Doppler Power Characteristics Obtained from Calibrated Channel State Information for Human Activity Recognition

This paper demonstrates the time-variant (TV) Doppler power characteristics of human activities using measured channel state information (CSI). We model the measured CSI as a complex channel transfer function corresponding to a 3D non-stationary multipath fading channel consisting of a fixed transmitter, a fixed receiver, fixed scatterers representing fixed objects, and a cluster of moving scatterers representing a moving person performing some human activities. We demonstrate the relationship between the TV Doppler frequency caused by each moving scatterer and the rate of change of its corresponding TV propagation delay. Furthermore, we express the TV mean Doppler shift in terms of the path gains of the fixed scatterers, the TV path gains, and the TV Doppler frequencies of the moving scatterers. To provide an insight into the TV Doppler power characteristics of the measured calibrated CSI, we employ the spectrogram from which we derive the TV mean Doppler shift. Finally, we present the spectrograms and the TV mean Doppler shifts of the measured calibrated CSI for different human activities. The results show the possibility of designing human activity recognition systems using commercial Wi-Fi devices by employing deep learning or machine learning algorithms.