Cellular Data Analytics for Detection and Discrimination of Body Movements

In this paper, we show the possibility of using the smartphone built-in cellular radio modem to track sudden changes in the environment around it, thus turning the cellphone into a radio-frequency (RF) virtual sensor. In particular, we demonstrate how to isolate anomalous RF patterns by applying time series modeling and analysis of downlink multi-cell radio signals. These RF anomalies may indicate a situation change, namely, a body or object(s), movement in the surrounding of the smart-phone. Unlike Wi-Fi and Bluetooth devices, that can be turned on and off according to the user demands, cellular radios are never really disconnected. Even in idle mode, they carry out continuous and autonomous measurements of the radio channel conditions, namely, the cellular signal quality (CSQ). This is performed in agreement with standardized cell reselection procedures. Body movements or scene changes in general in the surroundings of a cellular device are responsible for small CSQ fluctuations that can be isolated from normal network operations and classified accordingly. The validation of this unconventional RF sensing method is based on extensive measurement campaigns covering a period of one month, using up to four commercial off-the-shelf smartphones. As a practical application case study, we developed a real-time demonstrator that is able to detect body proximity events close to the device and discriminate other body-induced environmental changes in the surrounding of the smartphone. Usage of data analytics tools for passive sensing from cellular signals is a novel topic that shows great potential as paving the way to new applications and research opportunities.