Stochastic event detection in needle-tissue interaction

Inki Kim, Adam Gordon, Scarlett Rae Miller

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

Abstract

Over the last decade, many dynamic models that express needle-force relationships under tissues of varying mechanical properties have been developed. While great progress has been made in the development of these high-fidelity models, they are only valid within certain boundary conditions limiting their match with reality. This gap in realism is aggravated by variability in human tissues, needles, and the modes of interaction with the tissue. In an effort to develop more realistic models, the current paper was developed to create and test an event (i.e. changes of variability) detection method based on the probability distribution of residues-difference between force models and measurements. To obtain force measurements, we repeated robotic-driven needle insertion into a simulated mannequin. Needle types and tissue thickness were varied in the measurements in order to add realistic variability. To obtain the force model, the measurement data was used as an input to a Grey-Box model. From the measurements and models, we estimated the probability distribution of residues. For validation, a Gaussian-Mixture Model (GMM) was used to confirm the stochastic model successfully distinguishes the residual distributions under different variability. We found that by examining the residual distributions it is possible to detect unexpected variability in needle-tissue interactions. The findings from this paper have implications for developing real-time event detection methods and simulating patient-variability in haptic applications.

Original languageEnglish (US)
Title of host publication35th Computers and Information in Engineering Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791857045
DOIs
StatePublished - Jan 1 2015
EventASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015 - Boston, United States
Duration: Aug 2 2015Aug 5 2015

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume1A-2015

Other

OtherASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
CountryUnited States
CityBoston
Period8/2/158/5/15

Fingerprint

Event Detection
Needles
Tissue
Interaction
Probability Distribution
Probability distributions
Model
Repeated Measurements
Haptics
Gaussian Mixture Model
Force measurement
Fidelity
Mechanical Properties
Stochastic models
Insertion
Stochastic Model
Robotics
Dynamic Model
Express
Limiting

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Cite this

Kim, I., Gordon, A., & Miller, S. R. (2015). Stochastic event detection in needle-tissue interaction. In 35th Computers and Information in Engineering Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1A-2015). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2015-47384
Kim, Inki ; Gordon, Adam ; Miller, Scarlett Rae. / Stochastic event detection in needle-tissue interaction. 35th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME), 2015. (Proceedings of the ASME Design Engineering Technical Conference).
@inproceedings{84d9779f547d4e68afd31d60cb2a066f,
title = "Stochastic event detection in needle-tissue interaction",
abstract = "Over the last decade, many dynamic models that express needle-force relationships under tissues of varying mechanical properties have been developed. While great progress has been made in the development of these high-fidelity models, they are only valid within certain boundary conditions limiting their match with reality. This gap in realism is aggravated by variability in human tissues, needles, and the modes of interaction with the tissue. In an effort to develop more realistic models, the current paper was developed to create and test an event (i.e. changes of variability) detection method based on the probability distribution of residues-difference between force models and measurements. To obtain force measurements, we repeated robotic-driven needle insertion into a simulated mannequin. Needle types and tissue thickness were varied in the measurements in order to add realistic variability. To obtain the force model, the measurement data was used as an input to a Grey-Box model. From the measurements and models, we estimated the probability distribution of residues. For validation, a Gaussian-Mixture Model (GMM) was used to confirm the stochastic model successfully distinguishes the residual distributions under different variability. We found that by examining the residual distributions it is possible to detect unexpected variability in needle-tissue interactions. The findings from this paper have implications for developing real-time event detection methods and simulating patient-variability in haptic applications.",
author = "Inki Kim and Adam Gordon and Miller, {Scarlett Rae}",
year = "2015",
month = "1",
day = "1",
doi = "10.1115/DETC2015-47384",
language = "English (US)",
series = "Proceedings of the ASME Design Engineering Technical Conference",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "35th Computers and Information in Engineering Conference",

}

Kim, I, Gordon, A & Miller, SR 2015, Stochastic event detection in needle-tissue interaction. in 35th Computers and Information in Engineering Conference. Proceedings of the ASME Design Engineering Technical Conference, vol. 1A-2015, American Society of Mechanical Engineers (ASME), ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, United States, 8/2/15. https://doi.org/10.1115/DETC2015-47384

Stochastic event detection in needle-tissue interaction. / Kim, Inki; Gordon, Adam; Miller, Scarlett Rae.

35th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME), 2015. (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1A-2015).

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

TY - GEN

T1 - Stochastic event detection in needle-tissue interaction

AU - Kim, Inki

AU - Gordon, Adam

AU - Miller, Scarlett Rae

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Over the last decade, many dynamic models that express needle-force relationships under tissues of varying mechanical properties have been developed. While great progress has been made in the development of these high-fidelity models, they are only valid within certain boundary conditions limiting their match with reality. This gap in realism is aggravated by variability in human tissues, needles, and the modes of interaction with the tissue. In an effort to develop more realistic models, the current paper was developed to create and test an event (i.e. changes of variability) detection method based on the probability distribution of residues-difference between force models and measurements. To obtain force measurements, we repeated robotic-driven needle insertion into a simulated mannequin. Needle types and tissue thickness were varied in the measurements in order to add realistic variability. To obtain the force model, the measurement data was used as an input to a Grey-Box model. From the measurements and models, we estimated the probability distribution of residues. For validation, a Gaussian-Mixture Model (GMM) was used to confirm the stochastic model successfully distinguishes the residual distributions under different variability. We found that by examining the residual distributions it is possible to detect unexpected variability in needle-tissue interactions. The findings from this paper have implications for developing real-time event detection methods and simulating patient-variability in haptic applications.

AB - Over the last decade, many dynamic models that express needle-force relationships under tissues of varying mechanical properties have been developed. While great progress has been made in the development of these high-fidelity models, they are only valid within certain boundary conditions limiting their match with reality. This gap in realism is aggravated by variability in human tissues, needles, and the modes of interaction with the tissue. In an effort to develop more realistic models, the current paper was developed to create and test an event (i.e. changes of variability) detection method based on the probability distribution of residues-difference between force models and measurements. To obtain force measurements, we repeated robotic-driven needle insertion into a simulated mannequin. Needle types and tissue thickness were varied in the measurements in order to add realistic variability. To obtain the force model, the measurement data was used as an input to a Grey-Box model. From the measurements and models, we estimated the probability distribution of residues. For validation, a Gaussian-Mixture Model (GMM) was used to confirm the stochastic model successfully distinguishes the residual distributions under different variability. We found that by examining the residual distributions it is possible to detect unexpected variability in needle-tissue interactions. The findings from this paper have implications for developing real-time event detection methods and simulating patient-variability in haptic applications.

UR - http://www.scopus.com/inward/record.url?scp=84978976088&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978976088&partnerID=8YFLogxK

U2 - 10.1115/DETC2015-47384

DO - 10.1115/DETC2015-47384

M3 - Conference contribution

T3 - Proceedings of the ASME Design Engineering Technical Conference

BT - 35th Computers and Information in Engineering Conference

PB - American Society of Mechanical Engineers (ASME)

ER -

Kim I, Gordon A, Miller SR. Stochastic event detection in needle-tissue interaction. In 35th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME). 2015. (Proceedings of the ASME Design Engineering Technical Conference). https://doi.org/10.1115/DETC2015-47384