Unbiased estimation of human joint intrinsic mechanical properties during movement

Abstract

The overall mechanical properties of a joint are generated by a combination intrinsic (mechanical) and reflex (neural) mechanisms. Nevertheless, many methods for estimating joint mechanical properties have used a linear dynamic model whose parameters are commonly related to the joint inertial and visco-elastic properties. Such mechanical models cannot account for torques due to reflex mechanisms and consequently fitting them to data containing reflex torques can give biased results. This paper addresses this issue in two ways. First, using simulation studies, it demonstrates that fitting linear dynamic models in the presence of reflex torques will indeed provide biased estimates of intrinsic joint properties; the bias is significant for small reflex torques and increases proportionally with reflex torque magnitude. Second, it develops and validates a novel approach to accurately estimate the time-varying, intrinsic mechanical properties of a joint in the presence of reflex torques. The approach involves applying small position perturbations to the joint trajectory and then applying novel mathematical models and system identification techniques to analytically separate the measured total joint torque into its intrinsic and reflex components. The method first estimates a non-parametric, reflex electromyography-torque model, and uses it to predict the reflex torques which is subtracted from the total torque. Then, it estimates a non-parametric, linear, and time-varying model of the intrinsic mechanical properties from the residuals. Simulation results demonstrate that the new approach accurately tracks time-varying joint intrinsic mechanical properties during movement independently of the reflex torque magnitude. The new algorithm will be a useful tool in the study of motor control, as it supports the unbiased estimation of joint intrinsic mechanical properties during movement in the presence of reflex torques.