Devansh Agrawal

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Mind The Gap: Real-time Decentralized Distance Estimation using Ultrasound and Bluetooth across Multiple Smartphones

Devansh Agrawal, Peter Lyon, Martin Frobisher, Andy Doherty, Ben Allen, Freddie Rawlins
ArXiv
2020
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@article{agrawal2020mind,
  title={Mind The Gap: Real-time Decentralized Distance Estimation using Ultrasound and Bluetooth across Multiple Smartphones},
  author={Agrawal, Devansh R and Lyon, Peter and Frobisher, Martin and Doherty, Andy and Allen, Ben and Rawlins, Freddie},
  journal={arXiv preprint arXiv:2008.13564},
  year={2020}
}

Robust, low-cost solutions are needed to maintain social distancing guidelines during the COVID-19 pandemic. We establish a method to measure the distance between multiple phones across a large number of closely spaced smartphones with a median absolute error of 8.5 cm. The application works in real-time, using Time of Flight of near-ultrasound signals, providing alerts with sufficient responsiveness to be useful for distancing while devices are in users pockets and they are moving at walking speed. The approach is decentralized, requires no additional hardware, and can operate in the background without an internet connection. We have no device specific requirements nor need any manual calibration or device synchronization. It has been tested with over 20 different phones models, from both the Android and iOS systems in the past 5 years. To the best of our knowledge, this is the first successful such implementation, and has 25000 users at time of publishing.

Abstract

Robust, low-cost solutions are needed to maintain social distancing guidelines during the COVID-19 pandemic. We establish a method to measure the distance between multiple phones across a large number of closely spaced smartphones with a median absolute error of 8.5 cm. The application works in real-time, using Time of Flight of near-ultrasound signals, providing alerts with sufficient responsiveness to be useful for distancing while devices are in users pockets and they are moving at walking speed. The approach is decentralized, requires no additional hardware, and can operate in the background without an internet connection. We have no device specific requirements nor need any manual calibration or device synchronization. It has been tested with over 20 different phones models, from both the Android and iOS systems in the past 5 years. To the best of our knowledge, this is the first successful such implementation, and has 25000 users at time of publishing.

Design and source code modified from Jon Barron's website. Edit here.