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

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...

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Bibliographic Details

Main Authors:	Muhammad Salman Khan, Miao Yu, Pengming Feng, Liang Wang, Jonathon Chambers
Format:	Default Article
Published:	2014
Subjects:	Mechanical engineering not elsewhere classified Health care Fall detection Unsupervised classification Source separation Mel-frequency cepstral coefficient One class support vector machine Mechanical Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/16531
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