Implantable Device Positioning based on Magnetic Field Detection with the Aid of Genetic Algorithm in Body Area Bio-sensor Networks



Shigeaki Ogose1, *, Shintaro Mori2, Takahiro Sekii1
1 Department of Electronics and Information Engineering, Faculty of Engineering, Kagawa University, 2217-20 Hayashicho, Takamatsu, Kagawa, Japan
2 Department of Electronics Engineering and Computer Science, Faculty of Engineering, Fukuoka University, Fukuoka, Japan

Abstract

Background:

Minimally invasive medical care by the aid of Information Communication Technology (ICT) has attracted considerable attention. Sensor nodes including implantable devices within the bio-sensor network or Body Area Network (BAN) have have been utilized to reduce the burden on patients. To control the operation of devices properly or collect vital data effectively, it is important to obtain accurate information on their position.

Methods:

This paper provides an effective positioning method based on detection of magnetic fields generated from implantable devices using the genetic algorithm (GA). After providing the principle of the proposed method, some laboratory test measurement results are given to confirm its effectiveness.

Results:

Magnetic field detection using multiple magnetic field sensors was achieved. From the results of multiple point measurements on the three-dimensional components of the magnetic field strength, the position of the target was obtained with a smaller error. Laboratory test measurement results are in good agreement with the theoretical values.

Conclusion:

The proposed positioning method is an effective and an economical approach. It is also effective for detecting moving devices such as a capsule endoscope in a human body. With the aid of the GA, high-speed detection is obtained with a low calculation costs. The compressed sensing method reduces the number of measurement points.

Keywords: : Body area networks, Genetic algorithm, Sensor networks, Positioning, Implantable devices, ICT, IoT.


Abstract Information


Identifiers and Pagination:

Year: 2018
Volume: 12
Publisher Item Identifier: EA-TOBEJ-2018-12

Article History:

Received Date: 31/8/2018
Revision Received Date: 8/12/2018
Acceptance Date: 10/12/2018
Electronic publication date: 31/12/2018
Collection year: 2018

© 2018 Ogose et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Electronics and Information Engineering, Faculty of Engineering, Kagawa University, 2217-20 Hayashicho, Takamatsu, Kagawa, Japan; Tel: +81-87-864-2210; E-mail: ogose@eng.kagawa-u.ac.jp