Volume 8, Number 4 (1-2014)                   Salmand 2014, 8(4): 15-23 | Back to browse issues page


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Rezaee K, Haddadnia J, Delbari A, Madanian M. Predicting and Monitoring of the Elderly Falls Based on Modeling of the Motion Patterns Obtained From Video Sequences. Salmand. 2014; 8 (4) :15-23
URL: http://salmandj.uswr.ac.ir/article-1-581-en.html

1- MSc. Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
2- PhD Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran. , Haddadnia@hsu.ac.ir
3- PhD Iranian Research Center on Aging,university of social welfare & rehabilitation sciences ,Tehran, faculty member of Medical University of Sabzevar
4- MsC Faculty of Engineering, IsfahanUniversity Isfahan, Iran.
Abstract:   (7770 Views)

Objectives: Many countries are faced with the growing population of the elderly each year and so designing an appropriate system for monitoring of various elderly states is necessity. Every year thousands of the elderly suffer serious damages such as articular fractures, broken bones and even death due to their fall

Methods & Materials: In this paper, based on the analysis of images taken from the elderly’s movement, an efficient system has been proposed that, in the first phase, simulates the movement of the elderly by detecting their abnormal walking. The, by combining several important features, including an estimate of body angle, representation of the motion and estimate of the magnitude and direction of movement, the speed of the falling is calculated. This system has been implemented on a set of 57425 video frames received from the elderly residing in Farzanegan Health Care Center in Mashhad and the video sequences containing the actual occurrence the of falling. All the sequences were randomly converted into four Movie categories with these details: AVI format, 120×160 pixels resolution and 15 fps.

Results: Simulation of algorithm distinguishes the proposed system from similar ones, particularly due to its intelligent monitoring and its real time detection of the elderly’s fall. The average accuracy (AAC), detection rate (DR) and insignificant false alarm rate (FAR) are 94%, 92.91% and 5.52% respectively in acceptable level. The 92% sensitivity and 94.47% specificity indicate the ability of the system in identifying the incidents similar to the fall.

Conclusion: Many advantages such as high speed in data processing, unique accuracy and sensitivity and time parsimony make a system has particular performance and implementation of it due to intelligent monitoring and Real-Time tracking of seniors in Health Care Center and houses.

Full-Text [PDF 1183 kb]   (1371 Downloads)    
Type of Study: Research | Subject: General
Received: 2013/07/20 | Accepted: 2013/11/10 | Published: 2014/01/01

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