  This work presents a simulation framework to generate human micro-Dopplers in
WiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint
WiFi transmissions using MATLAB's WLAN toolbox and human animation models
derived from a marker-based motion capture system. We integrate WiFi
transmission signals with the human animation data to generate the
micro-Doppler features that incorporate the diversity of human motion
characteristics, and the sensor parameters. In this paper, we consider five
human activities. We uniformly benchmark the classification performance of
multiple machine learning and deep learning models against a common dataset.
Further, we validate the classification performance using the real radar data
captured simultaneously with the motion capture system. We present experimental
results using simulations and measurements demonstrating good classification
accuracy of $\geq$ 95\% and $\approx$ 90\%, respectively.
