Särestöniemi M., Pomalaza-Raez C., Kissi C., Iinatti J. (2020) On the UWB in-Body Propagation Measurements Using Pork Meat. In: Alam M.M., Hämäläinen M., Mucchi L., Niazi I.K., Le Moullec Y. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health. BODYNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-64991-3_2
On the UWB in-body propagation measurements using pork meat
|Author:||Särestöniemi, Mariella1; Pomalaza-Raez, Carlos2; Kissi, Chaïmaâ3;|
1Centre for Wireless Communications, University of Oulu, Finland
2Department of Electrical and Computer Engineering, Purdue University, USA
3Electronics and Telecommunication Systems Research Group, National School of Applied Sciences (ENSA), Ibn Tofail University, Morocco
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102023471
|Publish Date:|| 2021-02-02
This paper presents a study on the in-body propagation using pork meat at the lower ultrawideband (UWB) frequency band 3.74—4.25 GHz of the wireless body area network (WBAN) standard 802.15.6. Pork meat in terms of the dielectric properties is one of the most similar to human tissues and thus is commonly used in in-body propagation studies. Nevertheless, there are differences in the dielectric properties, creating some differences also in the radio propagation. The first objective of this paper is to investigate by simulations the propagation differences between human and pork tissue layer models. The simulations results show clear differences between the channel characteristics obtained using a human tissues and pork tissues: within the frequency range of interest, the path loss with pork meat can be up to 5 dB less than with the human meat. The second objective of this paper is to study, by measurements, the in-body channel characteristics using different types of pork meat piece having different fat and muscle compositions. It was found that path loss is clearly higher with the pork meat having separate skin, fat, and muscle layers compared to the pork meat having interlaced fat and muscle layers. Furthermore, the third objective of this paper is to study the impact of the meat temperature on the measured channel characteristics by comparing the channels obtained with the meat at the temperatures of 12 °C and at 37 °C. Also, in this case clear differences were observed in path loss: within the frequency range of interest, the path loss was maximum 5 dB lower with meat at 37 °C than with a colder meat. The results presented in this paper provide useful information and relevant aspects for the in-body propagation studies conducted with pork meat.
Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
|Pages:||18 - 33|
Body area networks. Smart IoT and big data for intelligent health. Proceeding of the 15th EAI international conference, BODYNETS 2020. Tallinn, Estonia October 21, 2020 (online conference)
|Host publication editor:||
Alam, M. M.
Niazi, I. K.
Le Moullec, Y.
EAI Bodynets. EAI International Conference on Body Area Networks
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported in part by the projects WBAN Communications in the Congested Environments (MeCCE), the Academy of Finland 6Genesis Flagship (grant 318927) and the European Union’s Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement No. 872752. Dr. Marko Sonkki is acknowledged for his participation on the on-body antenna design.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020. This is a post-peer-review, pre-copyedit version of an article published in Body area networks. Smart IoT and big data for intelligent health. Proceeding of the 15th EAI international conference, BODYNETS 2020. Tallinn, Estonia October 21, 2020 (online conference). The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-64991-3_2.