An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

Journal: Human factors

PMID: 36367971

Abstract

OBJECTIVE: This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS).

Authors

Jing Yang

Beijing Novartis Pharma Co. Ltd., Beijing, China.
Juan Antonio Barragan

School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Jason Michael Farrow

Department of Urology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Chandru P Sundaram

Department of Urology, Indiana University, Indianapolis, IN, USA.
Juan P Wachs
Denny Yu

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

Keywords

Humans Robotic Surgical Procedures Robotics Suction Task Performance and Analysis Workload

External Resources

View on PubMed Access via DOI PubMed (36367971)

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

Abstract

Authors

Keywords

External Resources

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