A subject-specific FEM model for evaluating buttock tissue response under sitting load

F. Lin*, B. Moran, J. Bankard, R. Hendrix, Mohsen Makhsous

*Corresponding author for this work

Research output: Contribution to journalConference article

9 Scopus citations

Abstract

Development of a model for evaluating tissue compression during sitting has been hampered by limitation of implementing the anatomical and mechanical parameters of the various layers of tissue. This study proposes a new method to setup and validate a Finite Element model for buttock tissue to evaluate the tissue response under external sitting load. Multi-layer tissue deformation was measured with ultrasound imaging under simulated sitting load, applied and monitored through an indenter which integrates ultrasound probe and force/pressure sensors. Subject-specific FE model can then be setup and validated. Pilot test of this model revealed that the highest peak stress was found in the vicinity of the ischial tuberosity and the greatest deformation seen in the muscle layer. The authors conclude that this new method allows accurate multi-layer tissue deformation be obtained under close to real sitting load, therefore, provides a better way to evaluate tissue response to external sitting load for seated individuals in a true and subject-specific fashion, which may provide helpful information to improve the sitting comfort and prevent pressure ulcers in wheelchair bound individuals.

Original languageEnglish (US)
Pages (from-to)5088-5091
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VII
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Keywords

  • FEM
  • Sitting load
  • Soft tissue
  • Ultrasound

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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