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

Khan, Muhammed Salman and Yu, Miao and Feng, Pengming and Wang, Liang and Chambers, Jonathon (2015) An unsupervised acoustic fall detection system using source separation for sound interference suppression. Signal Processing, 110 . pp. 199-210. ISSN 0165-1684

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

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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.

Keywords:Health care, fall detection, unsupervised classi?cation, source separation, JCOpen
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G120 Applied Mathematics
Divisions:College of Science > School of Computer Science
ID Code:26779
Deposited On:22 Mar 2017 15:14

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