Abstract-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. Index Terms-Assistive living, directed acyclic graph support vector machine (DAGSVM) system integration, fall detection, health care, multiclass classification.
The work in REF proposed a computer vision-based fall detection system for monitoring an elderly person in a home care application.
A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Computer Science Medicine