Appiah, Kofi and Hunter, Andrew and Waltham, Christopher (2011) Low-power and efficient ambient assistive care system for elders. In: IEEE Computer Vision and Pattern Recognition Workshop, 20-25th June 2011, Colorado Springs.

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Abstract

This paper presents a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. We build a probabilistic spatial map of resting locations using the head position of the subject, represented as cluster centres discovered by K-means in the camera view space. A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	This paper presents a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. We build a probabilistic spatial map of resting locations using the head position of the subject, represented as cluster centres discovered by K-means in the camera view space. A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.
Keywords:	embedded vision, low power, ambient intelligence
Subjects:	G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Kofi Appiah
Date Deposited:	08 Apr 2011 18:30
Last Modified:	12 Oct 2012 10:50
URI:	http://eprints.lincoln.ac.uk/id/eprint/4379

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