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Classification between different activities in an indoor environment usingwireless signals is an emerging technology for various applications, includingintrusion detection, patient care, and smart home. Researchers have showndifferent methods to classify activities and their potential benefits byutilizing WiFi signals. In this paper, we analyze classification of movingobjects by employing machine learning on real data from a massivemulti-input-multi-output (MIMO) system in an indoor environment. We conductmeasurements for different activities in both line-of-sight and nonline-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. Wepropose algorithms to exploit amplitude and phase-based features classificationtask. For the considered setup, we benchmark the classification performance andshow that we can achieve up to 98% accuracy using real massive MIMO data, evenwith a small number of experiments. Furthermore, we demonstrate the gain inperformance results with a massive MIMO system as compared with that of alimited number of antennas such as in WiFi devices.

Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data