Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body

Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, Albawendi, S ORCID: https://orcid.org/0000-0002-3439-7777, Powell, H, Appiah, K ORCID: https://orcid.org/0000-0002-9480-0679 and Langensiepen, C ORCID: https://orcid.org/0000-0002-0165-9048, 2018. Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body. IEEE Access, 6, pp. 70272-70282. ISSN 2169-3536

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Abstract

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.

Item Type:	Journal article
Publication Title:	IEEE Access
Creators:	Lotfi, A., Albawendi, S., Powell, H., Appiah, K. and Langensiepen, C.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	2018
Volume:	6
ISSN:	2169-3536
Identifiers:	Number Type 10.1109/access.2018.2881237 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	21 Nov 2018 09:10
Last Modified:	22 Jan 2019 14:49
URI:	https://irep.ntu.ac.uk/id/eprint/35087

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