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Realistic Micro-Doppler Database Generation Through Neural Style Transfer Framework

Vishwakarma, Shelly; Chetty, Kevin; Li, wenda; (2022) Realistic Micro-Doppler Database Generation Through Neural Style Transfer Framework. Presented at: IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece. Green open access

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Abstract

Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance, we propose a style-transfer neural framework to generate realistic synthetic micro-Doppler signature dataset. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfers these features to the clean simulated signatures generated through our simulator called SimHumaLator. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case.

Type: Conference item (UNSPECIFIED)
Title: Realistic Micro-Doppler Database Generation Through Neural Style Transfer Framework
Event: IEEE-EMBS International Conference on Biomedical and Health Informatics
Location: Ioannina, Greece
Dates: 27 Sep 2022 - 30 Sep 2022
Open access status: An open access version is available from UCL Discovery
Publisher version: https://bhi-bsn-2022.org/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10153700
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