Geometrical Gait Based Model for Fall Detection Using Thresholding

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外文摘要：This paper describes a method for real-time detection of human fall for video surveillance application. The proposed algorithm utilizes gait information in judging the fall incident. Gait refers to the pattern of human walking. Therefore, a fall is defined whenever there is a variation from the normal gait parameters. The detection algorithm is divided into three stages: human gait modeling, feature extraction and fall classification. Point Distribution Model (PDM) is employed to fit a skeleton model from a training set of data on the extracted human body contour. Then, the gait features are derived from the skeleton. The detection performance relies on the threshold values, which differ according to gait pattern. The results demonstrate that the gait analysis was able to detect fall incident accurately.

外文关键词：

Fall DetectionGeometrical ModelThresholding

作者：

Win Kong、Mohamad Hanif Md Saad、Mohd Asyraf Zulkifley、M A Hannan、Aini Hussain

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作者单位：

Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

Department of Mechanical and Material Engineering, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

出版年：

2015

DOI：

10.12733/jics20106973

Journal of information and computational science

ISSN：1548-7741

年,卷(期)：2015.12(18)