Parameter optimization for passive spinal exoskeletons based on experimental data and optimal control
Designing an exoskeleton to reduce the risk of low-back injury and to alleviate low-back pain is challenging. Individualizing the support provided to each user is an additional challenge. Subject-specific models of the human body and the exoskeleton combined with movement analysis can support the design process. Here, we utilize experimental data of lifting motions of several subjects to optimize spring characteristics of a spinal exoskeleton. We create subject-specific models of the human body and kinematically couple them with models of spinal exoskeletons. An optimal control approach is used to fit the motion of this combined model to that recorded from experiments, with the exoskeleton spring characteristics as free parameters to be optimized. Our results indicate that the combined human-exoskeleton model is able to track the recorded motions well. Based on the individual lifting styles, the support provided by the exoskeleton as well as the optimal spring stiffness vary across subjects. The computed interaction forces and moments between the human and the exoskeleton, are high on the exoskeleton pelvis module, as well as in the normal direction on the thigh module. The exoskeleton with the optimized spring characteristics could also provide a reduction of the lumbar and hip moments. This framework can be taken as a basis for virtual design and testing of exoskeletons before prototyping, and may be applied to various robotic-assisted human motions.