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

Perera, A; Arukgoda, J; Ranasinghe, R; Dissanayake, G

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

Perera, A Arukgoda, J Ranasinghe, R

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017, 2017, 2017-January pp. 1 - 8
Issue Date:: 2017-11-20

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Field	Value	Language
dc.contributor.author	Perera, A	en_US
dc.contributor.author	Arukgoda, J	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2017-11-20	en_US
dc.identifier.citation	2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017, 2017, 2017-January pp. 1 - 8	en_US
dc.identifier.isbn	9781509062980	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121705
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017	en_US
dc.relation.isbasedon	10.1109/IPIN.2017.8115936	en_US
dc.title	Localization system for carers to track elderly people in visits to a crowded shopping mall	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-January	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2017-January	en_US

Abstract:

© 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121705