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. 2021 Oct 7;21(19):6667.
doi: 10.3390/s21196667.

Evaluating the Dynamics of Bluetooth Low Energy Based COVID-19 Risk Estimation for Educational Institutes

Abdulah Jeza Aljohani  1 , Junaid Shuja  2 , Waleed Alasmary  3 , Abdulaziz Alashaikh  4
Affiliations

Evaluating the Dynamics of Bluetooth Low Energy Based COVID-19 Risk Estimation for Educational Institutes

Abdulah Jeza Aljohani et al. Sensors (Basel). .

Abstract

COVID-19 tracing applications have been launched in several countries to track and control the spread of viruses. Such applications utilize Bluetooth Low Energy (BLE) transmissions, which are short range and can be used to determine infected and susceptible persons near an infected person. The COVID-19 risk estimation depends on an epidemic model for the virus behavior and Machine Learning (ML) model to classify the risk based on time series distance of the nodes that may be infected. The BLE technology enabled smartphones continuously transmit beacons and the distance is inferred from the received signal strength indicators (RSSI). The educational activities have shifted to online teaching modes due to the contagious nature of COVID-19. The government policy makers decide on education mode (online, hybrid, or physical) with little technological insight on actual risk estimates. In this study, we analyze BLE technology to debate the COVID-19 risks in university block and indoor class environments. We utilize a sigmoid based epidemic model with varying thresholds of distance to label contact data with high risk or low risk based on features such as contact duration. Further, we train multiple ML classifiers to classify a person into high risk or low risk based on labeled data of RSSI and distance. We analyze the accuracy of the ML classifiers in terms of F-score, receiver operating characteristic (ROC) curve, and confusion matrix. Lastly, we debate future research directions and limitations of this study. We complement the study with open source code so that it can be validated and further investigated.

Keywords: BLE; COVID-19; classification; epidemic model; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
BLE based contact tracing application operation.
Figure 2
Figure 2
The research methodology for ML based risk prediction on BLE data.
Figure 3
Figure 3
Percentage of high and low risk persons for varying thresholds: linear.
Figure 4
Figure 4
Percentage of high and low risk persons on varying thresholds: sigmoid.
Figure 5
Figure 5
Comparison of ML classifiers for in terms of ROC.
Figure 6
Figure 6
Comparison of ML classifiers in terms of F-score.
Figure 7
Figure 7
Comparison of ML classifiers for indoor dataset (confusion matrix).
Figure 8
Figure 8
Comparison of ML classifiers for outdoor dataset (confusion matrix).
See this image and copyright information in PMC

References

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