Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body
- Resource Type
- Authors
- H. M. Powell; Suad Albawendi; Kofi Appiah; Ahmad Lotfi; Caroline Langensiepen
- Source
- IEEE Access, Vol 6, Pp 70272-70282 (2018)
- Subject
- General Computer Science
Computer science
Feature extraction
02 engineering and technology
image motion analysis
01 natural sciences
Motion (physics)
Silhouette
Minimum bounding box
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Projection (set theory)
ambient assisted living
business.industry
010401 analytical chemistry
General Engineering
Pattern recognition
0104 chemical sciences
machine learning
smart homes
Ambient intelligence
020201 artificial intelligence & image processing
Artificial intelligence
identification of persons
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
- Language
- English
- ISSN
- 2169-3536
Falls are one of the greatest risks for older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body. The proposed approach employs a new set of features to detect a fall. Motion information of a segmented silhouette when extracted can provide a useful cue for classifying different behaviours, while variation in shape and the projection histogram can be used to describe human body postures and subsequent fall events. The proposed approach presented here extracts motion information using best-fit approximated ellipse and bounding box around the human body, produces projection histograms and determines the head position over time, to generate 10 features to identify falls. These features are fed into a multilayer perceptron neural network for fall classification. Experimental results show the reliability of the proposed approach with a high fall detection rate of 99.60% and a low false alarm rate of 2.62% when tested with the UR Fall Detection dataset. Comparisons with state of the art fall detection techniques show the robustness of the proposed approach.