Title :
Detection of multi-occupancy using device-free passive localisation
Author :
Deak, Gabriel ; Curran, Kevin ; Condell, Joan ; Deak, Daniel
Author_Institution :
Comput. & Intell. Syst., Univ. of Ulster, Derry, UK
Abstract :
Indoor device-free passive localisation (DfPL) technology uses a received signal strength indication (RSSI)-based method to record variances of a measured signal where a person being tracked is not carrying any electronic device that can be used to estimate the location. The system monitors the changes in the RSSI measurements caused by the presence of a human body in an indoor environment. For example, it is known that the resonance frequency of water is 2.4 GHz and the human body contains >70% water. Thus, the human body attenuates the wireless signal reacting as an absorber. Wireless communication signal strengths between a number of nodes, using IEEE 802.11 or 802.15.4 standards, show that communication links covering distinct areas cannot be affected simultaneously by only one person. Thus, the authors have deployed a novel system that can identify multi-occupants in an environment using patterns of motion from those monitored areas. A pattern recognition neural network was used to identify two people in the environment. No other work based on the DfPL technique has focused on multi-occupancy.
Keywords :
IEEE standards; indoor radio; neural nets; object detection; object tracking; personal area networks; radio links; sensor placement; wireless LAN; IEEE 802.11 standards; IEEE 802.15.4 standards; RSSI measurement; communication links; device free passive localisation; indoor DfPL technique; indoor environment; location estimation; motion patterns; multioccupancy detection; pattern recognition neural network; people identification; person tracking; received signal strength indication; wireless communication signal strength;
Journal_Title :
Wireless Sensor Systems, IET
DOI :
10.1049/iet-wss.2013.0031