Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention

Yi Zheng, Grey Leonard, Herbert Zeh, Ann Majewicz Fey

Research output: Contribution to journalArticlepeer-review

Abstract

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 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 (p≤0.0001); Velocity (p≤0.015) and jerk (p≤0.001) 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 (p=0.0000). In general, we found that the jerk on nondominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively.

Original languageEnglish (US)
Article number2241006
JournalJournal of Medical Robotics Research
DOIs
StateAccepted/In press - 2022

Keywords

  • deep learning
  • Laparoscopic surgery
  • motion analysis
  • surgical stress

ASJC Scopus subject areas

  • Biomedical Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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