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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13017

Title: An online one class support vector machine based person-specific fall detection system for monitoring an elderly individual in a room environment
Authors: Yu, Miao
Yu, Yuanzhang
Rhuma, Adel
Naqvi, Syed M.R.
Wang, Liang
Chambers, Jonathon
Keywords: Health care
Assistive living
Fall detection
Online OCSVM
Posture detection
Issue Date: 2013
Publisher: © IEEE
Citation: YU, M. ... et al., 2013. An online one class support vector machine based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE Journal of Biomedical and Health Informatics, 17 (6), pp. 1002-1014.
Abstract: In this paper, we propose a novel computer vision based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person’s daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on data sets for 12 people, we confirm that our proposed person specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semi-unsupervised fall detection system from a system perspective because although an unsupervised type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step towards a complete unsupervised fall detection system.
Description: This article was published in the IEEE Journal of Biomedical and Health Informatics [© IEEE] and the definitive version is available at: http://dx.doi.org/10.1109/JBHI.2013.2274479 Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Version: Accepted for publication
DOI: 10.1109/JBHI.2013.2274479
URI: https://dspace.lboro.ac.uk/2134/13017
Publisher Link: http://dx.doi.org/10.1109/JBHI.2013.2274479
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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