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

Lookup NU author(s): Pengming Feng, Professor Jonathon Chambers

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.


Publication metadata

Author(s): Khan MS, Yu M, Feng P, Wang L, Chambers JA

Publication type: Article

Publication status: Published

Journal: Signal Processing

Year: 2014

Volume: 110

Pages: 199-210

Print publication date: 01/05/2015

Online publication date: 27/08/2014

Acceptance date: 12/08/2014

Date deposited: 31/07/2015

ISSN (print): 0165-1684

ISSN (electronic): 1872-7557

Publisher: Elsevier

URL: http://dx.doi.org/10.1016/j.sigpro.2014.08.021

DOI: 10.1016/j.sigpro.2014.08.021


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