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An unsupervised acoustic fall detection system using source separation for sound interference suppression

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journal contribution
posted on 22.12.2014 by Muhammad Salman Khan, Miao Yu, Pengming Feng, Liang Wang, Jonathon Chambers
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.

Funding

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].

History

School

  • Mechanical, Electrical and Manufacturing Engineering

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.

Publisher

Crown Copyright © Published by Elsevier B.V.

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Publication date

2015

Notes

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/

ISSN

0165-1684

Language

en

Licence

Exports