Healthcare Monitoring COVID-19 Patients Based on IoT System

Abstract

At the beginning of the Coronavirus disease 2019 (COVID-19) pandemic, the world needed to develop an innovative, accurate system for caring for and following up with patients remotely to reduce the massive influx of patients into hospitals. Therefore, the well-established Internet of Things (IoT) technology was used to build an applied model for health care. The main objective of this study was to create a system connected to an application that allows continuous remote and early detection of clinical deterioration by monitoring different levels of biometrics to reduce the patient's risk of serious complications. Assessments were conducted on four subjects (two males, two females) aged 30-50 years with COVID-19. The system was examined under conditions and medical supervision in the hospital, following a schedule of vital measurements (oxygen saturation rate, heart rate and temperature). An average of 4 examinations was recorded per day over a week. The model has recorded the mean of error of oxygen saturation rate (SpO2), pulse rate, and body temperature as (0.3975%), (0.2625%) and (2.925%) for four patients. Keywords: Healthcare, COVID-19, IoT System

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Bionatura Issue 4 Vol 8 No 1 2023
Healthcare Monitoring COVID-19 Patients Based on IoT System
Marwa Mawfaq Mohamedsheet Al-Hatab1,* , Aseel Thamer Ebrahem2,* , Ali Rakan Hasan AL-JADER3,* ,
Maysaloon Abed Qasim4,*, and Entisar Y. Abd al-jabbar5,*
1 Technical Engineering Collage, Northern Technical University (NTU), Mosul, Iraq
2 Technical Engineering Collage, Northern Technical University (NTU), Mosul, Iraq
3 Technical Engineering Collage, Northern Technical University (NTU), Mosul, Iraq
4 Technical Engineering Collage, Northern Technical University (NTU), Mosul, Iraq
5 Technical Engineering Collage, Northern Technical University (NTU), Mosul, Iraq
* Correspondence: marwa.alhatab@ntu.edu.iq
Available from: http://dx.doi.org/10.21931/RB/CSS/2023.08.04.24
ABSTRACT
At the beginning of the Coronavirus disease 2019 (COVID-19) pandemic, the
world needed to develop an innovative, accurate system for caring for and fol-
lowing up with patients remotely to reduce the massive influx of patients into
hospitals. Therefore, the well-established Internet of Things (IoT) technology was
used to build an applied model for health care. The main objective of this study
was to create a system connected to an application that allows continuous remote
and early detection of clinical deterioration by monitoring different levels of bi-
ometrics to reduce the patient's risk of serious complications. Assessments were
conducted on four subjects (two males, two females) aged 30-50 years with
COVID-19. The system was examined under conditions and medical supervision
in the hospital, following a schedule of vital measurements (oxygen saturation rate,
heart rate and temperature). An average of 4 examinations was recorded per day
over a week. The model has recorded the mean of error of oxygen saturation rate
(SpO2), pulse rate, and body temperature as (0.3975%), (0.2625%) and (2.925%)
for four patients.
Keywords: Healthcare, COVID-19, IoT System
INTRODUCTION
The World Health Organization (WHO) has classified Coronavirus disease 2019
(COVID-19) as a pandemic because it is a serious and contagious disease.
The Internet of Things (IoT) gathers, processes, and evaluates the data in many
systems, such as monitoring healthcare and redefining how its facilities and sys-
tems are enhanced. The technologies of IoT are represented by sensors, real-time
testing, embedded systems, and machine learning that deal with the concept of
the intelligent hospital and wireless-based equipment. In the medical domain, the
best technology for monitoring is IoT. The basic devices to Execute the IoT tech-
nique are medical instruments, medical sensors, etc. 14,22.

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In this paper, The Electronic home monitoring system allows the remote monitor-
ing of COVID-19 patients and reduces the risk of respiratory syndrome. In addi-
tion, the system has a medical sensor that provides several vital signs, such as a
temperature sensor, pulse rate sensor and Spo2 sensor. These measurements were
applied to four people with COVID-19 under the supervision of specialist staff.
Healthcare, according to COVID-19, uses IoT in different techniques that follow
literature reviews in 2022, Akpan abd et al. presented medical software for the
care of COVID-19 patients by programming to store their data; it helps the
healthcare staff to monitor the COVID-19 patients' condition. Therefore, this
technique can help the doctor to give the diagnosis and treatment to the patient 23.
In 2022, Ndiaye et al. applied the artificial intelligence technique to check the
performance of the acquired information of COVID-19 patients. They had accu-
rate results that helped to reduce the respiratory syndrome risk 24. In 2019,
Haleem et al. researched an intelligent sensor that presents data on the health
conditions of COVID-19 patients by checking vital measurements. In addition,
the results of this technique are represented by the precision and trust in the med-
ical field 25. In 2019, Janeh was present in Virtual Reality (VR). This electronic
technology Contributes to the preparation of the COVID-19 patients' data in real-
time and provides the schedule of the perfect treatment for them 26. In 2018,
Jagadeeswari et al. analyzed big data containing information about COVID-19
patients and stored it in hard copy or digital form. Healthcare has speed solutions
with this system 27. In the same year, Stergiou et al. presented a cloud computing
system used to store the information of COVID-19 patients on a computer with
the Internet. This technique helps the healthcare staff perform best for monitoring
COVID-19 patients 28. 2015 Lu et al. have been monitoring the responsible sys-
tem for the needed medical measurements. This technology has an accuracy in
the final results 29.
MATERIAL AND METHOD
Proposed Research Methodology
The IoT technology connects medical tools, gadgets, and machines to develop in-
telligent information systems tailored to the needs of COVID-19 patients. A dis-
tinct interdisciplinary strategy is required to improve production, quality, and in-
formation about impending diseases. In order to assess relevant data, IoT tech-
nology monitors changes in critical patient states. Also, IoT technologies signifi-
cantly influence high-quality medical equipment, which helps provide a personal-
ized response during the COVID-19 epidemic. These technologies can acquire,
store, and analyze data digitally. All healthcare records are stored digitally, and
patient data and information may be sent quickly in an emergency, allowing phy-
sicians to operate more efficiently 30. Several recent studies have used smart sen-
sors to achieve a high capacity level for monitoring and managing the significant
needs of medical temperature, Spo2 and pulse rate, and information regarding
Covid-19 patient health 31,32.
Required Materials
To execute the system, the following is described in the COVID-19 monitoring
diagram as shown in Figure 1:

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Figure 1. Block diagram for COVID-19 patient monitoring system.
The monitoring system designed in this work focuses on the equipment and
sensors (hardware) used for biometrics and the software by use of Blynk. The
hardware system included many parts, starting with the power supply, the
microcontroller, sensors of vital functions and output devices. The following are
the most important parts used to build the system:
An ESP-WROOM-32 module in the breadboard is a Node MCU ESP32 board.
At the heart of this module is the ESP32 processor, which is expandable and
adaptive. The clock frequency can be varied from 80 to 240 MHz, and two CPU
cores can be handled independently. It also works with real-time operating
systems (RTOS). The ESP-WROOM-32s module supports traditional Bluetooth,
Bluetooth low energy, and Wi-Fi. With various uses: Users can connect to a
mobile phone or broadcast a BLE Beacon for signal detection using Wi-Fi. For
signal detection, users can connect to a mobile phone or emit a BLE Beacon 33.
The module also supports data rates of up to 150 Mbps for maximum wireless
communication and antenna output power of 20 dBm. Consequently, this module
meets industry standards for transmission distance, high integration, network
connectivity, wireless connectivity and power consumption.
The MAX30100 is a full Spo2 and heart rate sensor system solution designed for
highly demanding wearable devices. The MAX30100 has a tiny overall solution
size while maintaining excellent optical and electrical performance. Only a few
external hardware components are required to incorporate into a wearable. The
MAX30100 features software registers for complete customization, and the
digital output data is stored in a 16-deep FIFO within the device. The FIFO
enables the MAX30100 to communicate with a microcontroller or
microprocessor across a shared bus without frequently reading data from the
device's Memories 34.
The MAX30205 temperature sensor detects temperature precisely and generates
an overheat alert or shutdown signal. This device transfers temperature
information to digital form (ADC) using a high-resolution sigma-delta analog-to-
digital converter. The accuracy fulfills ASTM E1112 clinical thermometry
criteria when placed on the final PCB. An I2C-compatible 2-wire serial interface
is used for communication to read temperature data and control the behavior of

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the open-drain overtemperature shutdown output. The I2C serial interface accepts
standard write, read, transmit, and receive byte instructions. The sensor's low
600A supply current, 2.7V to 3.3V supply voltage range, and lockup-protected
I2C-compatible interface make it excellent for wearable fitness and medical
applications 35.
Figure 2. The practical design for the COVID-19 patient monitoring model
Required Software
The second part involved designing a software application based on the Blynk
application to provide a remote display of vital readings in real-time. It can con-
sider an IoT application programming interface that enables data collection and
storage from devices connected to the Internet, an AP monitoring system con-
nects to the cloud and builds web applications that allow users to scan data in re-
al-time and run it remotely 36. The Blynk platform was used to design the pro-
posed model for measuring vital signs, including Spo2, heart rate and tempera-
ture. The Arabic language for programming the system has been adopted for ease
of use and handling in the country where the experiment was applied, as shown
in Figure 3, which can be downloaded for Android and iOS. Project objectives
have been achieved.

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Figure 3. Programming the Blynk application illustrates recording the system in real-time Spo2, pulse rate and body
temperature (vital measurements)
RESULTS
Four patients infected with COVID-19 (two males, two females) and their ages
ranged from (30-50 years) were studied and monitored in a COVID-19 isolation
hospital for one week. The average was taken for four readings per day (every six
hours) depending on the variables vital signs and for both Spo2, temperature and
pulse rate through direct clinical monitoring of patients in the hospital in
cooperation with the medical and nursing staff and after obtaining official
approvals. In conjunction with taking clinical readings for the three variables
mentioned for the same patients and at the same time using the proposed system
IoT by the researchers. The following results were obtained:

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Day
Patient 1
Patient 2
Patient 3
Patient 4
Average of
Hospital
Monitoring
Reading
Average of
Proposed
System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed
System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed
System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed
System
Reading
Saturday
91
90
89
90.5
87
86.7
89
89.6
Sunday
90
91
88
88.4
88
89
87
86
Monday
90
91.2
89
88.3
86
84.9
90
91
Tuesday
88
89.5
88
87
86
88.1
89
90.5
Wednesday
87
86.8
87
89
84
85
87
86
Thursday
86
86.7
85
83.9
85
84.4
86
85.9
Friday
85
84
87
87.4
84
86
84
85
Percentage of
Error (%)
0.35 %
0.23 %
0.68 %
0.33 %
Mean of Percentage of Error for 4 Patients
0.3975 %
Table 1. Reading of Oximeter (%)
Day
Patient 1
Patient 2
Patient 3
Patient 4
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Saturday
90
92
95
94. 2
97
96
91
92
Sunday
91
90.7
96
97
98
99.3
90
89
Monday
93
92
98
98.8
100
102
92
93.2
Tuesday
95
96.2
100
99
108
106.8
97
98
Wednesday
97
97.8
99
100.5
102
103
95
94.8
Thursday
100
99
105
103.5
114
110.7
100
103
Friday
105
102
107
108
101
100.5
104
102.4
Percentage of
Error (%)
0.18 %
0.14 %
0.23 %
0.5 %
Mean of Percentage of Error for 4 Patients
0.2625 %
Table 2. Reading of Pulsimeter (beat/min)

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Day
Patient 1
Patient 2
Patient 3
Patient 4
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Average of
Hospital
Monitoring
Reading
Average of
Proposed System
Reading
Saturday
37
36
37.6
36.8
39.9
38
37.2
36
Sunday
37.3
37
38.4
37
38.3
37.4
37.8
37
Monday
38
37
38.6
37.4
39.5
38.6
38.6
37.1
Tuesday
38.9
37.4
39.5
38
39.5
38
39
38.2
Wednesday
39
38
39.7
38.2
39.7
38.2
39.6
38.9
Thursday
39.5
37.9
39.4
38
40
38.9
39.9
39
Friday
39.5
38.2
40.4
39
40.5
39
40.1
39.6
Percentage of
Error (%)
2.8 %
3.3 %
3.3 %
2.3 %
Mean of Percentage of Error for 4 Patients
2.925 %
Table 3. Reading of Temperature ( C)
Figure 4. Relationship between oxygen saturation and pulse rate readings
Figure 4. Relationship between oxygen saturation and pulse rate readings
Average of Hospital Reading (SpO2) Average of Hospital Reading (pulse rate)
Average of Proposed Reading (SpO2) Average of Proposed Reading (pulse rate)

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Figure 5. Relationship between temperature readings
For the first patient:
The percentage of error of SpO2 between the hospital reading and the proposed
system reading is (0.35%) as shown in Table 1. The percentage of pulse rate error
between the hospital reading and the proposed system reading is (0.18%) as
shown in Table 2. The percentage of temperature error between the hospital
reading and the proposed system reading is (2.8%) as shown in Table 3.
For the second patient:
The percentage of error of SpO2 between the hospital reading and the proposed
system reading is (0.23%) as shown in Table 1. The percentage of pulse rate
between the hospital reading and the proposed system reading is (0.14%) Table 2,
and the percentage of temperature error between the hospital reading and the
proposed system reading is (3.3%) Table 3.
For the third patient:
The percentage of error of SpO2 between the hospital reading and the proposed
system reading is (0.68%) Table 1, the percentage of error of pulse rate between
the hospital reading and the proposed system is (0.23%) Table 2, and the
percentage of error of temperature between the hospital reading and the proposed
system reading is (3.3%) Table 3.
For the fourth patient:
The percentage of error of SpO2 between the hospital reading and the proposed
system reading is (0.33%) in Table 1, the percentage of error of pulse rate
between the hospital reading and the proposed system reading is (0.5%) in Table
2, and the percentage of error of temperature between the hospital reading and the
proposed system reading is (2.3%) Table 3.
Average of Hospital Reading (Temp.) Average of Proposed Reading (Temp.)

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Through the results obtained, we noticed that the mean error of SpO2 and pulse
rate for four patients are (0.3975%) and (0.2625%) as shown in Table 1,2, which
means there is a small percentage of error between oxygen saturation rate and
pulse rate as shown in Figure 4,
DISCUSSION
The visual similarity between Coronavirus's first and second sequence is consid-
ered a severe respiratory syndrome 1,2,3. Covid -19 led to clinical, economic, and
social crises. Otherwise, respiratory syndrome is a big risk of death for patients
who suffer from high blood pressure, diabetes, low immunity, chronic diseases,
and seniors 4,5. The high number of COVID-19 patients and massive flow to hos-
pitals confused health care staff service. Moreover, the contact between patients
and the staff caused increased injury with COVID-19. Therefore, remote patient
monitoring is the best mechanism for following up on the sick and presenting ap-
propriate treatment for his state 6,7.
Healthcare offers how to deal with COVID-19 patients, social distancing, and
quarantine measures, so electronic home monitoring can be considered great at-
tention for patients suffering from coronavirus 8,9,10. Electronic home monitoring
helps the healthcare staff monitor COVID-19 patients and rate their health condi-
tion at home using several sensors. Wearable sensors give vital signs for the hu-
man body's health and physical performance, such as temperature, oxygen satura-
tion rate (Spo2), breathing, A ratio of beats per minute (BPM), and others 11,12,13.
CONCLUSIONS
This leads us to conclude that the proposed system can be relied upon in the daily
monitoring of the condition of Covid-19 patients. The mean temperature error for
four patients is (2.925%) as shown in Table 3, which is relatively large, as shown
in Figure 5 because the proposed system relied on taking the temperature through
the end of the finger. It is considered the weakest method of measuring
temperature and can be treated by adding (0.5) degrees to the reading recorded by
the proposed system.
Reference:
1. Rathee, Geetanjali, et al. "ANN assisted-IoT enabled COVID-19 patient monitoring." Ieee Access 9
(2021): 42483-42492.
2. Shen, Bo, et al. "Proteomic and metabolomic characterization of COVID-19 patient sera." Cell 182.1
(2020): 59-72.
3. Yang, Po, et al. "Feasibility study of mitigation and suppression strategies for controlling COVID-19
outbreaks in London and Wuhan." PloS one 15.8 (2020): e0236857.
4. Nakshbandi, G., C. C. Moor, and M. S. Wijsenbeek. "Home monitoring for patients with ILD and the
COVID-19 pandemic." The Lancet Respiratory Medicine 8.12 (2020): 1172-1174.
5. Wijsenbeek, Marlies, and Vincent Cottin. "Spectrum of fibrotic lung diseases." New England Journal of
Medicine 383.10 (2020): 958-968.
6. Bunch, Alfonso, et al. "Through the storm: Automated peritoneal dialysis with remote patient monitor-
ing during COVID-19 pandemic." Blood Purification (2020): 1.
7. Annis, Tucker, et al. "Rapid implementation of a COVID-19 remote patient monitoring pro-
gram." Journal of the American Medical Informatics Association 27.8 (2020): 1326-1330.
8. Mukati, Naveen, et al. "Healthcare assistance to COVID-19 patient using Internet of Things (IoT) ena-
bled technologies." Materials Today: Proceedings (2021).
9. de Morais Barroca Filho, Itamir, et al. "An IoT-based healthcare platform for patients in ICU beds dur-
ing the COVID-19 outbreak." Ieee Access 9 (2021): 27262-27277.

Bionatura http://dx.doi.org/10.21931/RB/CSS/2023.08.04.24 10
10. Javaid, Mohd, and Ibrahim Haleem Khan. "Internet of Things (IoT) enabled healthcare helps to take the
challenges of COVID-19 Pandemic." Journal of Oral Biology and Craniofacial Research 11.2 (2021):
209-214.
11. Gordon, William J., et al. "Remote patient monitoring program for hospital discharged COVID-19 pa-
tients." Applied clinical informatics 11.05 (2020): 792-801.
12. Aalam, Ahmad A., et al. "Remote patient monitoring for ED discharges in the COVID-19 pandem-
ic." Emergency Medicine Journal 38.3 (2021): 229-231.
13. Jarjees, Mohammed S., et al. "Thingspeak-based respiratory rate streaming system for essential moni-
toring purposes." Bio-Algorithms and Med-Systems 16.3 (2020).
14. Ayoub, Mohammed G., Mazin N. Farhan, and Mohammed S. Jarjees. "Streaming in-patient BPM data
to the cloud with a real-time monitoring system." TELKOMNIKA 17.6 (2019): 3120-3125.
15. Javaid, Mohd, and Ibrahim Haleem Khan. "Internet of Things (IoT) enabled healthcare helps to take the
challenges of COVID-19 Pandemic." Journal of Oral Biology and Craniofacial Research 11.2 (2021):
209-214.
16. Singh, Ravi Pratap, et al. "Internet of things (IoT) applications to fight against COVID-19 pandem-
ic." Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14.4 (2020): 521-524.
17. Marwa MohamedSheet AL-Hatab, Raid Rafi Omar Al-Nima, Ilaria Marcantoni, Camillo Porcaro,
and Laura Burattini, “Classifying Various Brain Activities by Exploiting Deep Learning Techniques and
Genetic Algorithm Fusion Method”, TEST Engineering & Management, Vol. 83, pp. 3035-3052, 2020.
18. Marwa MohamedSheet AL-Hatab, Raid Rafi Omar Al-Nima, Ilaria Marcantoni, Camillo Porcaro, and
Laura Burattini, “Comparison Study Between Three Axis Views of Vision, Motor and Pre-Frontal Brain
Activities”, Journal of Critical Reviews, Vol. 7, Issue 5, pp. 2598-2607, 2020.
doi:10.31838/jcr.07.05.430
19. Al-Hatab, Marwa Mawfaq Mohamedsheet, et al. "Innovative Non-Invasive Blood Sugar Level Monitor-
ing for Diabetes Using UWB Sensor." Journal of Optoelectronics Laser 41.4 (2022): 422-437.
20. Abd Al-jabbar, Entisar Y., et al. "Study Axial Plane with Artificial Intelligence for the Purpose of Clas-
sifying Brain Tasks." Journal of Optoelectronics Laser 41.4 (2022): 433-439.
21. Rasha Dheyaa Mahdi, Maysaloon Abed Qasim,
22. Noor M. Allayla, Refed Adnan Jaleel. "A Customized IOMT- Cloud Based Healthcare
23. System for Analyzing of Brain Signals Via
24. Supervised Mining Algorithms".Journal of Engineering Science and Technology, 17, pp. 76–83 ,2022
25. Entisar Younis Al-Iraqi, Rashid Ali Fayadh. "UWB Transceiver for Non-Invasive Glucose
26. Concentration Measurement in Blood: Review" . Volume 62, Issue 04, April,pp.1649-1661 ,2020
27. Akpan, Ikpe Justice, Elijah Abasifreke Paul Udoh, and Bamidele Adebisi. "Small business awareness
and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the
COVID-19 pandemic." Journal of Small Business & Entrepreneurship 34.2 (2022): 123-140.
28. Ndiaye, Musa, et al. "IoT in the wake of COVID-19: A survey on contributions, challenges and evolu-
tion." Ieee Access 8 (2020): 186821-186839.
29. Haleem, Abid, and Mohd Javaid. "Industry 5.0 and its applications in orthopaedics." Journal of Clinical
Orthopaedics & Trauma 10.4 (2019): 807-808.
30. Janeh, O. "Gait training in virtual reality: short-term effects of different virtual manipulation techniques
in Parkinson’s disease. Cells 8." (2019).
31. Jagadeeswari, V., et al. "A study on medical Internet of Things and Big Data in personalized healthcare
system." Health information science and systems 6.1 (2018): 1-20.
32. Stergiou, Christos, et al. "Secure integration of IoT and cloud computing." Future Generation Comput-
er Systems 78 (2018): 964-975.
33. Lu, Haibao, Yongtao Yao, and Long Lin. "Temperature sensing and actuating capabilities of polymeric
shape memory composite containing thermochromic particles." Pigment & Resin Technology (2015).
34. Jia, Qiong, et al. "Big data analytics in the fight against major public health incidents (Including
COVID-19): a conceptual framework." International journal of environmental research and public
health 17.17 (2020).
35. Khan, Mohammad Monirujjaman, et al. "IoT-Based Smart Health Monitoring System for COVID-19
Patients." Computational and Mathematical Methods in Medicine 2021 (2021).

Bionatura http://dx.doi.org/10.21931/RB/CSS/2023.08.04.24 11
36. Ennafiri, M., and T. Mazri. "Internet of Things for smart healthcare: A review on a potential IOT-based
system and technologies to control COVID-19 pandemic." The Proceedings of the Third International
Conference on Smart City Applications. Springer, Cham, 2020.
37. Hakkı, S. O. Y. "ESP8266 and ESP32 Series of SoC Microcontrollers." Programmable Smart Micro-
controller Cards (2021): 110.
38. Esmaeiliazad, Tina. "Special Study of Earlobe Pulse Oximeter Using MAX30100 for Detecting SpO2
and Heart Beat." ACADEMIC JOURNAL (2021).
39. Sudha, S., P. Shruthi, and M. Sharanya. "IoT based measurement of body temperature using
max30205." Int. Res. J. Eng. Technol 5 (2018): 3913-3915.
40. Durani, Homera, et al. "Smart automated home application using IoT with Blynk app." 2018 Second
international conference on inventive communication and computational technologies (ICICCT). IEEE,
2018.
Received: May 15, 2023/ Accepted: June 10, 2023 / Published: June 15, 2023
Citation: Al-Hatab, M., M. Ebrahem A., T. Al-Jader A., R. Qaism, M., A.; Al-Jabbar, E.Y. Healthcare
Monitoring COVID-19 Patients Based on IoT System. Revista Bionatura 2023;8 (2) 63.
http://dx.doi.org/10.21931/RB/CSS/2023.08.04.24