2134/13018 Miao Yu Miao Yu Adel Rhuma Adel Rhuma Mohsen Naqvi Mohsen Naqvi Liang Wang Liang Wang Jonathon Chambers Jonathon Chambers A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment Loughborough University 2013 Assistive living Directed acyclic graph support vector machine (DAGSVM) system integration Fall detection Health care Multiclass classification Mechanical Engineering not elsewhere classified 2013-08-23 13:49:48 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/A_posture_recognition-based_fall_detection_system_for_monitoring_an_elderly_person_in_a_smart_home_environment/9573986 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.