Localization system for carers to track elderly people in visits to a crowded shopping mall

© 2017 IEEE. This work presents a real-time localization system developed for professional care givers to track residents of an aged care facility during their visits to a crowded, multi-story shopping mall. The proposed system consists of a Wi-Fi based self-localization platform integrated into a wheeled walking frame and an application installed in a hand-held tablet device for displaying the locations of walker users. The density of people in the shopping mall changes significantly during the day thus the expected Wi-Fi signal strength at a given location is subject to large variations. However, Identifying the location to be within a given area is adequate and the average speed of motion is less than 0.5 m/sec. In this paper, an algorithm that addresses these unique requirements is presented. We exploit the signal strength characteristics of existing Wi-Fi network and prior knowledge of the building floor plans for developing our core algorithm. The environments is divided in to cells that are either enclosed spaces or divisions of larger open regions. The probability density function of the Wi-Fi signal strength of each cell is estimated using Kernel Density Estimation (KDE) and is used in a probabilistic framework to estimate the user location. Motion model of the users as well as the detection of floor transition events are used to enhance the performance of the location estimator. The algorithm was implemented using an Odroid-C1 computer and a tablet with Android operating system. Results obtained during field trials at Roselands Shopping Mall in Sydney are presented