A Posture Recognition Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment
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.
IEEE Transactions on Information Technology in Biomedicine