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Experimental verification of human body communication path gain channel modeling for muscular-tissue characteristics
Zhang, Shuang1,2,3,4,5,6,7; Liu, Yi He1,7; Qin, Yu Ping1,7; Kuang, Jiang Ming1,7; Yang, Ji Ning1,7; Li, Jia Wen5,6; Wang, Jiu Jiang1,5,6,7; Zhang, Tao2,3,4; Zou, Xue Ming2,3,4
2019
Source PublicationIEEE Access
Volume7Pages:122769-122783
Abstract

To study the signal transmission mechanism in the human body, the channel characteristics are generally analyzed by modeling. In current modeling methods, the human body is considered quasi-static and the human tissues isotropic, for simplifying the model and its calculation; however, this does not consider the effect of the human tissues on electric signal transmission, resulting in considerable deviations between the calculated results and the measured values. To reduce model errors and improve precision, a channel modeling method with human muscular-tissue characteristics is proposed in this study. In this method, Maxwell's equations is used as the governing equation and a galvanic-coupling intra-body communication channel model with human-tissue characteristics is built in the cylindrical coordinate system. By building a numerical model with the same parameters as in the analytical model, the analytical solution is proved to be correct. By comparing the different-sample anisotropic models and the isotropic models with the experimental results, it is concluded that the anisotropic model with muscular-tissue characteristics is superior to the isotropic model without muscular-tissue characteristics, with respect to the curve variation tendency and error between the model calculations and the experimental results. The precision of this anisotropic model is enhanced by 200%; hence, it is more accurate. At last, in order to study the optimal communication frequency of the channel, we select 50 healthy persons as the subjects of this experiment, we find that the optimal communication frequency band of the human arm is 10 kHz to 50 kHz. Within this frequency band, the channel gain is the largest, and the mean deviation of samples is less than 2dB, which is very beneficial to signal transmission in human body.

KeywordChannel Modeling Galvanic Coupling Human-body Communication Tissue Characteristics
DOI10.1109/ACCESS.2019.2937945
URLView the original
Language英語English
WOS IDWOS:000487831900002
Scopus ID2-s2.0-85078279444
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Data Recovery Key Laboratory of Sichuan Province, College of Computer Science and Ai, Neijiang Normal University, Neijiang, 641100, China
2.School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
3.High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu, 611731, China
4.Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
5.State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, 999078, Macao
6.Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, 999078, Macao
7.Neijiang Normal University, BeiDou and Wisdom Medical Doctor Workstation, Neijiang, 641110, China
First Author AffilicationUniversity of Macau;  Faculty of Science and Technology
Recommended Citation
GB/T 7714
Zhang, Shuang,Liu, Yi He,Qin, Yu Ping,et al. Experimental verification of human body communication path gain channel modeling for muscular-tissue characteristics[J]. IEEE Access, 2019, 7, 122769-122783.
APA Zhang, Shuang., Liu, Yi He., Qin, Yu Ping., Kuang, Jiang Ming., Yang, Ji Ning., Li, Jia Wen., Wang, Jiu Jiang., Zhang, Tao., & Zou, Xue Ming (2019). Experimental verification of human body communication path gain channel modeling for muscular-tissue characteristics. IEEE Access, 7, 122769-122783.
MLA Zhang, Shuang,et al."Experimental verification of human body communication path gain channel modeling for muscular-tissue characteristics".IEEE Access 7(2019):122769-122783.
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