@inproceedings{60a929d0dc8d4f449176e19a5077a279,
title = "RF Based Real Time Human Motion Sensing",
abstract = "Recent research has shown that the propagation of Radio Frequencies signals is affected by human movements taking place between the RF transmitter and receiver antennas. Artificial intelligence has been widely used to classify the patterns of signal propagation. With the help of a universal software radio peripheral device, a system was developed based on a real-time machine learning classification algorithm to ensure alerts of incidents are received in a timely manner. The machine learning model was built to distinguish between 'No Activity' and 'Movement' status of a single human subject. The model recorded a high classification accuracy of 97.8 \% which enabled an accurate classification of new data in real-time.",
keywords = "Channel State Infor-mation, Human motion detection, Machine Learning, RF signals, Real-time",
author = "William Taylor and Ahmad Taha and Kia Dashtipour and Shah, \{Syed Aziz\} and Abbasi, \{Qammer H.\} and Imran, \{Muhammad Ali\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 ; Conference date: 04-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/APS/URSI47566.2021.9703954",
language = "English",
series = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2044--2045",
booktitle = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",
address = "United States",
}