Human Activity Recognition and Prediction Based on Wi-Fi Channel State Information and Machine Learning

At present, we are moving into the era of the Internet of Things. In this new era, it will be easy to find access points (APs) wherever we go. Signals from these APs can be used for more than just connecting to the Internet. The presence of a human between two APs and the human's behavior cause a change in the waveform of a Wi-Fi signal. In this paper, we explain how changes in waveforms affect the channel state information of the signal and how machine learning can utilize that information to recognize and predict human behavior.