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Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring

Tang, C; Vishwakarma, S; Li, W; Adve, R; Julier, S; Chetty, K; (2021) Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring. In: Proceedings of the 2021 IEEE Radar Conference (RadarConf '21). IEEE: Atlanta, GA, USA. (In press). Green open access

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Abstract

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human microDoppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research.

Type: Proceedings paper
Title: Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring
Event: 2021 IEEE Radar Conference (RadarConf '21)
Location: Atlanta, Georgia. US
Dates: 10 May 2021 - 14 May 2021
Open access status: An open access version is available from UCL Discovery
Publisher version: https://ewh.ieee.org/conf/radar/2021/
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.
Keywords: Passive WiFi Sensing, micro-Dopplers, activity recognition, deep learning, simulator
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
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 Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10120445
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