Exploring Model-based Failure Prediction of Passive Bio-electro-mechanical Implants

Daniel Gulick, Yuna Jung, Seunghyun Lee, Sule Ozev, Jennifer Blain Christen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A range of medical issues are treated by simple biomechanical implants to regulate fluid pressure and flow (e.g. valves, shunts). With moving parts in fluid, these implants are vulnerable to biological failures (infection, migration), mechanical failures (clogging, cracking), and parametric failures (change in flow resistance, cracking pressure). Existing biomechanical implants only show failure by clinical symptoms, which may be catastrophic. A means to better observe device behavior and predict failure is necessary. We explore merging biomechanical implants with low-footprint passive electronics, creating bio-electro-mechanical (BEM) devices and thereby allowing external monitoring. Passive feedback signals (RF backscatter) may be interpreted by a model to extract flow parameters and predict failure. A model may be trained by benchtop testing, to correlate direct measurements (flow, pressure) with passive device signals. Benchtop failure simulation (accelerated aging, simulated biofouling) may better train the model for failure prediction. This paper uses long-term pressure/flow testing data from a simple biomechanical device (hydrogel valve for hydrocephalus) as a test case for extracting predictive signals of imminent device failure.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 40th VLSI Test Symposium, VTS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665410601
StatePublished - 2022
Event40th IEEE VLSI Test Symposium, VTS 2022 - Virtual, Online, United States
Duration: Apr 25 2022Apr 27 2022

Publication series

NameProceedings of the IEEE VLSI Test Symposium


Conference40th IEEE VLSI Test Symposium, VTS 2022
Country/TerritoryUnited States
CityVirtual, Online


  • biomedical
  • failure prediction
  • model-based

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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