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Adaptive Human-Robotic Interaction for Robotic-assisted Surgical Settings.

Jing Yang¹, Iris Charlene Layadi², Juan P Wachs¹

¹School of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA.

Military Medicine

|November 10, 2023

Summary

This study developed a real-time system to monitor surgeons

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Area of Science:

Robotics
Human-Computer Interaction
Surgical Technology

Background:

Complex robotic-assisted surgical systems increase mental workload (MWL), negatively impacting surgeon performance and increasing errors.
Real-time monitoring of operator MWL is crucial for timely interventions to manage cognitive load.

Purpose of the Study:

To construct and evaluate an adaptive automation system based on real-time mental workload monitoring for robotic-assisted surgery.
To assess the system's effectiveness in moderating MWL and improving surgical task performance.

Main Methods:

Physiological signals from 10 participants were used to develop a real-time cognitive workload monitoring system.
15 participants performed surgical tasks with and without the adaptive aiding system to compare performance and perceived workload.

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Main Results:

The neural network model achieved 77.9% accuracy in classifying cognitive workload.
Participants using the adaptive aiding system demonstrated improved task performance and reduced perceived workload.

Conclusions:

The MWL monitoring system effectively reduced perceived workload and enhanced task performance under stress.
Automated adaptive aiding, triggered by cognitive workload, shows potential for improving robotic-assisted surgery outcomes.