Fat-IBC: Advancing intra-body communication for functional restoration

Abstract

Intra-body communication has been extensively researched over the past few decades to address needs in real-time monitoring, drug delivery, and proactive health sensing, all aimed at improving quality of life. These applications extend beyond healthcare to areas such as recreation, sports, and information technology. Several human body-centric (HBC) communication modalities have been developed, including galvanic, capacitive, and inductive methods, which use the human body or its parts as the communication channel. While these technologies enable wireless data transfer between different parts of the body, they are limited by low bandwidth and, consequently, reduced data rates. Until recently, radio frequency (RF) communication was considered an unlikely candidate for extensive HBC applications due to high attenuation in body tissues. However, in 2016, Asan et al.~from the Microwaves in Medical Engineering Group at Uppsala University, Sweden, published pioneering work demonstrating the feasibility of using adipose (fat) tissue to transmit microwave signals with significantly low loss (approximately 2 dB/cm). This initiated a wave of research into fat-based intra-body communication (Fat-IBC), with numerous studies exploring its potential. Anatomically, fat tissue is located between denser layers such as skin and muscle. Due to its low water content, fat exhibits low permittivity and signal loss, whereas muscle and skin have much higher permittivity and loss—three to four times that of fat. This contrast forms a natural waveguide, which can be exploited to transmit microwave signals effectively at ISM frequencies. Fat-IBC offers a significant advancement in intra-body data transmission by providing higher bandwidth and improved power efficiency, thereby extending the battery life of implanted devices. It also holds promise for applications such as artificial limbs, which require the wireless transfer of large volumes of electrophysiological data.