Residual Elimination Algorithm Enhancements to Improve Foot Motion Tracking During Forward Dynamic Simulations of Gait

Abstract

Patientspecific gait optimizations capable of predicting posttreatment changes in joint motions and loads could improve treatment design for gaitrelated disorders. To maximize potential clinical utility, such optimizations should utilize fullbody threedimensional patientspecific musculoskeletal models, generate dynamically consistent gait motions that reproduce pretreatment marker measurements closely, and achieve accurate foot motion tracking to permit deformable footground contact modeling. This study enhances an existing residual elimination algorithm (REA) Remy, C. D., and Thelen, D. G., 2009, “Optimal Estimation of Dynamically Consistent Kinematics and Kinetics for Forward Dynamic Simulation of Gait,â€‌ ASME J. Biomech. Eng., 131(3), p. 031005) to achieve all three requirements within a single gait optimization framework. We investigated four primary enhancements to the original REA: (1) manual modification of tracked marker weights, (2) automatic modification of tracked joint acceleration curves, (3) automatic modification of algorithm feedback gains, and (4) automatic calibration of model joint and inertial parameter values. We evaluated the enhanced REA using a fullbody threedimensional dynamic skeletal model and movement data collected from a subject who performed four distinct gait patterns: walking, marching, running, and bounding. When all four enhancements were implemented together, the enhanced REA achieved dynamic consistency with lower marker tracking errors for all segments, especially the feet (mean rootmeansquare (RMS) errors of 3.1 versus 18.4 mm), compared to the original REA. When the enhancements were implemented separately and in combinations, the most important one was automatic modification of tracked joint acceleration curves, while the least important enhancement was automatic modification of algorithm feedback gains. The enhanced REA provides a framework for future gait optimization studies that seek to predict subjectspecific posttreatment gait patterns involving large changes in footground contact patterns made possible through deformable footground contact models.