Alternative robotic control methods that account for system compliance decrease the errors in ligament tensions computed using the superposition method

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Superposition testing is a method to quantify in situ ligament tension by measuring the change in joint loads before and after ligament sectioning. Despite its widespread use, the traditional robot control method used in superposition testing may introduce errors because it does not account for system compliance when prescribing joint kinematics. Therefore, our objective was to quantify the errors in superposition-computed tensions using the robot control method, and to validate a novel motion capture control method that accounts for system compliance by measuring joint kinematics using optical motion capture sensors fixed to the bones. Using our robotic testing system, we performed superposition testing to quantify lateral collateral ligament tension in five cadaveric knees during prescribed varus and external rotation loading trajectories using both robot control and motion capture control. We computed the errors between superposition-computed tensions and gold-standard ligament tensions measured by an in-series load cell. Compared to robot control, we found that motion capture control significantly decreased the errors in superposition-computed tensions in the lateral collateral ligament during both varus (from -92 ± 30 N to -27 ± 21 N) and external rotation (from -27 ± 19 N to -10 ± 9 N) loading by decreasing errors in joint kinematics and bone positions. Thus, we recommend implementing a control method that accounts for system compliance to achieve low errors in superposition-computed tensions for a particular robotic testing system and ligament type. With proper reporting of errors, superposition testing will continue to be a valuable experimental method to quantify in situ ligament tensions.

Article activity feed