Using Multimodal Data to Predict Surgeon Situation Awareness

Aurelien Lechappe, Mathieu Chollet, Jerome Rigaud, Caroline G.L. Cao

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


The use of robotic surgical systems creates new team dynamics in operating rooms and constitutes a major challenge for the development of crucial non-technical skills such as situation awareness (SA). Techniques for assessing SA mostly rely on subjective assessments, observation or interviews; few utilize multimodal measures that combine physiological, behavioural, and subjective indicators. We proposed a conceptual model relating situation awareness with mental workload (MW), stress and communication. To validate this model, we collected subjective feedback, measurable behaviours and physiological signals from surgeons performing a robot-assisted radical prostatectomy procedure. Preliminary results suggest that subjective MW is a better indicator of SA than subjective stress. Physiological measures did not correlate with subjective measures of stress and MW. Results also suggest that some indicators of communication quality associated with various levels of SA tend to be linked with surgical complexity.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st Congress of the International Ergonomics Association, IEA 2021
Subtitle of host publicationMethods and Approaches
EditorsNancy L. Black, W. Patrick Neumann, Ian Noy
Number of pages9
ISBN (Print)9783030746131
StatePublished - 2022
Externally publishedYes
Event21st Congress of the International Ergonomics Association, IEA 2021 - Virtual, Online
Duration: Jun 13 2021Jun 18 2021

Publication series

NameLecture Notes in Networks and Systems
Volume223 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference21st Congress of the International Ergonomics Association, IEA 2021
CityVirtual, Online


  • Communication
  • Mental workload
  • Physiological signals
  • Robot-assisted surgery
  • Stress

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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