Intuitive Clinician Control Interface for a Powered Knee-Ankle Prosthesis: A Case Study

Abstract

This paper presents a potential solution to the challenge of configuring powered knee-ankle prostheses in a clinical setting. Typically, powered prostheses use impedance-based control schemes that contain several independent controllers which correspond to consecutive periods along the gait cycle. This control strategy has numerous control parameters and switching rules that are generally tuned by researchers or technicians and not by a certified prosthetist. We propose an intuitive clinician control interface (CCI) in which clinicians tune a powered knee-ankle prosthesis based on a virtual constraint control scheme, which tracks desired periodic joint trajectories based on a continuous measurement of the phase (or progression) of gait. The interface derives virtual constraints from clinician-designed joint kinematic trajectories. An experiment was conducted in which a certified prosthetist used the control interface to configure a powered knee-ankle prosthesis for a transfemoral amputee subject during level-ground walking trials. While it usually takes engineers hours of tuning individual parameters by trial and error, the CCI allowed the clinician to tune the powered prosthesis controller in under 10 min. This allowed the clinician to improve several amputee gait outcome metrics, such as gait symmetry. These results suggest that the CCI can improve the clinical viability of emerging powered knee-ankle prostheses.

A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis

“…The addition of tunable parameters provides the ability of easily adopting various walking styles, such as different step lengths or pushoff initiation points, which was not possible in [6]. See [49] for an example of using these tunable parameters in an intuitive clinical control interface.…”

Analysis of Continuously Varying Kinematics for Prosthetic Leg Control Applications

“…This finding is congruent with previous work, as the basis method (and the comparable finite state machine used in this study) are designed to predict average kinematics, with no accommodation for stride-to-stride variance or the specific subject using the prosthesis. Previous work has shown that providing averagesubject kinematics is still an effective method for improving amputee gait [13], [19], and that it is possible, and practical, to tune the kinematic trajectories of individual tasks to better match a subject in a clinical setting [18]. Concurrently, recent work has shown that the continuous format of the basis model can be leveraged to improve the tuning of all tasks, given one tuned task [58].…”

“…Both of these algorithms share the phase and subject individuality kinematic error factors, which are the largest sources of the overall error. If these two factors can be reduced, by tuning and improving the phase variable calculation and employing subject-specific tuning techniques [18], [58], the relative advantage of the basis method would be more significant. Lastly, three of the test tasks in this study matched the available tasks in the state machine exactly.…”

“…Then, classification algorithms are used to determine what available state is closest to the task the user is currently performing, and that set of parameters is selected [15]- [17]. In the case of prosthetic trajectoryfollowing controllers, which enforce predefined kinematic trajectories for the knee and ankle joint (usually based on experimentally recorded average subject trajectories [18]), a high-level FSM task controller is used to switch between task-dependent trajectories [19]. Changing these trajectories to perform new tasks is important for providing normative biomechanical trends like increased ankle work and power at higher speeds, and greater power production in both joints while walking uphill [19].…”

Continuous instinct control for powered knee-ankle prostheses

“…In the absence of an emergency, the fixed movement pattern of a person is an instinctive reaction that does not require real-time brain control. The paper proposes the CCI interface adjusting the joint trajectory [16].Therefore, this paper proposes an instinctive human joint trajectory. The joint trajectory is obtained by a Bezier piecewise fitting using the joint trajectory of a healthy individual.…”