Model-Based Sensorimotor Integration for Multi-Joint Control: Development of a Virtual Arm Model

Abstract

An integrated, sensorimotor virtual arm (VA) model has been developed and validated for simulation studies of control of human arm movements. Realistic anatomical features of shoulder, elbow and forearm joints were captured with a graphic modeling environment, SIMM. The model included 15 musculotendon elements acting at the shoulder, elbow and forearm. Muscle actions on joints were evaluated by SIMM generated moment arms that were matched to experimentally measured profiles. The Virtual Muscle (VM) model contained appropriate admixture of slow and fast twitch fibers with realistic physiological properties for force production. A realistic spindle model was embedded in each VM with inputs of fascicle length, gamma static (gamma(stat)) and dynamic (gamma(dyn)) controls and outputs of primary (I(a)) and secondary (II) afferents. A piecewise linear model of Golgi Tendon Organ (GTO) represented the ensemble sampling (I(b)) of the total muscle force at the tendon. All model components were integrated into a Simulink block using a special software tool. The complete VA model was validated with open-loop simulation at discrete hand positions within the full range of alpha and gamma drives to extrafusal and intrafusal muscle fibers. The model behaviors were consistent with a wide variety of physiological phenomena. Spindle afferents were effectively modulated by fusimotor drives and hand positions of the arm. These simulations validated the VA model as a computational tool for studying arm movement control. The VA model is available to researchers at website http://pt.usc.edu/cel .

Conclusions on motor control depend on the type of model used to represent the periphery

“…This second-order model is of course still a simplification of the higher order dynamics of the periphery, which might affect conclusions based on such a model (Kistemaker and Rozendaal (2011). Still other researchers model the nonlinear excitation and contraction dynamics and the tendon compliance (e.g., Nijhof and Kouwenhoven 2000;van Soest and Bobbert 1993) and the most elaborate models also incorporate the muscle spindle feedback (e.g., Kistemaker et al 2006;Song et al 2008).…”

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization

“…Other optimization models have been developed for posture control or ergonomics analyses [10][11][12]. More recently, neuro-muscular aspects have also been investigated [13,14]. Several shoulder models are thus available to estimate muscle and joint forces, but there is some discrepancy in their prediction of joint reaction force.…”

Modelling muscle spindle dynamics for a proprioceptive prosthesis

“…4) is similar to that used in the Virtual Arm model [5] and can be broken down into two main stages: (1) biomechanical models to estimate muscle length changes as the elbow joint is flexed or extended, and (2) mathematical models of the muscle spindle to convert these muscle lengths into estimates of spindle firing patterns.…”