A Comparison of Expert Ratings and Marker-Less Hand Tracking along OSATS-Derived Motion Scales

David P. Azari, Brady L. Miller, Brian V. Le, Jacob A. Greenberg, Reginald C. Bruskewitz, Kristin L. Long, Guanhua Chen, Robert G. Radwin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objective: This study creates linear and generalized additive models (GAMs) of video-recorded two-dimensional hand motion (synonymously referred to as hand movements or hand kinematics) to predict expert-rated performance along a series of surgical motion scales. Background: Surgical performance assessments are costly and time consuming. Automatically quantifying hand motion may offload some burden of surgical coaching and intervention by automatically collecting features of psychomotor performance. Methods: Five experts rated anonymized video clips of benchtop suturing and tying tasks (n = 219) along four visual-analog (0-10) performance scales: fluidity of motion, motion economy, tissue handling, and hand coordination. Custom software tracked both participant hands across successive video frames and populated a robust feature set to train a series of predictive models to reproduce the expert ratings. Results: A GAM (which accounts for nonlinear effects) predicted fluidity of motion ratings with slope = 0.71, intercept = 1.98, and ${{\boldsymbol{R}}^2}$ = 0.77 for clinicians of different experience levels. Fluidity of motion and motion economy models outperformed those created to predict hand coordination and tissue handling ratings. Conclusions: Hand motion tracking may not address all contextual features of surgical tasks. Future work will explore how well simulation-based models extrapolate to more dynamic settings of the operating room.

Original languageEnglish (US)
Article number9286874
Pages (from-to)22-31
Number of pages10
JournalIEEE Transactions on Human-Machine Systems
Volume51
Issue number1
DOIs
StatePublished - Feb 2021
Externally publishedYes

Keywords

  • Cameras
  • medicine
  • motion estimation
  • position measurement
  • surgical instruments
  • video recording
  • video signal processing

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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