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Enabling deep-tissue networking for miniature medical devices

Author(s)
Ma, Yunfei; Luo, Zhihong; Steiger, Christoph Winfried Johannes; Traverso, Carlo Giovanni; Adib, Fadel
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
We present IVN (In-Vivo Networking), a system that enables powering up and communicating with miniature sensors implanted or injected in deep tissues. IVN overcomes fundamental challenges which have prevented past systems from powering up miniature sensors beyond superficial depths. These challenges include the significant signal attenuation caused by bodily tissues and the miniature antennas of the implantable sensors. IVN's key contribution is a novel beamforming algorithm that can focus its energy toward an implantable device, despite its inability to estimate its channel or its location. We implement a multi-antenna prototype of IVN, and perform extensive evaluations via in-vitro, ex-vivo, and in-vivo tests in a pig. Our results demonstrate that it can power up and communicate with millimeter-sized sensors at over 10 cm depths in fluids, as well as battery-free tags placed in a central organ of a swine. The implications of our new beamforming technology extend beyond miniature implantables. In particular, our results demonstrate that IVN can power up off-the-shelf passive RFIDs at distances of 38 m, i.e., 7.6× larger than the operation range of the same RFIDs.
Date issued
2018-08
URI
https://hdl.handle.net/1721.1/128779
Department
Massachusetts Institute of Technology. Media Laboratory; Koch Institute for Integrative Cancer Research at MIT
Journal
Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication
Publisher
Association for Computing Machinery (ACM)
Citation
Ma, Yunfei et al. "Enabling deep-tissue networking for miniature medical devices." Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, August 2018, Budapest, Hungary, Association for Computing Machinery © 2018 Copyright held by the owner/author(s)
Version: Author's final manuscript
ISBN
9781450355674

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