Motion optimization and parameter identification for a human and lower-back exoskeleton model

TL;DR

This paper develops a computational framework to optimize exoskeleton parameters and simulate human-exoskeleton interactions, aiming to reduce lower-back injury risk during lifting tasks.

Contribution

It introduces a novel optimal control approach for identifying exoskeleton spring parameters within a dynamic human model.

Findings

Predicted human and exoskeleton motions within torque limits.

Significant reduction in peak lower-back torques.

Decreased cumulative lower-back load during lifting.

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.