AUTHOR=Golkar Mahsa A. , Sobhani Tehrani Ehsan , Kearney Robert E. TITLE=Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions JOURNAL=Frontiers in Computational Neuroscience VOLUME=11 YEAR=2017 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2017.00035 DOI=10.3389/fncom.2017.00035 ISSN=1662-5188 ABSTRACT=
Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from