@inproceedings{10.1145/3568444.3568458,
author = {Yaghoubisharif, Negin and Getschmann, Christopher and Echtler, Florian},
title = {HeadsUp: Mobile Collision Warnings through Ultrasound Doppler Sensing},
year = {2022},
isbn = {9781450398206},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3568444.3568458},
doi = {10.1145/3568444.3568458},
abstract = {Smartphone-using pedestrians are often distracted, leading to frequent accidents of varying severity with a risk of both awkwardness and injury. We introduce HeadsUp, a mobile app designed to warn the user of imminent collisions with solid obstacles. HeadsUp runs on unmodified commodity smartphones without additional hardware and uses active ultrasound sensing based on the Doppler effect. We contribute an analysis of the ultrasound audio characteristics of six different smartphone models to verify the feasibility of our approach across vendors and device classes, and a description of two implementation variants of our signal processing pipeline. We evaluate our system both in a lab environment and under real-world conditions, and we conclude that HeadsUp can effectively work at a range of up to 3 meters, even though overall performance is heavily dependent on both the individual user and the environment characteristics.},
booktitle = {Proceedings of the 21st International Conference on Mobile and Ubiquitous Multimedia},
pages = {200–207},
numpages = {8},
keywords = {smartphones, environment, sensing, collision warning, Doppler, ultrasound},
location = {Lisbon, Portugal},
series = {MUM '22}
}