Journal of Information Technology and Computer Science Volume 8, Number 2, August 2023, pp. 170-178
Journal Homepage: www.jitecs.ub.ac.id
Wearable Wireless Sensor Network for Mitigating COVID-19 Transmission Through Physical Distancing
Muhammad Niswar*1, Andani2, Muhammad Ahlan Fachrurrozie Haris2, Achmad Basuki 3
1,2,3Hasanuddin University, Makassar, 3Brawijaya University, Malang
1[email protected], 2[email protected], 3[email protected], 4[email protected]
*Corresponding Author
Received 08 June 2023; accepted 29 August 2023
Abstract. The global community is currently facing a critical situation with the widespread transmission of the Covid-19 virus. The rapid increase in Covid-19 cases has necessitated immediate action and treatment. As the World Health Organization (WHO) recommended, maintaining a minimum distance of one meter between individuals is an effective preventive measure to avoid transmission through respiratory droplets. To address this issue, this paper introduces a cost-effective design and implementation of a wearable wireless sensor network using the ESP32 microcontroller and Bluetooth Low-Energy (BLE) technology that promotes physical distancing and contact tracing to mitigate the spread of COVID-19. In this research, we have determined that the safe distance between individuals is 1.5 meters. Proximity testing has indicated that an RSSI value of -62 dBm corresponds to a distance below 1.5 meters. Hence, we set -62 dBm as the distance threshold between wearable devices to ensure safe physical distancing. These wearable wireless sensor network devices are intended to assist individuals in maintaining a safe distance from others, thereby reducing the risk of COVID-19 transmission.
Keywords: Covid19, Physical Distance, Wireless Sensor Network, BLE
1 Introduction
The global community is currently on high alert due to the widespread transmission of a virus called Covid-19. Covid-19 is a type of virus that can cause various illnesses, ranging from mild flu-like symptoms to more severe conditions like Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). Common symptoms include cough, fever, fatigue, shortness of breath, and loss of appetite. Unlike the common cold, the Covid-19 virus can rapidly progress and lead to more severe infections. The rapid increase in Covid-19 cases necessitates immediate action and treatment. This virus has a high potential for easy transmission, impacting individuals of all age groups. Consequently, several governments have implemented strict measures such as lockdowns and isolation protocols to curb the spread. References [1], [2], [3], [4], [5] provide insight into the danger of Covid-19, ranging from its impact on healthcare workers to the role of asymptomatic carriers and the importance of control measures in mitigating transmission.
Implementing effective preventive measures can help reduce the number of cases. According to the World Health Organization (WHO) [6], one crucial preventive measure is maintaining a minimum distance of one meter between individuals. Many
Muhammad Niswar et al. , Wearable Wireless Sensor Network... 171 countries have enforced policies promoting physical distancing to prevent transmission through respiratory droplets, including Indonesia, which has transitioned from social distancing to physical distancing. The recommended distance for effective physical distancing is approximately 1 to 3 meters. Adhering to physical distancing guidelines aims to decrease contact and ultimately lower the transmission rate of Covid-19.
There are several studies on using information technology to mitigate the spread of COVID-19. Reference [7], [8], [9] presents the applications of IoT (Internet of Things) in the context of the COVID-19 pandemic. It explores the various ways in which IoT has been applied and highlights the significant applications that have emerged during this global health crisis. Reference [10] explores the effectiveness of digital contact tracing in reducing the transmission of COVID-19. The study uses simulation models to evaluate the impact of digital contact tracing on controlling the spread of the virus. Through their simulations, the researchers demonstrate that digital contact tracing can significantly slow down or even prevent COVID-19 transmission.
They find that when a high proportion of the population uses a digital contact tracing app, the spread of the virus can be substantially reduced. Reference [11] presents a study that quantifies the potential impact of digital contact tracing on controlling the transmission of SARS-CoV-2, the virus responsible for COVID-19. The research aims to assess the effectiveness of digital contact tracing in containing the epidemic. The study utilizes mathematical modeling and simulation techniques to evaluate the role of digital contact tracing in reducing transmission. The authors investigate different scenarios and parameters to estimate the potential impact of contact tracing apps on controlling the spread of the virus. Reference [12] presents a comprehensive survey of the applications of the Internet of Things (IoT) in addressing the challenges posed by the COVID-19 pandemic. The article provides an overview of IoT-based solutions for COVID-19, such as telemedicine, remote patient monitoring, and smart quarantine systems. It discusses the use of IoT in monitoring body temperature, respiratory rates, and other vital signs to detect early symptoms and manage the spread of the virus.
Reference [13] provides a comprehensive analysis of smart healthcare systems implemented during the COVID-19 pandemic. The authors conduct a systematic review to explore the various applications of smart technologies in healthcare settings to combat the challenges posed by the pandemic
.
Reference [14] discusses the use of Internet of Things (IoT) and Artificial Intelligence (AI)-enabled 5G networks for crowd monitoring and social distancing to combat the COVID-19 pandemic. They propose a system that utilizes IoT devices, such as cameras and sensors, to monitor crowd density and detect violations of social distancing guidelines. Reference [15] introduces an intelligent system for COVID-19 diagnosis and prediction using Wireless Sensor Networks (WSN) and Machine Learning. They propose a system that utilizes WSN to collect physiological data from individuals, such as body temperature, heart rate, and respiratory rate. This data is then fed into a machine learning algorithm for analysis and prediction of COVID-19 infection. References [16] propose an affordable approach to implementing social distancing measures by leveraging inexpensive IoT sensors and utilizing existing protective equipment like plastic face shields. References [17]presents a cost-effective solution for indoor navigation in large smart buildings, ensuring social distancing. The system utilizes Bluetooth Low Energy (BLE) and strategically placed BLE Beacons for guiding users to their destinations. References [18] propose a localization method using PIR sensors to track human positions in outdoor environments. Reference [19] introduces a wearable device that utilizes RF communication for information exchange and proximity estimation, enabling social distancing and contact tracing. Reference [20] introduces a novel smart social distance system using a compact and affordable wearable device that enables individuals to maintain safe distances, mitigating COVID-19 exposure and slowing its local and national spread. Reference [21] introduces a compact and affordable wearable device
172 Volume 8, Number 2, August 2023, pp. 170-178
p-ISSN: 2540-9433; e-ISSN: 2540-9824
that estimates user proximity using Wi-Fi signal strength and provides audible and visual alerts when distancing is insufficient, offering a promising solution for practicing social distancing during the COVID-19 pandemic. Reference [22] proposes a Social Distance Vest for Covid-19 Prevention that utilizes Arduino Uno, D6T-44L-06 thermal sensor, HC-SR04 Ultrasonic sensor, LED, and a buzzer mounted on a reflective vest.
Reference [23] presents IoT and AI-based system that monitors physical distancing and tracks physical contact in a room, utilizing a Raspberry Pi, webcam, and web application to display contact information, with YOLO algorithms for human object detection and the Euclidean distance formula for measuring distances.
This paper proposes a low-cost wearable wireless sensor network using Bluetooth Low-Energy (BLE) technology for physical distancing to reduce COVID-19 transmission. This paper describes the design and implementation of the wearable device and contact tracing web application. Moreover, it evaluates the wearable device’s capability to detect the distance between devices.
Figure 1. Research Method
2 Method
Our research method, as shown in Figure 1, encompasses a systematic approach involving several phases. It commences with describing the Problem Statement, outlining the urgency of addressing COVID-19 transmission and the need for innovative solutions. The subsequent Literature Review delves into existing strategies for transmission mitigation and wearable technologies, establishing a foundation for the proposed system. System Requirements are meticulously defined, considering functional aspects like distance accuracy and user-friendliness, followed by the architectural System Design and Development phase, which designs the hardware- software integration for the wearable system. Implementation translates the design into a functional system using the microcontroller and BLE technology. Lastly, Evaluation assesses the system's efficacy through performance testing, accuracy validation, and
Muhammad Niswar et al. , Wearable Wireless Sensor Network... 173 potential user engagement, ensuring the viability of the proposed solution for effective COVID-19 transmission mitigation.
This research proposed a wearable device that serves as a personal proximity monitor, providing real-time alerts and reminders to individuals to maintain a safe distance for mitigating the transmission of COVID-19 in the room. The following subsections describe the system of the proposed wearable devices.
2.1 Hardware Design
The hardware design of wearable medical devices consists of an ESP32 microcontroller [16], nRF51822 BLE technology, Buzzer, LED, and Battery, as shown in Figure 2. The ESP32 microcontroller serves as a receiver and captures the BLE beacon. It reads the ID (Identifier) of the BLE beacon and calculates the received signal strength to estimate the distance. Based on this calculation, the ESP32 controls the activation and deactivation of the buzzer and LED indicators. The Bluetooth beacon transmits a unique ID associated with each user, allowing for identifying and tracking physical contacts between individuals. Additionally, the ESP32 sends the physical contact data to the server and stores it in the database through a Wi-Fi connection, hence, we can conduct future contact tracing in case any of the users are infected with COVID-19. The ESP32 microcontroller serves as the core of the device, enabling data processing, wireless communication, and interaction with other devices. Table 1 shows the hardware specification of wearable device.
Table 1. Hardware Specifications
Components Specification
Micro-controller ESP32 (WeMos Lolin 32 Board) Sensor Bluetooth Low-Energy (nRF51822)
Output Devices LED, Buzzer
Battery Li-Po Battery 300mH
We use external BLE (nRF51822), instead of using the built-in Bluetooth of the ESP32 because of the coexistence challenges posed by the shared antenna between Wi- Fi and Bluetooth functionalities in the ESP32. We use a Wi-Fi connection for sending physical contact data to the server. This shared antenna can interfere with Wi-Fi and Bluetooth signals when both are active simultaneously, impacting their performance and stability. Additionally, the operational challenges outlined, including coexistence difficulties, power management concerns, potential packet loss, and latency issues, highlight the complexities of concurrently utilizing both Wi-Fi and built-in Bluetooth.
Using an external BLE (nRF51822) module can help overcome these challenges by offering a dedicated antenna for Bluetooth communication, reducing the risk of interference, enhancing transmission range, and leading to more reliable performance in the application.
Figure 2. Hardware Design
174 Volume 8, Number 2, August 2023, pp. 170-178
p-ISSN: 2540-9433; e-ISSN: 2540-9824 2.2 Distance Measurement
The ESP32, equipped with BLE capabilities, receives signals from nearby nRF51822 BLE beacons. By measuring the Received Signal Strength Indicator (RSSI), which indicates the signal strength between two devices, the ESP32 can estimate the proximity or distance to nearby BLE device. As the distance between devices changes, the RSSI value will vary accordingly. By monitoring changes in RSSI, proximity to a BLE device can be determined. Closer proximity results in a stronger RSSI, while greater distance leads to a weaker RSSI. When the distance between two BLE devices reaches less than 1.5 meters, LED and Buzzer are activated to alert users to maintain physical distance. The formula used to get the distance measure using the RSSI parameter is:
(1)
Where:
A = Signal strength measured at a distance 1 meter (dBm).
RSSI = Signal strength received by the receiver (dBm).
n = Propagation Constant (urban area:2.7)
DEFINE beacon_address = "00:00:00:00:00:00"
DEFINE rssi_threshold = -62
DEFINE bt_interface = bluetooth.Bluetooth() SET beacon = bt_interface.connect(beacon_address) WHILE True DO
SET data = beacon.receive() SET rssi = data["rssi"]
SET beacon_id = data["id"]
IF rssi < rssi_threshold THEN PRINT "RSSI:", rssi
PRINT "Beacon ID:", beacon_id CALL activate_buzzer() CALL activate_led() END IF
CALL continue_receiving() END WHILE
Figure 3. Pseudo-code of Proximity Alert
Figure 3 shows the pseudocode of the proximity alert that implemented in ESP32 microcontroller. The ESP32 continuously receives data from the BLE beacon and extracts the received signal strength indicator (RSSI) and beacon ID from the data. It
Muhammad Niswar et al. , Wearable Wireless Sensor Network... 175 then compares the RSSI value to a predefined threshold to determine if the beacon is within a certain proximity. If the RSSI value is lower than the threshold, indicating close proximity, the program prints the RSSI value and beacon ID. It also calls two functions, "activate_buzzer()" and "activate_led()", which activate a buzzer and LED, respectively, to provide a visual and audible indication of the beacon's proximity. The program then continues to receive data from the beacon.
2.3 System Design
Figure 4 shows the overall system design of the wearable physical distancing device and contact tracing to reduce the risk of Covid-19 transmission. When a person wearing the sensor device comes close to another person (within 1.5 meters), the wearable device promptly transmits the physical contact data, including the user’s ID, username, and timestamp, to the server and stores it in the database through a WiFi connection. By preserving the physical contact information in the database, we establish a foundation for future contact tracing efforts in case any of the users contract COVID- 19.
Figure 4. System Design
3 Results and Discussion
3.1. Proximity Testing
We conducted a series of tests on the proposed system to validate its capability to detect the distance between devices and to observe the relationship between RSSI and the actual distance.
Figure 5. Testing Scenario
176 Volume 8, Number 2, August 2023, pp. 170-178
p-ISSN: 2540-9433; e-ISSN: 2540-9824
The test involved measuring RSSI while varying the distance between two devices, as shown in Figure 4. The results clearly show that the RSSI level decreases as the distance between devices increases, as shown in Figure 5. From Figure 5, it can be seen that RSSI level above -62 dBm corresponds to a distance below 1.5 meters.
Therefore, we set -62 dBm as the distance threshold between devices to ensure physical distancing. When this threshold is breached, the buzzer and LED in the wearable device are activated to alert the users to maintain physical distancing.
Figure 6. Relationship between RSSI and actual distance 3.2. Web-based Contact Tracing
In order to effectively store data and track users' physical contacts in the event of a Covid-19 infection, we have developed a web application as shown in Figure 5.
This web application uses the PHP framework and utilizes a MySQL database to securely store the relevant physical contact information. This web application aims to establish a tracking system that can efficiently record and manage the physical contact data of users. This data includes essential details such as user ID, username, and timestamp, which are essential for accurate contact tracing. The MySQL database has been chosen as the backend storage solution due to enabling efficient and structured data management, allowing for quick and accurate retrieval and analysis of physical contact information when needed.
Figure 5. Contact Tracing Web Application
Muhammad Niswar et al. , Wearable Wireless Sensor Network... 177
4 Conclusion and Future Directions
The COVID-19 pandemic has underscored the importance of maintaining physical distance to prevent the transmission of the virus. This paper introduces the design and implementation of a cost-effective wearable wireless sensor network. This network employs the ESP32 microcontroller and Bluetooth (nRF51822) technology to enforce physical distancing, thereby mitigating the spread of COVID-19. Proximity testing revealed that an RSSI of -62 dBm corresponds to a distance less than 1.5 meters.
Hence, we established -62 dBm as the threshold to ensure proper distancing between devices. These wearable wireless sensor network devices aim to assist individuals in maintaining a safe physical distance and reducing the risk of COVID-19 transmission.
However, our testing was conducted within a limited scope of 2-3 wearable device nodes. In the near future, we plan to conduct comprehensive tests across various real- world scenarios, particularly in densely populated environments. These tests will evaluate the accuracy, reliability, and overall effectiveness of the wearable sensor network. We will also consider factors such as signal interference and battery life during our assessments.
References
1. Weissman DN, de Perio MA, Radonovich LJ. COVID-19 and Risks Posed to Personnel During Endotracheal Intubation. JAMA. 323(20):2027–2028. doi:10.1001/jama.2020.6627 (2020)
2. Meyerowitz EA, Richterman A, Gandhi RT, Sax PE. Transmission of SARS-CoV-2: A Review of Viral, Host, and Environmental Factors. Ann Intern Med. Jan;174(1):69-79. doi:
10.7326/M20-5008. Epub 2020 Sep 17. PMID: 32941052; PMCID: PMC7505025 (2021) 3. Moritz U. G. Kraemer et al. The effect of human mobility and control measures on the
COVID-19 epidemic in China. Science 368, 493-497 (2020)
4. Henrik Salje et al. Estimating the burden of SARS-CoV-2 in France.Science 369, 208-211 (2020)
5. Wilmes P, Zimmer J, Schulz J, Glod F, Veiber L, Mombaerts L, Rodrigues B, Aalto A, Pastore J, Snoeck CJ, Ollert M, Fagherazzi G, Mossong J, Goncalves J, Skupin A, Nehrbass U. SARS-CoV-2 transmission risk from asymptomatic carriers: Results from a mass screening programme in Luxembourg. Lancet Reg Health Eur. 2021 May;4:100056. doi:
10.1016/j.lanepe.2021.100056. Epub Feb 27. PMID: 33997830; PMCID: PMC7912359 (2021)
6. WHO. Coronavirus disease (COVID-19) advice for the public. WHO. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice- for-public (2020)
7. Ogundokun, Roseline & Bamidele, Awotunde & Adeniyi, Emmanuel & Misra, Sanjay.
Application of the Internet of Things (IoT) to Fight the COVID-19 Pandemic. 10.1007/978- 3-030-81473-1_4 (2022)
8. Gasmi, Amin. Enabled IoT Applications for Covid-19. 10.1007/978-3-030-68936-0_15.
(2021)
9. Kumar, Mukesh & Nayar, Nandini & Mehta, Gaurav & Sharma, Abha. Application of IoT in Current Pandemic of COVID-19. IOP Conference Series: Materials Science and Engineering.
1022. 012063. 10.1088/1757-899X/1022/1/012063 (2021)
10. Seresinhe, R., Radford, A., & Reed-Tsochas, F. Digital contact tracing can slow or even prevent COVID-19 transmission: results from a simulation study. Journal of Medical Internet Research, 23(2), e23448 (2021)
11. Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., ... & Fraser, C. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), eabb6936 (2020)
12. Akram, R. N., Javaid, N., Niaz, I. A., Qasim, U., & Khan, Z. A. Internet of Things for COVID-19: A Survey. IEEE Access, 8, 130783-130807 (2020)
13. Silva, B. M., Rodrigues, J. J., & Saleem, K. Smart healthcare systems for COVID-19
178 Volume 8, Number 2, August 2023, pp. 170-178
p-ISSN: 2540-9433; e-ISSN: 2540-9824
14. Akbar, S. A., Majeed, A., Javaid, N., & Ilahi, M. (2020). IoT-Based Crowd Monitoring for Social Distancing Using AI-Enabled 5G Networks for the Combat of COVID-19 Pandemic.
IEEE Network, 34(4), 122-130 (2020)
15. Fu, X., Chen, R., Jiang, Y., Ye, W., Li, M., & Chen, X. An Intelligent COVID-19 Diagnosis and Prediction System Based on WSN and Machine Learning. IEEE Sensors Journal, 20(23), 14155-1416 (2020)
16. Eric Blancaflor, Novi Dacalano, Masayuki Suzuki, Christer John Esguerra, Reiniel Albert Potestades, and Timothy Mejia. Distansya: An IoT Platform to Physical Distancing Provision Notification. In 2021 4th International Conference on Computing and Big Data (ICCBD 2021). Association for Computing Machinery, New York, NY, USA, 142–148.
https://doi.org/10.1145/3507524.3507547 (2022)
17. M. Fazio, A. Buzachis, A. Galletta, A. Celesti and M. Villari, "A proximity-based indoor navigation system tackling the COVID-19 social distancing measures," 2020 IEEE Symposium on Computers and Communications (ISCC), Rennes, France, pp. 1-6, doi:
10.1109/ISCC50000.2020.9219634 (2020)
18. Nadikattu, Rahul Reddy and Nadikattu, Rahul Reddy and Mohammad, Sikender Mohsienuddin and Whig, Pawan, Novel Economical Social Distancing Smart Device for COVID-19. International Journal of Electrical Engineering and Technology (IJEET), 2020, Available at SSRN: https://ssrn.com/abstract=3640230 (2020)
19. Y. Verbelen, S. Kaluvan, U. Haller, M. Boardman and T. B. Scott, "Design and Implementation of a Social Distancing and Contact Tracing Wearable," 2020 6th IEEE Congress on Information Science and Technology (CiSt), Agadir - Essaouira, Morocco, pp.
466-471, doi: 10.1109/CiSt49399.2021.9357203 (2020)
20. Alhmiedat, T.; Aborokbah, M. Social Distance Monitoring Approach Using Wearable Smart Tags. Electronics, 10, 2435. https://doi.org/10.3390/electronics10192435 (2021)
21. André Oliveira Cunha, Joana Venturin Loureiro, and Rodrigo Laiola Guimarães. Design and Development of a Wearable Device for Monitoring Social Distance using Received Signal Strength Indicator. In Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia '20). Association for Computing Machinery, New York, NY, USA, 57–60.
https://doi.org/10.1145/3428658.3431095 (2020)
22. Henry Eric Kapalamula, Justice Stanley Mlatho, Paul Stone Macheso, Design and implementation of a Social Distance Vest for Covid19 prevention (SODIV-COP), International Journal of Intelligent Networks, Volume 3, Pages 113-118, ISSN 2666-6030, https://doi.org/10.1016/j.ijin.2022.08.003 (2022)
23. M. D. Furqan, A. Achmad, Wardi and M. Niswar, "IoT and AI-enabled Physical Distance Monitoring Application to Prevent COVID19 Transmission," 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), Malang, Indonesia, pp. 117-120, doi: 10.1109/CyberneticsCom55287.2022.9865290 (2022) 24. Internet of Things with ESP32. http://esp32.net/
25. Bluetooth Low-Energy nrf51822 https://www.nordicsemi.com/products/nrf51822
