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Title: An unsupervised acoustic fall detection system using source separation for sound interference suppression
Authors: Khan, Muhammad Salman
Yu, Miao
Feng, Pengming
Wang, Liang
Chambers, Jonathon
Keywords: Health care
Fall detection
Unsupervised classification
Source separation
Mel-frequency cepstral coefficient
One class support vector machine
Issue Date: 2015
Publisher: Crown Copyright © Published by Elsevier B.V.
Citation: KHAN, M.S. ... et al, 2015. An unsupervised acoustic fall detection system using source separation for sound interference suppression. Signal Processing, 110, pp.199-210.
Abstract: We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
Sponsor: M.S. Khan acknowledges the financial support of UET, Peshawar, the Higher Education Commission (HEC) of Pakistan and Engineering and Physical Sciences Research Council (EPSRC), [Grant no. EP/K014307/1].
Version: Published
DOI: 10.1016/j.sigpro.2014.08.021
URI: https://dspace.lboro.ac.uk/2134/16531
Publisher Link: http://dx.doi.org/10.1016/j.sigpro.2014.08.021
ISSN: 0165-1684
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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