Gradient-specified optimization based on muscle surface mesh and moment arm as an effect-oriented approach of automated musculotendon path modeling

Abstract

There is more to musculotendon path modeling than aligning a cable to reflect the geometric features of a musculotendon unit. From the perspective of simulation accuracy, the key is to replicate the length- and moment arm-joint angle relations of the target muscle. In this study, we propose an effect-oriented approach of automated path modeling, via hybrid calibration based on muscle surface mesh and moment arm. The task is formulated as a least-squares optimization problem with a threefold objective for the path to: (1) pass through multiple ellipses representing muscle cross-sections, (2) yield moment arms that match experimental measurements, and (3) yield moment arms in the expected functional directions. We demonstrate the performance of our optimization framework with the musculoskeletal surface mesh from the Visible Human Male and moment arm datasets from literature--producing 42 paths that are anatomically realistic and biomechanically accurate within 20.1 minutes. Our optimization framework is gradient-specified, which is faster and more accurate than using the default numerical gradient, making it applicable for large-scale subject-specific uses.