Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

TL;DR

This paper presents a systematic, model-based control method enabling powered prostheses to emulate human ankle push-off and joint trajectories, improving natural gait without subject-specific tuning.

Contribution

It introduces a force-based nonlinear control approach using multi-contact models to achieve human-like walking on prostheses without individual tuning.

Findings

Better tracking of human kinematic trajectories compared to traditional methods.

Successful emulation of ankle push-off in prostheses for two subjects.

No subject-specific tuning required for the control approach.

Abstract

Ankle push-off largely contributes to limb energy generation in human walking, leading to smoother and more efficient locomotion. Providing this net positive work to an amputee requires an active prosthesis, but has the potential to enable more natural assisted locomotion. To this end, this paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis for 2 subjects. In particular, we leverage model-based control approaches for dynamic bipedal robotic walking to develop a systematic method to realize human-like walking on a powered prosthesis that does not require subject-specific tuning. We begin by synthesizing an optimization problem that yields gaits that resemble human joint trajectories at a kinematic level, and realize these gaits on a prosthesis through a control Lyapunov function based nonlinear controller that responds to real-time ground reaction forces and interaction forces with the human. The proposed controller is implemented on a prosthesis for two subjects without tuning between subjects, emulating subject-specific human kinematic trends on the prosthesis joints. These experimental results demonstrate that our force-based nonlinear control approach achieves better tracking of human kinematic trajectories than traditional methods.