Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies

Bernhard Anzengruber-Tánase; Alois Ferscha; Martin Schobesberger

doi:10.1109/ACII.2019.8925517

Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies

Bernhard Anzengruber-Tánase
, Alois Ferscha
, Martin Schobesberger

Institute of Pervasive Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Due to the large cognitive strain put upon surgeons' attention during laparoscopic cholecystectomies, automated interface systems that provide meaningful information and feedback without incurring increased cognitive load are necessary. This work outlines (i) a method based on visual data analysis for estimating a surgeon's visual distraction from the surgical monitor and (ii) an approach for using this information to provide unobtrusive reminders to (novice) surgeons during critical sections of the surgical workflow. A large corpus of sensor data was collected during 38 real-world laparoscopic cholecystectomies, and the algorithms were trained and optimized using this data. The algorithm for detecting visual distraction achieved a recognition rate of 93.8%, and a temporal attention-sensitive algorithm for triggering feedback based on thresholds was successfully devised. Further insights into patterns of attention/distraction of surgeons could be derived by means of qualitative analysis.

Original language	English
Title of host publication	2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
Publisher	IEEE
Pages	346-352
Number of pages	7
ISBN (Electronic)	9781728138886
DOIs	https://doi.org/10.1109/ACII.2019.8925517
Publication status	Published - Sept 2019

Fields of science

202017 Embedded systems
102 Computer Sciences
102009 Computer simulation
102013 Human-computer interaction
102019 Machine learning
102020 Medical informatics
102021 Pervasive computing
102022 Software development
102025 Distributed systems
211902 Assistive technologies
211912 Product design

JKU Focus areas

Digital Transformation

Access to Document

10.1109/ACII.2019.8925517

Cite this

@inproceedings{9957060fd2714a638b39095070003a6b,

title = "Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies",

abstract = "Due to the large cognitive strain put upon surgeons' attention during laparoscopic cholecystectomies, automated interface systems that provide meaningful information and feedback without incurring increased cognitive load are necessary. This work outlines (i) a method based on visual data analysis for estimating a surgeon's visual distraction from the surgical monitor and (ii) an approach for using this information to provide unobtrusive reminders to (novice) surgeons during critical sections of the surgical workflow. A large corpus of sensor data was collected during 38 real-world laparoscopic cholecystectomies, and the algorithms were trained and optimized using this data. The algorithm for detecting visual distraction achieved a recognition rate of 93.8\%, and a temporal attention-sensitive algorithm for triggering feedback based on thresholds was successfully devised. Further insights into patterns of attention/distraction of surgeons could be derived by means of qualitative analysis.",

author = "Bernhard Anzengruber-T{\'a}nase and Alois Ferscha and Martin Schobesberger",

year = "2019",

month = sep,

doi = "10.1109/ACII.2019.8925517",

language = "English",

pages = "346--352",

booktitle = "2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)",

publisher = "IEEE",

}

Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies. / Anzengruber-Tánase, Bernhard; Ferscha, Alois ; Schobesberger, Martin.
2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2019. p. 346-352 8925517.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies

AU - Anzengruber-Tánase, Bernhard

AU - Ferscha, Alois

AU - Schobesberger, Martin

PY - 2019/9

Y1 - 2019/9

N2 - Due to the large cognitive strain put upon surgeons' attention during laparoscopic cholecystectomies, automated interface systems that provide meaningful information and feedback without incurring increased cognitive load are necessary. This work outlines (i) a method based on visual data analysis for estimating a surgeon's visual distraction from the surgical monitor and (ii) an approach for using this information to provide unobtrusive reminders to (novice) surgeons during critical sections of the surgical workflow. A large corpus of sensor data was collected during 38 real-world laparoscopic cholecystectomies, and the algorithms were trained and optimized using this data. The algorithm for detecting visual distraction achieved a recognition rate of 93.8%, and a temporal attention-sensitive algorithm for triggering feedback based on thresholds was successfully devised. Further insights into patterns of attention/distraction of surgeons could be derived by means of qualitative analysis.

AB - Due to the large cognitive strain put upon surgeons' attention during laparoscopic cholecystectomies, automated interface systems that provide meaningful information and feedback without incurring increased cognitive load are necessary. This work outlines (i) a method based on visual data analysis for estimating a surgeon's visual distraction from the surgical monitor and (ii) an approach for using this information to provide unobtrusive reminders to (novice) surgeons during critical sections of the surgical workflow. A large corpus of sensor data was collected during 38 real-world laparoscopic cholecystectomies, and the algorithms were trained and optimized using this data. The algorithm for detecting visual distraction achieved a recognition rate of 93.8%, and a temporal attention-sensitive algorithm for triggering feedback based on thresholds was successfully devised. Further insights into patterns of attention/distraction of surgeons could be derived by means of qualitative analysis.

UR - https://ieeexplore.ieee.org/document/8925517

U2 - 10.1109/ACII.2019.8925517

DO - 10.1109/ACII.2019.8925517

M3 - Conference proceedings

SP - 346

EP - 352

BT - 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

PB - IEEE

ER -

Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

EyeControl: Eye-Controlled Machines

MinIAttention: Attention Management in Minimal Invasive Surgery

Cite this

Attention/Distraction Estimation for Surgeons during Laparoscopic Cholecystectomies

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

EyeControl: Eye-Controlled Machines

MinIAttention: Attention Management in Minimal Invasive Surgery

Cite this