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).
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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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