TY  - INPR
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
AV  - public
Y1  - 2021/05/17/
TI  - Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring
KW  - Passive WiFi Sensing
KW  -  micro-Dopplers
KW  -  activity recognition
KW  -  deep learning
KW  -  simulator
T3  - IEEE Radar Conference (RadarConf)
A1  - Tang, C
A1  - Vishwakarma, S
A1  - Li, W
A1  - Adve, R
A1  - Julier, S
A1  - Chetty, K
CY  - Atlanta, GA, USA
UR  - https://ewh.ieee.org/conf/radar/2021/
PB  - IEEE
ID  - discovery10120445
N2  - 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.
ER  -