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  • Accepted Manuscript: Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention

This content will become publicly available on June 1, 2023

Title: Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention
It has been shown that intraoperative stress can have a negative effect on surgeon surgical skills during laparoscopic procedures. For novice surgeons, stressful conditions can lead to significantly higher velocity, acceleration, and jerk of the surgical instrument tips, resulting in faster but less smooth movements. However, it is still not clear which of these kinematic features (velocity, acceleration, or jerk) is the best marker for identifying the normal and stressed conditions. Therefore, in order to find the most significant kinematic feature that is affected by intraoperative stress, we implemented a spatial attention-based Long Short-Term Memory (LSTM) classifier. In a prior IRB approved experiment, we collected data from medical students performing an extended peg transfer task who were randomized into a control group and a group performing the task under external psychological stresses. In our prior work, we obtained “representative” normal or stressed movements from this dataset using kinematic data as the input. In this study, a spatial attention mechanism is used to describe the contribution of each kinematic feature to the classification of normal/stressed movements. We tested our classifier under Leave-One-User-Out (LOUO) cross-validation, and the classifier reached an overall accuracy of 77.11% for classifying “representative” normal and stressed movements using more » kinematic features as the input. More importantly, we also studied the spatial attention extracted from the proposed classifier. Velocity and acceleration on both sides had significantly higher attention for classifying a normal movement ([Formula: see text]); Velocity ([Formula: see text]) and jerk ([Formula: see text]) on nondominant hand had significant higher attention for classifying a stressed movement, and it is worthy noting that the attention of jerk on nondominant hand side had the largest increment when moving from describing normal movements to stressed movements ([Formula: see text]). In general, we found that the jerk on nondominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively. « less
Authors:
; ; ;
Award ID(s):
2109635
Publication Date:
NSF-PAR ID:
10380014
Journal Name:
Journal of Medical Robotics Research
Volume:
07
Issue:
02n03
ISSN:
2424-905X
Sponsoring Org:
National Science Foundation
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