Nitish Thatte
Carnegie Mellon University

February 03, 2017, Robust and Natural Gait via Neuromuscular Control for Transfemoral Prostheses, Porter Hall A19C

Abstract

We present work towards developing a control method for powered knee and ankle prostheses based on a neuromuscular model of human locomotion. Previous research applying neuromuscular control to simulated biped models and to powered ankle prostheses suggest that this approach can adapt to changes in speed, incline, and rough ground. The improved robustness and generalizability of the approach may arise from its modeling of various physical and neural components of the human neuromuscular system. For example, research has shown that muscular reflexes, such as positive force feedback, can generate human-like compliant leg behavior, that muscle properties such as the force-velocity relationship are important for regulating energy in simplified gait models, and that biarticular structures play an important role in preventing joint over extension during compression of multi-segmented legs. While research has demonstrated that these components individually contribute to the robustness of simplified legged systems, it is unclear if their combined effect when applied to prostheses will help improve the amputee gait robustness. Therefore, the goal of this thesis is to investigate how to apply neuromuscular control to a powered knee and ankle prosthesis and quantify the robustness of amputee gait under this control strategy.

To further motivate our use of neuromuscular control, we first model and simulate an amputee walking with a powered prosthesis and perform optimizations to obtain parameters for the proposed neuromuscular control and the established impedance control method for prostheses. We find that neuromuscular control significantly improves the simulated amputee’s gait robustness on uneven ground. To confirm that this improved robustness is evident on a real system, we design and build a powered knee and ankle prosthesis that features powerful actuators capable of producing sufficient torque and speed for trip recovery and series elasticity to enable accurate reproduction of the neuromuscular model torques. In parallel, we have investigated methods to optimize prosthesis control parameters for specific subjects via qualitative feedback. In completed work, we present and evaluate the performance of a Bayesian optimization method that works with a user’s preferences between pairs of parameters.

In our proposed work we intend to implement the neuromuscular control on the completed prosthesis hardware and evaluate its robustness properties. We hypothesize that the proposed control will allow amputees to more quickly recover from disturbances. Furthermore, we will extend our method for optimizing control parameters to include more forms of qualitative feedback and explicit consideration of user adaptation over time. Finally, we propose to improve the neuromuscular control’s response to disturbances during swing via explicit detection, classification, and execution of recovery strategies.

Thesis Committee

Hartmut Geyer, Chair
Steven Collins
Chris Atkeson
Elliott Rouse, Northwestern University