Publication:
RSSI-based positioning for health care service using artificial neural network approach

Issued Date

2015-01-01

Resource Type

Conference Paper

DOI

10.1109/BMEiCON.2014.7017419

Other identifier(s)

2-s2.0-84923010071

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

BMEiCON 2014 - 7th Biomedical Engineering International Conference. (2015)

Suggested Citation

Parinya Otarawanna, Warakorn Charoensuk RSSI-based positioning for health care service using artificial neural network approach. BMEiCON 2014 - 7th Biomedical Engineering International Conference. (2015). doi:10.1109/BMEiCON.2014.7017419 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/35913

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Title

RSSI-based positioning for health care service using artificial neural network approach

Author(s)

Parinya Otarawanna
 Warakorn Charoensuk

Other Contributor(s)

Mahidol University

Abstract

© 2014 IEEE. the fluctuation of received signal strength indicator emerges poor accuracy in healthcare location monitoring service. To enhance the capability of position classification, this paper represents positioning system based on ZigBee standard using artificial neural networks algorithm. Time-delay Multi-Layer Perceptron is proposed by using Levenberg-Marquardt optimization. For the result, the average error of four empirical experiments reaches to 7 centimeters with 1.5 square meters grid resolution. The reduction of grid scale in order to extend output resolution is also a limitation for RSSI-based positioning due to an uncertainly and ambiguity of RSSI vector.

Keyword(s)

Engineering

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/35913

Collections

Scopus 2011-2015