Energy-optimal human walking with feedback-controlled robotic prostheses: a computational study

This webpage supports the following article about a computational formalism for designing robotic prosthesis through an optimization-based prediction of human movement during a simultaneously optimized prosthesis actuation.


Citation: Matthew Handford and Manoj Srinivasan.
Energy-optimal human walking with feedback-controlled robotic prostheses: a computational study.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, accepted, in press, 2018.

DOI: 10.1109/TNSRE.2018.2858204

Authors: Matthew Handford and Manoj Srinivasan

Article PDF: Article + Supplementary Information



Lower limb amputees, on average, experience re- duced mobility and higher metabolic rates than non-amputees. It may be possible to improve their mobility and metabolic rate with an optimized robotic prosthesis. However, we still need to determine what specific parameters we should optimize. Here, we use large-scale trajectory optimization on a simulated transtibial amputee with a robotic prosthesis to obtain metabolic energy- minimizing walking gaits with multiple prosthesis controllers. Using this simulation, we were able to obtain trends in the energetics and kinematics for various levels of prosthesis work. We find that the net metabolic rate has a roughly quadratic relationship with the net prosthesis work rate. This simulation predicts that metabolic rate could be reduced below that of a non-amputee, although such gaits are highly asymmetric and not seen in experiments with amputees. Walking simulations with left-right symmetry in kinematics or ground reaction forces have higher metabolic rates than asymmetric gaits, suggesting a potential reason for asymmetries in amputee walking. Our findings suggest that a computational framework such as the one presented here could augment the experimental approaches to prosthesis design iterations, although quantitatively accurate prediction of experiments from simulation remains an open problem.


Matthew Handford and Manoj Srinivasan were supported by NSF CMMI grant 1300655.