title: A posture recognition based fall detection system for monitoring an elderly person in a smart home environment.
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 post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) 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.
authors.forename: Miao
authors.initials: M
authors.lastname: Yu
authors.forename: Adel
authors.initials: A
authors.lastname: Rhuma
authors.forename: Syed Mohsen
authors.initials: SM
authors.lastname: Naqvi
authors.forename: Liang
authors.initials: L
authors.lastname: Wang
authors.forename: Jonathon
authors.initials: J
authors.lastname: Chambers
mesh_terms: D000058:Accidental Falls; D000367:Age Factors; D000368:Aged; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D011187:Posture / Q000502:physiology*; D060388:Support Vector Machine