Studies show that 25% to 47% of elderly will at least once experience falls and this figure is approximately 50% among the elderly living in nursing home. In this paper, based on the Gaussian Mixture Model (GMM) and estimating their parameters by Expectation Maximization (EM) algorithm, a new method has been proposed that firstly, the binary movement of the elderly is segmented from video sequences. Next, the occurrence of falls in older persons is done relying on anatomic body changes and Motion History Images (MHI). Elevation of the system performance was set up on a set of video frames received from the elderly residing in Mother Health Care Center in Sabzevar city and CAVIAR database containing the actual occurrence the of falling. Then, based on the standard deviation and the C-motion coefficient of the walking, suspected incident falls and actual falls are accurately segregated and finally, the sensitivity of 92.68% and the specificity of 96% were obtained which represent a desired capability of the output system. In overall, appropriate simulation of algorithms on the data set due to low error rate in which is less than 6% and meanwhile a careful monitoring of the elderly’s falls will be provided by implementing this system in elderly nursing and residential homes.
Rezaee, K., Haddadnia, J., & Delbari, A. (2014). Elderly Fall Monitoring System Based on Gaussian Mixture Models and Anatomical Changes in Video Sequences. Journal of Machine Vision and Image Processing, 1(2), 67-77.
MLA
Khosro Rezaee; Javad Haddadnia; Ahmad Delbari. "Elderly Fall Monitoring System Based on Gaussian Mixture Models and Anatomical Changes in Video Sequences". Journal of Machine Vision and Image Processing, 1, 2, 2014, 67-77.
HARVARD
Rezaee, K., Haddadnia, J., Delbari, A. (2014). 'Elderly Fall Monitoring System Based on Gaussian Mixture Models and Anatomical Changes in Video Sequences', Journal of Machine Vision and Image Processing, 1(2), pp. 67-77.
VANCOUVER
Rezaee, K., Haddadnia, J., Delbari, A. Elderly Fall Monitoring System Based on Gaussian Mixture Models and Anatomical Changes in Video Sequences. Journal of Machine Vision and Image Processing, 2014; 1(2): 67-77.
)