Abstract-New medical procedures promise continuous patient monitoring and drug delivery through implanted sensors and actuators. When over the air wireless radio frequency (OTA-RF) links are used for intra-body implant communication, the network incurs heavy energy costs owing to absorption within the human tissue. With this motivation, we explore an alternate form of intra-body communication that relies on weak electrical signals, instead of OTA-RF. To demonstrate the feasibility of this new paradigm for enabling communication between sensors and actuators embedded within the tissue, or placed on the surface of the skin, we develop a rigorous analytical model based on galvanic coupling of low energy signals. The main contributions in this paper are: (i) developing a suite of analytical expressions for modeling the resulting communication channel for weak electrical signals in a three dimensional multi-layered tissue structure, (ii) validating and verifying the model through extensive finite element simulations, published measurements in existing literature, and experiments conducted with porcine tissue, (iii) designing the communication framework with safety considerations, and analyzing the influence of different network and hardware parameters such as transmission frequency and electrode placements. Our results reveal a close agreement between theory, simulation, literature and experimental findings, pointing to the suitability of the model for quick and accurate channel characterization and parameter estimation for networked and implanted sensors.

Sensors implanted inside a body compose so called intra body networks (IBNs), which promise high degree of mobility, remote diagnostic accuracy, and the potential of directly activating the action of drug delivery actuators. To enable communication among these implanted sensors, we use the concept of galvanic coupling, in which extremely low energy electrical signals are coupled into the human body tissues by leveraging the conductive properties of the tissues. Several challenges emerge in this new communication paradigm, such as how to appropriately model the signal propagation through various tissue paths such as from muscle to skin across different tissue boundaries and quantify the achievable data rates. The main contributions in this paper are: (i) we build a 2-port tissue equivalent circuit model to characterize the body channel and to identify the range of suitable operating frequencies and (ii) we theoretically estimate the channel capacity for various sensor locations that incorporates factors like the tissue propagation path, operating frequency and noise level.