Multimodal training by demonstration for robot-assisted surgery

Alaa Eldin Abdelaal, Gregory D. Hager, Septimiu E. Salcudean

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

Abstract

Improving surgical training has the potential to reduce medical errors and consequently to save many lives. We briefly present our efforts to improve this training for robot-assisted surgery. In particular, we explore how data collected from expert demonstrations can enhance the training efficiency for novices. Thus far, our results show that combining hand-over-hand training based on experts' motion data with trial and error training can improve the training outcomes in robotic and conventional laparoscopic surgery settings. We briefly describe our current efforts for exploring how gaze-based training methods, based on experts' eye gaze data, can improve the training outcomes as well.

Original languageEnglish (US)
Title of host publicationHRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages549-551
Number of pages3
ISBN (Electronic)9781450370578
DOIs
StatePublished - Mar 23 2020
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: Mar 23 2020Mar 26 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
CountryUnited Kingdom
CityCambridge
Period3/23/203/26/20

Keywords

  • Gaze tracking
  • Robot-assisted surgery
  • Surgical training
  • Training by demonstration

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Abdelaal, A. E., Hager, G. D., & Salcudean, S. E. (2020). Multimodal training by demonstration for robot-assisted surgery. In HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 549-551). (ACM/IEEE International Conference on Human-Robot Interaction). IEEE Computer Society. https://doi.org/10.1145/3371382.3377448