A predictive model for haptic assistance in robot assisted trocar insertion

Ashirwad Chowriappa, Raul Wirz, Yong Won Seo, Aditya Reddy, Tushar Kesavadas, Peter Scott, Khurshid Guru, Thenkurussi Kesavadas

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

Abstract

In this work we propose a new Prediction from Expert Demonstration (PED) methodology to provide haptic assistance in robot assisted trocar surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encode a set of force, torque and penetration trajectories by using a Gaussian Mixture Model (GMM). A generalization of these profiles and associated parameters are retrieved by Gaussian Mixture Regression (GMR). A haptic assistance mode was devised to help novices perform the procedure based on the proposed PED model. We validated the methodology for surgical assistance on (n= 15) participants. The PED haptic model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED haptic assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.

Original languageEnglish (US)
Title of host publication2013 World Haptics Conference, WHC 2013
Pages121-126
Number of pages6
DOIs
StatePublished - Aug 19 2013
Externally publishedYes
Event2013 IEEE World Haptics Conference, WHC 2013 - Daejeon, Korea, Republic of
Duration: Apr 14 2013Apr 17 2013

Publication series

Name2013 World Haptics Conference, WHC 2013

Other

Other2013 IEEE World Haptics Conference, WHC 2013
CountryKorea, Republic of
CityDaejeon
Period4/14/134/17/13

Keywords

  • Gaussian Model
  • Haptic
  • Robot Assisted Surgery
  • Surgical Assistance
  • Trocar Insertion

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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