A framework for analysis of surgeon arm posture variability in robot-assisted surgery

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Abstract
Teleoperated robot-assisted surgery (RAS) provides surgeons with improved dexterity, movement control, and visualization in comparison to standard minimally invasive surgery. However, there exists little quantitative understanding of the motor performance of human operators in RAS. Models of how users control their movements and how this control relates to surgical performance could provide inspiration for new robot or human interface designs, as well as more targeted training methods. Toward this end, we present a framework for the analysis of surgeon arm posture variability based on the uncontrolled manifold (UCM) concept, a method used in the study of human motor control for testing hypotheses about the coupling of control and task variables. We partition users' joint angle variability into variability that does and does not result in hand trajectory change. In a preliminary study applying this framework, we explored how expert and novice operators control planar reaching and reversal movements when moving freehand as well as using a teleoperated RAS system. We show that only movements in task-relevant directions are stabilized by the coordination of joint angles, and that this stabilization is stronger for expert movements than novice movements. We also show that stabilization is stronger in freehand than teleoperated movements, especially for the expert. These preliminary findings suggest that the proposed framework can be useful for: (1) assessment of teleoperator design and control that reveals how design parameters affect the ability of the user to exploit the UCM for stabilization of hand movement, and (2) skill assessment in RAS.

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