Lookup NU author(s): Pengming Feng,
Professor Jonathon Chambers
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Khan MS, Yu M, Feng P, Wang L, Chambers JA
Publication type: Article
Publication status: Published
Journal: Signal Processing
Print publication date: 01/05/2015
Online publication date: 27/08/2014
Acceptance date: 12/08/2014
ISSN (print): 0165-1684
ISSN (electronic): 1872-7557
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