Predictive Simulations of Gait with Exoskeletons that Alter Energetics

Robotic exoskeletons, designed to augment human locomotion, have the potential to restore function in those with mobility impairments and enhance it in able-bodied individuals. However, optimally controlling these devices, to work in concert with complex and diverse human users, is a challenge. Accurate model simulations of the interaction between exoskeletons and walking humans may expedite the design process and improve control. Here, we use predictive gait simulations to investigate the effect of an exoskeleton that alters the energetic consequences of walking. To validate our approach, we re-created an past experimental paradigm where robotic exoskeletons were used to shift peoples energetically optimal step frequency to frequencies higher and lower than normally preferred. To match the experimental controller, we modelled a knee-worn exoskeleton that applied resistive torques that were either proportional or inversely proportional to step frequency--decreasing or increasing the energy optimal step frequency, respectively. We were able to replicate the experiment, finding higher and lower optimal step frequencies than in natural walking under each respective condition. Our simulated resistive torques and objective landscapes resembled the measured experimental resistive torque and energy landscapes. Individual muscle energetics revealed distinct coordination strategies consistent with each exoskeleton controller condition. Predicted step frequency and energetic outcomes were best achieved by increasing the number of virtual participants (varying whole-body anthropometrics), rather than number of muscle parameter sets (varying muscle anthropometrics). In future, our approach can be used to design controllers in advance of human testing, to help identify reasonable solution spaces or tailor design to individual users.