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 presents fall detection by using the information from ellipse fitting and a projection histogram along the axes of the ellipse to distinguish different postures of the person.
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