Are patient-specific joint and inertial parameters necessary for accurate inverse dynamics analyses of gait?

Variations in joint parameter (JP) values (axis positions and orientations in body segments) and inertial parameter (IP) values (segment masses, mass centers, and moments of inertia) as well as kinematic noise alter the results of inverse dynamics analyses of gait. Three-dimensional linkage models with joint constraints have been proposed as one way to minimize the effects of noisy kinematic data. Such models can also be used to perform gait optimizations to predict post-treatment function given pre-treatment gait data. This study evaluates whether accurate patient-specific JP and IP values are needed in three-dimensional linkage models to produce accurate inverse dynamics results for gait. The study was performed in two stages. First, we used optimization analyses to evaluate whether patient-specific JP and IP values can be calibrated accurately from noisy kinematic data, and second, we used Monte Carlo analyses to evaluate how errors in JP and IP values affect inverse dynamics calculations. Both stages were performed using a dynamic, 27 degrees-of-freedom, full-body linkage model and synthetic (i.e., computer generated) gait data corresponding to a nominal experimental gait motion. In general, JP but not IP values could be found accurately from noisy kinematic data. Root-mean-square (RMS) errors were 3 degrees and 4 mm for JP values and 1 kg, 22 mm, and 74 500 kg * mm2 for IP values. Furthermore, errors in JP but not IP values had a significant effect on calculated lower-extremity inverse dynamics joint torques. The worst RMS torque error averaged 4% bodyweight * height (BW * H) due to JP variations but less than 0.25% (BW * H) due to IP variations. These results suggest that inverse dynamics analyses of gait utilizing linkage models with joint constraints should calibrate the model's JP values to obtain accurate joint torques.