Lower Limb Estimation from Sparse Landmarks using an Articulated Shape Model

Abstract

Rapid generation of lower limb musculoskeletal models is essential for patient-specific gait modeling. Motion-capture is a routine part of gait assessment but contains relatively sparse geometric information. We present an articulated statistical shape model of the lower limb that estimates realistic bone geometry, pose, and muscle attachment regions from seven commonly used motion-capture markers. Our method obtained a lower (p=0.02) surface error of 4.5 mm RMS compared to 8.5 mm RMS using standard isotropic scaling, and was more robust, converging in all 26 test cases compared to 20 for isotropic scaling.