Abstract
In this study, the researchers developed 2 wearable hardware that utilizes Arduino Nano 33 BLE Sense as it’s microcontroller in the implementation of social distancing and contact tracing with an application and database. The application provides an indicator for risk status, notifications for social distancing if the two devices are within 100 cm of each other. The two devices were calibrated at a displacement value of 1 m and yielded a RSSI value of −69 dBm for device 1 and −67 dBm for device 2 respectively. The database also provides a manual override for the risk status that is displayed in the graphical user interface in the instances of human error.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hernandez-Orallo, E., Manzoni, P., Calafate, C.T., Cano, J.C.: Evaluating how smartphone contact tracing technology can reduce the spread of infectious diseases: the case of COVID-19. IEEE Access 8, 99083–99097 (2020). https://doi.org/10.1109/ACCESS.2020.2998042
Centers for Disease Control and Prevention. Coronavirus disease 2019: pregnancy and breastfeeding. https://www.cdc.gov/coronavirus/2019-ncov/prepare/pregnancy-breastfeeding.html.
Kajikawa, N., Minami, Y., Kohno, E., Kakuda, Y.: On availability and energy consumption of the fast connection establishment method by using bluetooth classic and bluetooth low energy. In: Proceedings - 2016 4th International Symposium Computing and Networking, CANDAR 2016, pp. 286–290 (2017). https://doi.org/10.1109/CANDAR.2016.76.
Dimaunahan, E.D., Cruz, J.C.D., Agasino, F.M.L., Gonda, N.Z.T., Isada, N.G.P., Tolentino, B.C.A.M.: An arduino-based self-sustaining buoy prototype for a non-contact infrared sensor system raw crude oil detector. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, pp. 2–6 (2019). https://doi.org/10.1109/HNICEM.2018.8666263
Shang, F., Su, W., Wang, Q., Gao, H., Fu, Q.: A location estimation algorithm based on RSSI vector similarity degree. Int. J. Distrib. Sensor Netw. 2014 (2014). https://doi.org/10.1155/2014/371350
Sese, J.T., et al.: Adaptation of the ITU-T E.161 international standard as mapping layout for a wearable text-input device for the blind. In: 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019, 590–591 (2019). https://doi.org/10.1109/GCCE46687.2019.9015434
Oguchi, K., Maruta, S., Hanawa, D.: Human positioning estimation method using received signal strength indicator (RSSI) in a wireless sensor network. Procedia Comput. Sci. 34, 126–132 (2014). https://doi.org/10.1016/j.procs.2014.07.066
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Balbin, J.J.R., Cruz, M.R.C., Juan, K.R.D.S. (2021). Distance Monitoring with Contact Tracing Logging System Using Received Signal Strength Indicator. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-90321-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90320-6
Online ISBN: 978-3-030-90321-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)