Inductive Wireless Power Transfer to Multiple Biomedical Implants

George Alexandrov

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2022-10
May 1, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-10.pdf

Recent progress in smart devices and the Internet of Things has led to a huge growth in smart systems. Phones, homes, cars, and appliances can all communicate with each other in order to expand their functionality and offer new ways of interaction. Unfortunately, human input/output modalities are still limited to our five senses - sight, hearing, touch, taste, and smell - leading to a gap between humans and the evolving world around us.

The Human Intranet (HI) is the vision that human capabilities will be extended through human-device interaction. It is an open, scalable platform that seamlessly integrates an ever- increasing number of sensor, actuation, computation, storage, communication, and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. A major challenge is providing reliable energy to these devices, as they are inherently distributed and modular, have widely varying energy requirements, and must operate chronically.

This thesis focuses on powering distributed implants inside the body. Two dimensional tessellations of magnetic coils are used to generate a homogeneous magnetic field across a large area and energize implants within. A system for distributed recording of electromyography (EMG) signals in the forearm is presented. Single-channel recording implants located under- neath the skin and fat are wirelessly powered through a custom inductive link and transmit uplink data through FDM passive backscatter, enabling chronic EMG recording for use in gesture recognition platforms.

Advisor: Jan M. Rabaey


BibTeX citation:

@phdthesis{Alexandrov:EECS-2022-10,
    Author = {Alexandrov, George},
    Title = {Inductive Wireless Power Transfer to Multiple Biomedical Implants},
    School = {EECS Department, University of California, Berkeley},
    Year = {2022},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-10.html},
    Number = {UCB/EECS-2022-10},
    Abstract = {Recent progress in smart devices and the Internet of Things has led to a huge growth in smart systems. Phones, homes, cars, and appliances can all communicate with each other in order to expand their functionality and offer new ways of interaction. Unfortunately, human input/output modalities are still limited to our five senses - sight, hearing, touch, taste, and smell - leading to a gap between humans and the evolving world around us.

The Human Intranet (HI) is the vision that human capabilities will be extended through human-device interaction. It is an open, scalable platform that seamlessly integrates an ever- increasing number of sensor, actuation, computation, storage, communication, and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. A major challenge is providing reliable energy to these devices, as they are inherently distributed and modular, have widely varying energy requirements, and must operate chronically.

This thesis focuses on powering distributed implants inside the body. Two dimensional tessellations of magnetic coils are used to generate a homogeneous magnetic field across a large area and energize implants within. A system for distributed recording of electromyography (EMG) signals in the forearm is presented. Single-channel recording implants located under- neath the skin and fat are wirelessly powered through a custom inductive link and transmit uplink data through FDM passive backscatter, enabling chronic EMG recording for use in gesture recognition platforms.}
}

EndNote citation:

%0 Thesis
%A Alexandrov, George
%T Inductive Wireless Power Transfer to Multiple Biomedical Implants
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 1
%@ UCB/EECS-2022-10
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-10.html
%F Alexandrov:EECS-2022-10