@inproceedings{14f62541e83a4fe3a15f006e9e388438,
title = "Joint Surgeon Attributes Estimation in Robot-Assisted Surgery",
abstract = "This paper proposes a computational framework to estimate surgeon attributes during Robot-Assisted Surgery (RAS). The three investigated attributes are workload, performance, and expertise levels. The framework leverages multimodal sensing and joint estimation and was evaluated with twelve surgeons operating on the da Vinci Skills Simulator. The multimodal signals include heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity. The proposed framework reached an average estimation error of 11.05%, and jointly inferring surgeon attributes reduced estimation errors by 10.02%.",
keywords = "da vinci, machine learning, multimodality, robot-assisted surgery, surgeon assessment, teleoperation, workload",
author = "Tian Zhou and Cha, {Jackie S.} and Gonzalez, {Glebys T.} and Wachs, {Juan P.} and Chandru Sundaram and Denny Yu",
note = "Publisher Copyright: {\textcopyright} 2018 Authors.; 13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 ; Conference date: 05-03-2018 Through 08-03-2018",
year = "2018",
month = mar,
day = "1",
doi = "10.1145/3173386.3176981",
language = "English",
series = "ACM/IEEE International Conference on Human-Robot Interaction",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "285--286",
booktitle = "HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction",
address = "United States",
}