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January 30, 2020ACM Transactions on Human-Robot InteractionOpen Access

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

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Why the study?

Current methods for measuring cognitive load in robot-assisted surgery rely on subjective questionnaires that disrupt surgical workflow.

Does a multisensor approach using multimodal physiological signals accurately predict cognitive workload levels in surgeons performing robot-assisted surgery?

Population

Twelve surgeons performing tasks on the da Vinci Skills Simulator

Comparison

Prediction with single vs multiple physiological modalities

Authors

TZTian ZhouMidwestern UniversityJCJackie ChaUniversity of Wisconsin–MadisonGGGlebys GonzalezMoffitt Cancer Center

Discussion

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Implication

May enable real-time workload monitoring in robotic surgery; hypothesis-generating and requires larger validation before adoption.

Structured PICO

Does a multisensor approach using multimodal physiological signals accurately predict cognitive workload levels in surgeons performing robot-assisted surgery?

P
Population
Twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator.
I
Intervention
Multisensor approach using continuous data across multiple physiological modalities (heart rate variability, electrodermal, and electroencephalogram activity) fused using independent component analysis.
C
Comparator
Individual physiological signals and different classifiers.
O
Outcome
Prediction of cognitive workload levels compared to ground-truth.

A multisensor approach using multimodal physiological signals can accurately predict surgeon cognitive workload during robot-assisted surgery, outperforming individual signals.

Cite This Study

Zhou et al. (2020) studied this question.

synapsesocial.com/papers/69d670fdf653e43faa88b3fehttps://doi.org/10.1145/3368589
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  3. 3Regression Diagnostics -- Identifying Influential Data and Sources of Collinearity1981 · 6,479 citations