Title: Chameleon: personalised and adaptive fall detection of elderly people in home-based environments
Authors: Lingmei Ren; Weisong Shi
Addresses: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China ' Department of Computer Science, Wayne State University, Detroit 48202, MI, USA
Abstract: Threshold-based fall detection has been widely adopted in conventional fall detection systems. In this paper, we argue that a fixed threshold is not flexible enough for different people. By exploiting the personalised and adaptive threshold, we propose a novel threshold extraction model, which meets being adaptive to detect a fall, while only taking consideration of data from activity of daily living (ADL). We believe this is a solid step toward improving the performance of the threshold-based fall detection solution. Furthermore, we incorporate the proposed idea into Chameleon. To evaluate the performance of this threshold extraction model, we compared Chameleon with advanced magnitude detection (AMD) and fixed and tracking fall detection (FTFD). The results show Chameleon has an accuracy of 96.83% when detecting falls, which is 1.67% higher than FTFD and 2.67% higher than AMD. Meanwhile, the sensitivity and the specificity of Chameleon are also higher than the other two algorithms.
Keywords: accelerometers; fall detection; personalisation; elderly; old people; home falls; personalised threshold; adaptive threshold; threshold extraction; falling over; home care.
International Journal of Sensor Networks, 2016 Vol.20 No.3, pp.163 - 176
Received: 18 Oct 2014
Accepted: 28 Dec 2014
Published online: 17 Mar 2016 *