Using Multimodal Data to Predict Surgeon Situation Awareness - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

Using Multimodal Data to Predict Surgeon Situation Awareness

Résumé

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.
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Dates et versions

hal-03584633 , version 1 (22-02-2022)

Identifiants

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Aurelien Lechappe, Mathieu Chollet, Jerome Rigaud, Caroline Cao. Using Multimodal Data to Predict Surgeon Situation Awareness. IEA 2021: 21st Congress of the International Ergonomics Association, Jun 2021, En ligne, France. pp.308-316, ⟨10.1007/978-3-030-74614-8_37⟩. ⟨hal-03584633⟩
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