  This work presents an application of Integrated sensing and communication
(ISAC) system for monitoring human activities directly related to healthcare.
Real-time monitoring of humans can assist professionals in providing healthy
living enabling technologies to ensure the health, safety, and well-being of
people of all age groups. To enhance the human activity recognition performance
of the ISAC system, we propose to use synthetic data generated through our
human micro-Doppler simulator, SimHumalator to augment our limited measurement
data. We generate a more realistic micro-Doppler signature dataset using a
style-transfer neural network. The proposed network extracts environmental
effects such as noise, multipath, and occlusions effects directly from the
measurement data and transfers these features to our clean simulated
signatures. This results in more realistic-looking signatures qualitatively and
quantitatively. We use these enhanced signatures to augment our measurement
data and observe an improvement in the classification performance by 5%
compared to no augmentation case. Further, we benchmark the data augmentation
performance of the style transferred signatures with three other synthetic
datasets -- clean simulated spectrograms (no environmental effects), simulated
data with added AWGN noise, and simulated data with GAN generated noise. The
results indicate that style transferred simulated signatures well captures
environmental factors more than any other synthetic dataset.
