PREDICTIVE MODELS FOR ESTIMATION OF THE HUMAN STANCE EQUILIBRIUM PARAMETERS USING INVERSE DYNAMICS AND RESPONSE SURFACE METHOD

Abstract

Activation of the muscles and reaction forces of the joints rely on the body posture. The aim of the present paper is to investigate the relationship between the kinematics of static postures and muscle activation and joint forces by means of predictive regression models. To cover a reasonable number of postures and muscle recruitment patterns in forward inclinations, 4096 postures were analyzed. The response surface method was used to estimate the results of optimization-based inverse dynamics analysis. Two sorts of input variables (three angular positions of the lower limb joints and optimized muscular activation levels) and two model responses (muscle activation and joint force) were designed. The predictive models showed adequate goodness-of-fit in average ([Formula: see text]). The predictive models that applied on the feasible balanced postures revealed considerable reliance of the biomechanical efforts on the postural angles. The ankle force was majorly supported by activation of the calf muscles as [Formula: see text] (kN). The knee and the hip joint were dominantly influenced by the hamstring activation. Quantitative assessment of biomechanical parameters in the balanced standing postures may help researchers in finding standing information by knowing one type of experimental data such as the kinematic angles or the muscle electromyography.