Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164964
Title: Interpersonal distance tracking with mmWave radar and IMUs
Authors: Dai, Yimin
Shuai, Xian
Tan, Rui
Xing, Guoliang
Keywords: Engineering::Computer science and engineering::Computer systems organization::Computer system implementation
Issue Date: 2023
Source: Dai, Y., Shuai, X., Tan, R. & Xing, G. (2023). Interpersonal distance tracking with mmWave radar and IMUs. 22nd International Conference on Information Processing in Sensor Networks (IPSN '23), 123-135. https://dx.doi.org/10.1145/3583120.3586958
Project: RG88/22 
Conference: 22nd International Conference on Information Processing in Sensor Networks (IPSN '23)
Abstract: Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach.
URI: https://hdl.handle.net/10356/164964
URL: https://dl.acm.org/doi/proceedings/10.1145/3583120
ISBN: 979-8-4007-0118-4
DOI: 10.1145/3583120.3586958
Schools: Interdisciplinary Graduate School (IGS) 
School of Computer Science and Engineering 
Rights: © 2023 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Conference Papers
SCSE Conference Papers

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