Document Detail

Posture Recognition Based Fall Detection System For Monitoring An Elderly Person In A Smart Home Environment.
MedLine Citation:
PMID:  22922730     Owner:  NLM     Status:  Publisher    
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.
M Yu; A Rhuma; S Naqvi; L Wang; J Chambers
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-8-22
Journal Detail:
Title:  IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society     Volume:  -     ISSN:  1558-0032     ISO Abbreviation:  IEEE Trans Inf Technol Biomed     Publication Date:  2012 Aug 
Date Detail:
Created Date:  2012-8-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9712259     Medline TA:  IEEE Trans Inf Technol Biomed     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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