  Classification between different activities in an indoor environment using
wireless signals is an emerging technology for various applications, including
intrusion detection, patient care, and smart home. Researchers have shown
different methods to classify activities and their potential benefits by
utilizing WiFi signals. In this paper, we analyze classification of moving
objects by employing machine learning on real data from a massive
multi-input-multi-output (MIMO) system in an indoor environment. We conduct
measurements for different activities in both line-of-sight and non
line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We
propose algorithms to exploit amplitude and phase-based features classification
task. For the considered setup, we benchmark the classification performance and
show that we can achieve up to 98% accuracy using real massive MIMO data, even
with a small number of experiments. Furthermore, we demonstrate the gain in
performance results with a massive MIMO system as compared with that of a
limited number of antennas such as in WiFi devices.
