A monocular view-invariant fall detection system for the elderly in assisted home environments

There is an increasing interest in real-time fall detection systems for the elderly in developed countries because more and more elderly are staying alone. There is a great demand for such fall detections systems in the smart home industry and the healthcare industry. Various fall detection approach...

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Main Authors:	Htike@Muhammad Yusof, Zaw Zaw, Egerton, Simon, Kuang, Ye Chow
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	AI Indexes (General)
Online Access:	http://irep.iium.edu.my/43199/ http://irep.iium.edu.my/43199/ http://irep.iium.edu.my/43199/1/IE_2011.PDF

id	iium-43199
recordtype	eprints
spelling	iium-431992015-06-08T03:29:47Z http://irep.iium.edu.my/43199/ A monocular view-invariant fall detection system for the elderly in assisted home environments Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow AI Indexes (General) There is an increasing interest in real-time fall detection systems for the elderly in developed countries because more and more elderly are staying alone. There is a great demand for such fall detections systems in the smart home industry and the healthcare industry. Various fall detection approaches have been proposed recently by researchers. However, the majority of the proposed approaches require sensors to be attached on the subjects under surveillance. Sensors are intrusive and restrictive. Moreover, critical situations can often go undetected if the elderly forget to wear those vital sensors. As a result, researchers have recently gained interest in computer vision based solutions. Viewpoint invariance is a very important issue in computer vision because camera position is arbitrary and the subjects are free to move around in the environment. This paper presents a vision-based framework that can detect falls using a single camera, irrespective of the viewpoint of the camera with respect to the subjects The proposed system makes use of invariant pose models which perform view-invariant human pose recognition. An ensemble of pose models performs inference on each video frame. Each pose model employs an expectation-maximization algorithm to estimate the probability that the given frame contains the corresponding pose. Over a sequence of frames, all the pose models collectively produce a multivariate time series. The system detects falls by analyzing the time series. We utilize the fuzzy hidden Markov model (FHMM) to detect falls. We have performed some experiments on two datasets and the results are found to be promising. 2011-06-25 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/43199/1/IE_2011.PDF Htike@Muhammad Yusof, Zaw Zaw and Egerton, Simon and Kuang, Ye Chow (2011) A monocular view-invariant fall detection system for the elderly in assisted home environments. In: 7th International Conference on Intelligent Environments (IE), 25-28 July 2011, Notthingham, UK. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6063363
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	AI Indexes (General)
spellingShingle	AI Indexes (General) Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow A monocular view-invariant fall detection system for the elderly in assisted home environments
description	There is an increasing interest in real-time fall detection systems for the elderly in developed countries because more and more elderly are staying alone. There is a great demand for such fall detections systems in the smart home industry and the healthcare industry. Various fall detection approaches have been proposed recently by researchers. However, the majority of the proposed approaches require sensors to be attached on the subjects under surveillance. Sensors are intrusive and restrictive. Moreover, critical situations can often go undetected if the elderly forget to wear those vital sensors. As a result, researchers have recently gained interest in computer vision based solutions. Viewpoint invariance is a very important issue in computer vision because camera position is arbitrary and the subjects are free to move around in the environment. This paper presents a vision-based framework that can detect falls using a single camera, irrespective of the viewpoint of the camera with respect to the subjects The proposed system makes use of invariant pose models which perform view-invariant human pose recognition. An ensemble of pose models performs inference on each video frame. Each pose model employs an expectation-maximization algorithm to estimate the probability that the given frame contains the corresponding pose. Over a sequence of frames, all the pose models collectively produce a multivariate time series. The system detects falls by analyzing the time series. We utilize the fuzzy hidden Markov model (FHMM) to detect falls. We have performed some experiments on two datasets and the results are found to be promising.
format	Conference or Workshop Item
author	Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow
author_facet	Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow
author_sort	Htike@Muhammad Yusof, Zaw Zaw
title	A monocular view-invariant fall detection system for the elderly in assisted home environments
title_short	A monocular view-invariant fall detection system for the elderly in assisted home environments
title_full	A monocular view-invariant fall detection system for the elderly in assisted home environments
title_fullStr	A monocular view-invariant fall detection system for the elderly in assisted home environments
title_full_unstemmed	A monocular view-invariant fall detection system for the elderly in assisted home environments
title_sort	monocular view-invariant fall detection system for the elderly in assisted home environments
publishDate	2011
url	http://irep.iium.edu.my/43199/ http://irep.iium.edu.my/43199/ http://irep.iium.edu.my/43199/1/IE_2011.PDF
first_indexed	2023-09-18T21:01:33Z
last_indexed	2023-09-18T21:01:33Z
_version_	1777410657740652544

A monocular view-invariant fall detection system for the elderly in assisted home environments

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