A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment
journal contributionposted on 23.08.2013 by Miao Yu, Adel Rhuma, Mohsen Naqvi, Liang Wang, Jonathon Chambers
Any type of content formally published in an academic journal, usually following a peer-review process.
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