Contact Tracing with Smartphone Sensors
The scenario
Understanding when people have been near one another can help researchers study how infectious diseases spread. Digital traces from mobile devices offer a way to estimate these encounters and could support personalised exposure alerts. This research explores how smartphone sensors can detect proximity without needing to know each person's exact location.
My approach
A smartphone can act as a proxy for its owner when studying proximity. Its sensors record aspects of the surrounding environment, allowing us to investigate whether two devices were in the same place at the same time.
Our approaches compare time-stamped sensor readings from different phones. Similar readings can provide evidence of co-location, although they do not by themselves prove that the owners were close enough for infection to occur.
In 2014, we investigated the use of outdoor WiFi signals from public access points to identify encounters retrospectively. The method does not require a map or precise location coordinates. A key challenge is that different phones can report different signal strengths and available networks, even at the same position, because their antennas have different sensitivities. The video below demonstrates our matching-rate measure.
In 2017, we explored another source of evidence: the smartphone magnetometer. Metal structures, reinforced concrete and electrical wiring distort the Earth's magnetic field, creating distinctive patterns that vary from place to place, as shown in the figure.
Passengers travelling together experience similar sequences of these magnetic variations. Journeys between consecutive stops, sometimes lasting up to ten minutes, provide a window in which to compare their phones' readings and assess whether they are travelling together.
The videos below show our system being evaluated over 150 kilometres of travel across London, including Overground trains, Underground trains and buses.
Publications
For further details, see the publications below.1) Nguyen, Khuong An, Zhiyuan Luo, and Chris Watkins. "On the feasibility of using two mobile phones and WLAN signal to detect co-location of two users for epidemic prediction." In Progress in Location-Based Services 2014, pp. 63-78. Springer, 2015.
2) Nguyen, Khuong An, Chris Watkins, and Zhiyuan Luo. "Co-location epidemic tracking on London public transports using low power mobile magnetometer." In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-8. IEEE, 2017.
3) Nguyen, Khuong An, Raja Naeem Akram, Konstantinos Markantonakis, Zhiyuan Luo, and Chris Watkins. "Location Tracking Using Smartphone Accelerometer and Magnetometer Traces." In Proceedings of the 14th International Conference on Availability, Reliability and Security (ARES), pp. 1-9. 2019.
4) Nguyen, Khuong An, and Zhiyuan Luo. "Cover your cough: detection of respiratory events with confidence using a smartwatch." In Conformal and Probabilistic Prediction and Applications, pp. 114-131. 2018.