Expertise, Teleoperation, and Task Constraints Affect the Speed–Curvature–Torsion Power Law in RAMIS

Abstract

Quantitative characterization of surgical movements can improve the quality of patient care by informing the development of new training protocols for surgeons, and the design and control of surgical robots. Here, we focus on the relationship between the speed of movement and its geometry that was extensively studied in computational motor control. In three-dimensional movements, this relationship is defined by a family of speed–curvature–torsion power laws, such as the one-sixth power law. We present a novel characterization of open and teleoperated suturing movements using the speed–curvature–torsion power-law analysis. We fitted the gain and the exponents of this power law to suturing movements of participants with different levels of surgical experience in open (using sensorized forceps) and teleoperated (using the da Vinci Research Kit/da Vinci Surgical System) conditions from two different datasets. We found that expertise and teleoperation significantly affected the gain and exponents of the power law, and that there were large differences between different segments of movement. These results confirm that the relationship between the speed and geometry of surgical movements is indicative of surgical skill, open a new avenue for understanding the effect of teleoperation on the movements of surgeons, and lay the foundation for the development of new algorithms for automatic segmentation of surgical tasks.