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http://dx.doi.org/10.5573/ieek.2013.50.8.232

Video Based Fall Detection Algorithm Using Hidden Markov Model  

Kim, Nam Ho (Dongyang Mirae University)
Yu, Yun Seop (Hankyong National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.8, 2013 , pp. 232-237 More about this Journal
Abstract
A newly developed fall detection algorithm using the HMM (Hidden Markov Model) extracted from the video is introduced. To distinguish between the fall from personal difference fall pattern or the normal activities of daily living (ADL), HMM machine learning algorithm is used. For getting fall feature vector of video, the motion vector from the optical flow is applied to the PCA (Principal Component Analysis). The combination of the angle, ratio of long-short axis, velocity from results of PCA make the new fall feature parameters. These parameters were applied to the HMM and the results were compared and analyzed. Among the newly proposed various kinds of fall parameters, the angle of movement showed the best results. The results show that this parameter can distinguish various types of fall from ADLs with 91.5% sensitivity and 88.01% specificity.
Keywords
fall detection; optical flow; PCA; Hidden Markov Model; fall feature parameters;
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