© 2015 by Binary Information Press. 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.
All Science Journal Classification (ASJC) codes
- Building and Construction
- Mechanical Engineering
- Management, Monitoring, Policy and Law
Kong, W., Saad, M. H. M., Zulkifley, M. A., Hannan, M. A., & Hussain, A. (2015). Geometrical gait based model for fall detection using thresholding. Journal of Information and Computational Science, 6693-6700. https://doi.org/10.12733/jics20106973