@inproceedings{discovery10120445,
       booktitle = {Proceedings of the 2021 IEEE Radar Conference (RadarConf '21)},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Atlanta, GA, USA},
          volume = {2021},
            year = {2021},
           title = {Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring},
       publisher = {IEEE},
           month = {May},
          series = {IEEE Radar Conference (RadarConf)},
        keywords = {Passive WiFi Sensing, micro-Dopplers, activity recognition, deep learning, simulator},
             url = {https://ewh.ieee.org/conf/radar/2021/},
          author = {Tang, C and Vishwakarma, S and Li, W and Adve, R and Julier, S and Chetty, K},
        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.}
}