WBAN Radio Channel Characteristics Between the Endoscope Capsule and on-Body Antenna

This paper presents a study on the radio channel characteristics between an endoscope capsule and an on-body antenna in different parts of the small intestine with different on-body antenna location options. The study is conducted using finite integration technique based electromagnetic simulation software CST and one of its anatomical voxels. An endoscope capsule model with a dipole antenna is set inside different areas of the small intestine of the voxel model. A recently published highly-directive on-body antenna designed for on-in-body communications is used in the evaluations. Different rotation angles of the capsule are also considered both with a layer model and a voxel model. It is found that radio channel characteristics vary remarkably depending on the antenna location in the small intestine and location of the on-body antenna. Thus, the on-body antennas should be located carefully to ensure coverage over the whole intestine area. However, the path loss does not only depend on the distance between a capsule and the on-body antenna but also on the tissues between the capsule and on-body antennas. Furthermore, orientation of the capsule has also strong impact when linearly polarized antennas are used.

Authors:
Särestöniemi Mariella, Pomalaza Raez Carlos, Berg Markus, Kissi Chaïmaâ, Hämäläinen Matti, Iinatti Jari

Publication type:
A4 Article in conference proceedings

Place of publication:
Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019

Keywords:
capsule endoscopy, directive antenna, gastrointestinal monitoring, implant communications, Ultra-Wideband, Wireless body area networks

Published:
16 November 2019

Full citation:
Särestöniemi M., Raez C.P., Berg M., Kissi C., Hämäläinen M., Iinatti J. (2019) WBAN Radio Channel Characteristics Between the Endoscope Capsule and on-Body Antenna. In: Mucchi L., Hämäläinen M., Jayousi S., Morosi S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham

DOI:
https://doi.org/10.1007/978-3-030-34833-5_27

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2019120245121