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.