Experimental and simulation-based assessment of the human postural response to sagittal plane perturbations with localized muscle fatigue and aging

This research was motivated by occupational falls, which are one of the leading causes of fatalities in skilled labor divisions. The effects of localized muscle fatigue (LMF) on surrogate measures of postural sway are well-established. This is significant since these increases have been linked to elevated risk of falls, and workers with increased risks of falling fatality frequently engage in fatiguing tasks.;An initial study was conducted to investigate the effects of LMF and aging on balance recovery from postural perturbations without stepping. Sagittal plane perturbations were administered to young and older individuals before and after fatiguing exercises. Measures of balance recovery (BR) were based on the center of mass (COM) and center of pressure (COP) trajectories and the maximum perturbation that could be withstood. Changes in BR measures were consistent with an LMF- and aging-induced decrement in recovering from the perturbations.;The second study investigated the effects of aging and LMF on the neural control of upright stance during small postural perturbations. Small magnitude postural perturbations were administered to young and older individuals before and after fatiguing exercises. A single degree of freedom (DOF) human body model was developed that accurately simulated the experimental data. Feedback gains and time-delay were optimized for each participant, and a delay margin analysis was performed to assess system robustness. Results indicated that older individuals had a longer "effective" time-delay and exhibited greater reliance on afferent velocity information. No changes in feedback controller gains, time-delay, or delay margins were found with LMF in either age group.;The final study investigated using a nonlinear controller to simulate responses to large magnitude postural perturbations. A three DOF model of the human body was developed and controlled with the state-dependent Riccati equation (SDRE). Parameters of the SDRE were optimized to fit the experimentally recorded kinematics. Unlike other nonlinear controllers, the SDRE provides meaningful parameters for interpretation in the system identification. The SDRE approach was successful at stabilizing the dynamical system; however, accurate results were not obtained. Explanations for this are presented along with an alternative formulation to the time-delayed optimal control problem using Roesser state space equations.