Augmenting experimental data with simulations to improve activity classification in healthcare monitoring

TY - GEN

T1 - Augmenting experimental data with simulations to improve activity classification in healthcare monitoring

AU - Tang, Chong

AU - Vishwakarma, Shelly

AU - Li, Wenda

AU - Adve, Raviraj

AU - Julier, Simon

AU - Chetty, Kevin

PY - 2021/5/7

Y1 - 2021/5/7

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 micro-Doppler 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.

AB - 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 micro-Doppler 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.

U2 - 10.1109/radarconf2147009.2021.9455314

DO - 10.1109/radarconf2147009.2021.9455314

M3 - Conference contribution

BT - 2021 IEEE Radar Conference (RadarConf21)

PB - IEEE

T2 - 2021 IEEE Radar Conference

Y2 - 8 May 2021 through 14 May 2021

ER -