Abstract
The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.
Highlights
W ITH the threat of COVID-19, a highly infectious virus, maintaining social distance is an effective way to prevent infection
The risk of COVID-19 transmission increases if an uninfected person is less than 6 ft from an infected person for longer than 15 minutes ( called Too Close for Too Long (TCTL))
In this paper we have presented research, development, and comparative analysis of classical estimation theory methods, which enables faster computation in a more logically explainable manner and novel hybrid model-based Machine Learning (ML) approaches for proximity distance estimation using the Received Signal Strength Indicator (RSSI) information radiated from the broadcast channels of the Bluetooth Low-Energy (BLE)
Summary
W ITH the threat of COVID-19, a highly infectious virus, maintaining social distance is an effective way to prevent infection. Energy (BLE) signal has attracted significant attention This led Massachusetts Institute of Technology (MIT), Boston, MA, to lead the Private Automated Contact Tracing (PACT) consortium [1] to make available several high quality BLE Received Signal Strength Indicator (RSSI) datasets, which were gathered in a variety of proximity scenarios. Their goal was to challenge research and development community to discover a solution to this timely and important problem. We present the results of our extensive comparative performance evaluation of classical estimation theory and Machine Learning (ML) algorithms for social distance estimation using the BLE RSSI data. We are planning to extend BLE by incorporating other opportunistic wireless signals including those from Wi-Fi and Ultra-wideband devices to increase the precision of range estimation
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