International Journal of Computational Intelligence Systems

Volume 6, Issue 1, January 2013, Pages 127 - 136

DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

Authors
Hamideh Kerdegari, Khairulmizam Samsudin, Abdul Rahman Ramli, Saeid Mokaram
Corresponding Author
Hamideh Kerdegari
Received 2 August 2012, Accepted 4 September 2012, Available Online 2 January 2013.
DOI
https://doi.org/10.1080/18756891.2013.761769How to use a DOI?
Keywords
Wearable fall detection system, Tri-axial accelerometer, Classification, Multilayer perceptron
Abstract

This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL) were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP) neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 1
Pages
127 - 136
Publication Date
2013/01/02
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.761769How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hamideh Kerdegari
AU  - Khairulmizam Samsudin
AU  - Abdul Rahman Ramli
AU  - Saeid Mokaram
PY  - 2013
DA  - 2013/01/02
TI  - DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK
JO  - International Journal of Computational Intelligence Systems
SP  - 127
EP  - 136
VL  - 6
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.761769
DO  - https://doi.org/10.1080/18756891.2013.761769
ID  - Kerdegari2013
ER  -