Title: Motion optimization and parameter identification for a human and lower-back exoskeleton model,
Abstract: Designing an exoskeleton to reduce the risk of low-back injury during lifting
is challenging. Computational models of the human-robot system coupled with
predictive movement simulations can help to simplify this design process. Here,
we present a study that models the interaction between a human model actuated
by muscles and a lower-back exoskeleton. We provide a computational framework
for identifying the spring parameters of the exoskeleton using an optimal
control approach and forward-dynamics simulations. This is applied to generate
dynamically consistent bending and lifting movements in the sagittal plane. Our
computations are able to predict motions and forces of the human and
exoskeleton that are within the torque limits of a subject. The identified
exoskeleton could also yield a considerable reduction of the peak lower-back
torques as well as the cumulative lower-back load during the movements. This
work is relevant to the research communities working on human-robot
interaction, and can be used as a basis for a better human-centered design
process.