Using human-in-the-loop optimization for guiding manual prosthesis adjustments: a proof-of-concept study

Introduction: Human-in-the-loop optimization algorithms have proven useful in optimizing complex interactive problems, such as the interaction between humans and robotic exoskeletons. Specifically, this methodology has been proven valid for reducing metabolic cost while wearing robotic exoskeletons. However, many prostheses and orthoses still consist of passive elements that require manual adjustments of settings. Methods: In the present study, we investigated if human-in-the-loop algorithms could guide faster manual adjustments in a procedure similar to fitting a prosthesis. Eight healthy participants wore a prosthesis simulator and walked on a treadmill at 0.8 ms^-1 under 16 combinations of shoe heel height and pylon height. A human-in-the-loop optimization algorithm was used to find an optimal combination for reducing the loading rate on the limb contralateral to the prosthesis simulator. To evaluate the performance of the optimization algorithm, we used a convergence criterium. We evaluated the accuracy by comparing it against the optimum from a full sweep of all combinations. Results: In five out of the eight participants, the human-in-the-loop optimization reduced the time taken to find an optimal combination; however, in three participants, the human-in-the-loop optimization either converged by the last iteration or did not converge. Discussion: Findings from this study show that the human-in-the-loop methodology could be helpful in tasks that require manually adjusting an assistive device, such as optimizing an unpowered prosthesis. However, further research is needed to achieve robust performance and evaluate applicability in persons with amputation wearing an actual prosthesis.