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Falling person detection using multisensor signal processing

Authors:

B. Ugur Toreyin,

E. Birey Soyer,

Ibrahim Onaran,

A. Enis CetinAuthors Info & Claims

EURASIP Journal on Advances in Signal Processing, Volume 2008

Article No.: 29, Page 29

https://doi.org/10.1155/2008/149304

Published: 01 January 2008 Publication History

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Abstract

Falls are of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR), and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR, and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov models (HMM) are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.

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Cited By

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Droghini DFerretti DPrincipi ESquartini SPiazza F(2017)A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic FeaturesComputational Intelligence and Neuroscience10.1155/2017/15126702017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/1512670
Principi EDroghini DSquartini SOlivetti PPiazza F(2016)Acoustic cues from the floorExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.04.00760:C(51-61)Online publication date: 30-Oct-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.04.007

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Falling person detection using multisensor signal processing

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cover image EURASIP Journal on Advances in Signal Processing

EURASIP Journal on Advances in Signal Processing Volume 2008, Issue

January 2008

2311 pages

ISSN:1110-8657

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 January 2008

Accepted: 12 September 2007

Received: 28 February 2007

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Droghini DFerretti DPrincipi ESquartini SPiazza F(2017)A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic FeaturesComputational Intelligence and Neuroscience10.1155/2017/15126702017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/1512670
Principi EDroghini DSquartini SOlivetti PPiazza F(2016)Acoustic cues from the floorExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.04.00760:C(51-61)Online publication date: 30-Oct-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.04.007

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Fall detection and risk of falling assessment with wearable sensors

Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

A study on a fall detection monitoring system for falling elderly using open source hardware

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