In this paper, we present an approach to using the impedance-controlled "anklebot" for task-oriented locomotor training after stroke. Our objective is to determine the feasibility of using the anklebot as a gait training tool by increasing the contribution of the paretic ankle in walking function. Underlying our training approach is a novel gait event-triggered, sub-task control algorithm that enables precise timing of robotic assistance to key functional deficits of hemiparetic gait, as well as sagittal-plane biomechanical models capable of predicting necessary levels of robotic support specific to the nature and severity of deficits. These features may facilitate customizability of assisted walking to individual gait deficit profiles. As with our previous studies, we employ an adaptive approach in that, training parameters are incrementally progressed towards those of more normal gait depending on subject performance and tolerance. Here, we present and validate the sub-event detection and sub-task control method, the biomechanical models for the swing and landing phases of gait, and as proof-of-concept, pilot data to demonstrate initial efficacy of the approach.
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