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abstract

Estimating mechanical properties of soft objects using surface measurements from AR headsets

Published: 09 October 2023 Publication History

Abstract

Physics-driven predictions of soft tissue mechanics are vital for various medical interventions. Insights on the mechanical properties of soft tissues are essential for obtaining personalised predictions from these models. This study aims to provide a workflow to identify the material parameters of soft homogeneous materials under gravity loading using 3D surface geometrical measurements acquired from a wearable augmented reality (AR) headset’s depth camera. Preliminary results show that the parameter estimation procedure can successfully recover the ground truth material parameter C1 of a cantilever beam using synthetic surface data. This workflow could be used for real-time navigational guidance during soft tissue treatment procedures.

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Published In

cover image ACM Conferences
VRST '23: Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology
October 2023
542 pages
ISBN:9798400703287
DOI:10.1145/3611659
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 October 2023

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Author Tags

  1. Mechanical parameter estimation
  2. augmented reality

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  • Abstract
  • Research
  • Refereed limited

Funding Sources

  • Ministry for Business, Innovation and Employment New Zealand
  • New Zealand Breast Cancer Foundation
  • University of Auckland Foundation

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VRST 2023

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Overall Acceptance Rate 66 of 254 submissions, 26%

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