A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment
journal contributionposted on 2013-08-23, 13:49 authored by Miao Yu, Adel Rhuma, Mohsen Naqvi, Liang Wang, Jonathon Chambers
We propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.
- Mechanical, Electrical and Manufacturing Engineering
CitationYU, M. ... et al., 2012. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Transactions on Information Technology in Biomedicine, 16 (6), pp. 1274 - 1286.
- AM (Accepted Manuscript)
NotesThis article was published in the IEEE Transactions on Information Technology in Biomedicine [© IEEE] and the definitive version is available at: http://dx.doi.org/10.1109/TITB.2012.2214786