Low-Cost Haptic Simulation Using Material Fracture

David F. Pepley, Hong En Chen, Yichun Tang, Sanjib Das Adhikary, Scarlett R. Miller, Jason Z. Moore

Research output: Contribution to journalArticle

Abstract

Medical simulation training is widely used to effectively train for invasive medical procedures such as peripheral nerve blocks. Traditionally, accurate haptic training relies on expensive cadavers, manikins, or advanced haptic robots. Proposed herein is a novel concept for haptic training called the low-cost haptic force needle insertion simulator (LCNIS), which uses material fracture inside disposable cartridges to accurately replicate the force of inserting a needle into tissue. Cadaver and material fracture experiments were performed to develop and determine the accuracy of the LCNIS. The material testing showed that polycarbonate had the highest maximum needle puncture force of the materials tested, 9.85 N, and that fluorinated ethylene propylene had the lowest maximum puncture force, 0.84 N. The cadaver results showed that the error between the three peak forces in a cadaver and a cadaver mimicking cartridge was 1.00 N, 0.01 N, and 1.54 N. The standard deviation of these peaks was 0.60 N, 0.55 N, and 0.41 N. This novel method of haptic simulation can easily be adapted to recreate any type of force and, therefore, could be utilized to train for a wide variety of medical procedures.

Original languageEnglish (US)
Article number8704968
Pages (from-to)563-570
Number of pages8
JournalIEEE Transactions on Haptics
Volume12
Issue number4
DOIs
StatePublished - Oct 1 2019

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Needles
Costs
Simulators
Materials testing
Polycarbonates
Propylene
Ethylene
Robots
Tissue
Experiments

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications

Cite this

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Low-Cost Haptic Simulation Using Material Fracture. / Pepley, David F.; Chen, Hong En; Tang, Yichun; Adhikary, Sanjib Das; Miller, Scarlett R.; Moore, Jason Z.

In: IEEE Transactions on Haptics, Vol. 12, No. 4, 8704968, 01.10.2019, p. 563-570.

Research output: Contribution to journalArticle

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