Non-Invasive Localization Using Software-Defined Radios

• Published in: IEEE sensors journal Vol. 22; no. 9; pp. 9018 - 9026
• Main Authors: Khan, Muhammad Zakir, Taha, Ahmad, Taylor, William, Imran, Muhammad Ali, Abbasi, Qammer H.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Classification algorithms; Health care; Human activity detection; Human motion; Human performance; Identification methods; indoor positioning; localisation; Localization; Monitoring; occupancy monitoring; OFDM; Performance evaluation; Radio frequency; Radio waves; Radiofrequency identification; Radios; Sensors; Software radio; Wireless fidelity
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3160796

Non-invasive indoor human activity detection using radio waves has attracted the interest of researchers, contributing to a range of new applications including smart healthcare. Localisation of activities can assist in developing advanced healthcare systems able to identify the location of patients. Radio frequencies have been shown in numerous studies as a non-invasive method to identify human activity. This is achieved by observing the signal propagation described in the Channel State Information (CSI). This paper presents experimental results using Universal Software-Defined Radio Peripheral (USRP) devices to identify and localise a single human subject performing activities by utilizing the CSI of radio frequencies. The experiments are carried out to retrieve CSI samples observing a single subject perform no-activity, sitting, standing, and leaning forward actions in various positions in a room. Additional CSI is captured for the subject walking in two directions across the observed area. Giving a total of 6 activities spanning the monitored area. CSI is also collected while the monitored area is empty for further comparison. Artificial intelligence is used to make classifications on collected CSI. The proposed approach uses a Super Learner (SL) algorithm that can identify the location of different activities with 96% accuracy, outperforming existing benchmark approaches.