Frequency response characterization of various media for magnetic human body communication


Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye

Tezin Onay Tarihi: 2023

Tezin Dili: İngilizce

Öğrenci: EGEMEN BERAT YALÇINKAYA

Danışman: Tülay Yıldırım

Özet:

This thesis presents an overview of the emerging field of Human Body Communication (HBC), which utilizes the electrical or magnetic properties of the human body to facilitate signal transmission among electronic devices through human tissue. The three methodologies of HBC - Capacitive HBC, Galvanic HBC, and Magnetic HBC - are discussed, with the aim of providing a brief understanding of the HBC methodology and identifying areas for future research and development. The thesis specifically employs the Magnetic HBC method to conduct frequency response measurements of various media, including salt water and sugar water, using a Network Analyzer. Furthermore, the potential of HBC in the modeling of the human body and its use in non-invasive methods for monitoring human health is explored. The findings of the measurements reveal that the frequency range where the human body is most permeable is between 380-450 kHz, and the amount of fat in the body should be considered in modeling the human. The use of magnetic HBC enables the measurement of bodily metrics through non-invasive methods, such as the ratio of sugar, fat, and water in the body. The thesis presents the findings of a regression analysis that demonstrates a potential model for measuring blood sugar levels in individuals with diabetes. The accuracy of this sugar model is essential for managing diabetes and preventing complications associated with high or low blood sugar levels. These findings suggest that HBC has the potential to greatly enhance our understanding of the human body and aid in the development of non-invasive methods for monitoring human health. Further research is needed to confirm the reliability and generalizability of the sugar model across different people.