Motion optimization and parameter identification for a human and lower-back exoskeleton model
This work addresses the problem of low-back injury prevention for workers or individuals performing lifting tasks, representing an incremental improvement in human-robot interaction design.
The study tackled the challenge of designing a lower-back exoskeleton to reduce injury risk during lifting by developing a computational framework to identify exoskeleton spring parameters, which predicted motions within human torque limits and yielded a considerable reduction in peak and cumulative lower-back loads.
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.