Joint Surgeon Attributes Estimation in Robot-Assisted Surgery

Tian Zhou, Jackie S. Cha, Glebys T. Gonzalez, Juan P. Wachs, Chandru Sundaram, Denny Yu

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

Abstract

This paper proposes a computational framework to estimate surgeon attributes during Robot-Assisted Surgery (RAS). The three investigated attributes are workload, performance, and expertise levels. The framework leverages multimodal sensing and joint estimation and was evaluated with twelve surgeons operating on the da Vinci Skills Simulator. The multimodal signals include heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity. The proposed framework reached an average estimation error of 11.05%, and jointly inferring surgeon attributes reduced estimation errors by 10.02%.

Original languageEnglish (US)
Title of host publicationHRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages285-286
Number of pages2
VolumePart F135192
ISBN (Electronic)9781450356152
DOIs
StatePublished - Mar 1 2018
Event13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 - Chicago, United States
Duration: Mar 5 2018Mar 8 2018

Other

Other13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018
CountryUnited States
CityChicago
Period3/5/183/8/18

Keywords

  • da vinci
  • machine learning
  • multimodality
  • robot-assisted surgery
  • surgeon assessment
  • teleoperation
  • workload

ASJC Scopus subject areas

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

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  • Cite this

    Zhou, T., Cha, J. S., Gonzalez, G. T., Wachs, J. P., Sundaram, C., & Yu, D. (2018). Joint Surgeon Attributes Estimation in Robot-Assisted Surgery. In HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Vol. Part F135192, pp. 285-286). IEEE Computer Society. https://doi.org/10.1145/3173386.3176981