eprintid: 10120445
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/12/04/45
datestamp: 2021-02-02 11:57:25
lastmod: 2021-09-20 22:14:26
status_changed: 2021-02-02 11:57:25
type: proceedings_section
metadata_visibility: show
creators_name: Tang, C
creators_name: Vishwakarma, S
creators_name: Li, W
creators_name: Adve, R
creators_name: Julier, S
creators_name: Chetty, K
title: Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
divisions: F52
keywords: Passive WiFi Sensing, micro-Dopplers, activity recognition, deep learning, simulator
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2021-05-17
date_type: published
publisher: IEEE
official_url: https://ewh.ieee.org/conf/radar/2021/
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1844644
lyricists_name: Chetty, Kevin
lyricists_name: Julier, Simon
lyricists_name: Li, Wenda
lyricists_name: Vishwakarma, Shelly
lyricists_id: KCHET45
lyricists_id: SJULI23
lyricists_id: WLIXX67
lyricists_id: SVISH21
actors_name: Chetty, Kevin
actors_id: KCHET45
actors_role: owner
full_text_status: public
series: IEEE Radar Conference (RadarConf)
volume: 2021
place_of_pub: Atlanta, GA, USA
event_title: 2021 IEEE Radar Conference (RadarConf '21)
event_location: Atlanta, Georgia. US
event_dates: 10 May 2021 - 14 May 2021
institution: IEEE Radar Conference
book_title: Proceedings of the 2021 IEEE Radar Conference (RadarConf '21)
citation:        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   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10120445/1/RadarConf2021_Final_Version.pdf