961. The Electrocardiogram of the Premature Infant.
American Heart Journal 61(4), pp. 467–475
Janney, B.J., Krishnakumar, S., Anushree,
P.B., Rayshma, V., Suresh, S., 2018. Deisgn of Mobile Infant Incubator with
Comforting Pillow. International Journal of Engineering and Technology(UAE),
Volume 7(2), pp. 6–9
Kanalikova, K., 1990. Diagnosis of Congenital Heart Defects in
Childhood. Bratislavské lekárske listy, Volume 91(12), pp. 868–873
Kirana, V.C., Andayani, D.H., Pudji, A., 2021.
“Effect of Closed and Opened the Door to Temperature on PID-Based Baby
Incubator with Kangaroo Mode. Indonesian Journal of Electronics,
Electromedical Engineering, And Medical Informatics, Volume 3(3), pp. 121–127
Kohler,
B.U., Hennig, C., Orglmeister, R., 2002. The Principles of Software QRS
Detection. IEEE Engineering in Medicine and biology Magazine, Volume
21, pp. 42–57
Koli, M., Ladge, P., Prasad, B., Boria, R., Balur, N.J., 2018.
Intelligent Baby Incubator. Proceedings of the 2nd International Conference
on Electronics, Communication and Aerospace Technology, ICECA 2018, pp.
1036–1042
Kvalvik, L.G., Wilcox, A.J., Skjærven, R., Ostbye, T., Harmon,
Q.E., 2020. Term Complications and Subsequent Risk of Preterm Birth: Registry Based
Study. The BMJ, Volume 369
Lawn, J.E., Davidge, R., Paul, V., Von Xylander, S., De Graft
Johnson, J., Costello, A., Kinney, M., Segre, J., Molyneux, E., 2013.
Preterm Baby Survival and Care Round the World Born Too Soon: Care for the
Preterm Baby. Reproductive Health, Volume 10(10), p. 5
Luthfiyah, S., Kristya, F., Wisana, I.D.G.H., Thaseen, M., 2021. Baby Incubator
Monitoring Center for Temperature and Humidity Using WiFi Network. Journal
of Electronics, Electromedical Engineering, and Medical Informatics, Volume
3(1), pp. 8–13
Maghfiroh, A.M., Amrinsani, F., Setiawan,
S.Y., Firmansyah, R.M., Misra, S., 2022. Infant Warmer with Digital Scales for
Auto Adjustment PID Control Parameters. Jurnal Teknokes, Volume 15(2),
pp. 117–123
Maghfiroh, A.M., Arifin, A., Sardjono, T.A., 2019. Wavelet-Based
Respiratory Rate Estimation Using Electrocardiogram. Proceedings - 2019
International Seminar on Intelligent Technology and Its Application, pp.
354–359
Mathew, H.B.D.L., Gupta, A., 2015. Controlling of Temperature
and Humidity for an Infant Incubator Using Microcontroller. International
Journal of Advanced Research in Electrical, Electronics and Instrumentation
Engineering, Volume 4(6), pp. 4975–4982
Muosa, A.H., 2017. Wireless Controland
Monitoring Systemfor Premature Infant IncubatorEnvironment. Journal of College of Education For Pure
Sciences (ICEPS), Volume 7(4), pp. 28–39
Nachabe, L., Girod-Genet, M., ElHassan, B., Jammas, J., 2015.
M-Health Application for Neonatal Incubator Signals Monitoring through a
CoAP-Based Multi-Agent System. 2015 International Conference on Advances in
Biomedical Engineering, pp. 170–173
Nayak, A., Singh, M., 2015. Study of Tuning of PID Controller By
Using Particle Swarm Optimization. International Journal of Advanced
Engineering Research and Studies IV(Jan.-March), Volume 346, p. 350
Ogata, K., 2005. Mathematical Modelling of Control Systems. Modern
control Engineering, pp. 13–62
Rahman, A., Hassan, N., Ihsan, S.I., 2022. Fuzzy
Logic Controlled Two Speed Electromagnetic Gearbox for Electric Vehicle. International
Journal of Technology 13(2), pp. 297–309
Sattar, Y., Chhabra, L., 2021. Electrocardiogram. StatPearls
Publishing
Shaib, M., Rashid, M., Hamawy, L., Arnout, M., El Majzoub, I.,
Zaylaa, A.J., 2017. Advanced Portable Preterm Baby Incubator. In 2017
Fourth International Conference on Advances in Biomedical Engineering (ICABME), pp.
1–4
Sinuraya, E.W., Pamungkas, 2019. Design of Temperature Control
System for Infant Incubator Using Auto Tuning Fuzzy-PI Controller. International
Journal of Engineering and Information Systems (IJEAIS), Volume 3(1), pp.
33–41
Utomo, S.B., Irawan, J.F., Mujibtamala, A., Nari, M.I., Amalia, R.,
2021. Automatic Baby Incubator System with Fuzzy-PID Controller. IOP
Conference Series: Materials Science and Engineering, Volume 1034(1), p.
012023
Utomo, T.P., Nuryani, N., Darmanto, 2017. QRS Peak Detection for
Heart Rate Monitoring on Android Smartphone. Journal of Physics: Conference
Series, Volume 909(1), p. 012006
Widhiada, W., Nindhia, T.G.T., Gantara, I.N., Budarsa, I.N.,
Suarndwipa, I.N., 2019a. Temperature Stability and Humidity on Infant Incubator
Based on Fuzzy Logic Control. ACM International Conference Proceeding Series,
pp. 155–159
Widhiada, W., Antara, I.N.G., Budiarsa, I.N. and Karohika, I.M.G.,
2019b. The Robust PID Control System of Temperature Stability and Humidity on
Infant Incubator Based on Arduino at Mega 2560. IOP Conference Series: Earth
and Environmental Science, Volume 248(1), p. 012046
Zaelani, A.V., Koestoer, R.A., Roihan, I.,
Harinaldi, 2019. Analysis of Temperature Stabilization in Grashof Incubator
with Environment Variations Based on Indonesian National Standard (SNI). AIP
Conference Proceedings, Volume 2062(1), p. 020003