Assist-as-needed robotic rehabilitation strategy based on z-spline estimated functional ability

Abstract

Assist-as-needed (AAN) robotic-rehabilitation therapy is an active area of research which aims to promote neuroplasticity and motor coordination through active participation in functional task. A key component of this strategy is to provide robotic assistance to patients only when needed. To achieve this, accurate estimation of patients' movement/functional ability (FA) is required to evaluate patients' need for robotic assistance and to provide the required amount of assistance, which is still a significant challenge to AAN robotic-rehabilitation therapy. This study proposes an AAN technique based on a new Functional Activity Spline Function (FASF) to estimate patients' FA and to adapt robotic assistance. The FASF is formulated using z-spline curve to estimate patients' movement ability based on the quality-of-movement and the time score of the patient in each functional task. A Linear Quadratic Gaussian Integral (LQGi) torque controller is applied with a FASF-to-torque mapping algorithm to physically provide low-level torque assistance on the elbow/shoulder joints. Fifteen patients were involved in the experimental study which consists of two tasks: (Task1) a pick-and-place task and (Task2) a table-to-mouth reaching task. The results showed that the proposed ANN control strategy has successfully estimated the patients' FA consistently with high repeatability, and able to provide the robotic assistance according to the patients' needs in the task. For different levels of impairment, the average percent-torque assistance across trials relative to the highest possible assistive torque are within the range of 5.43%-24.85% (for the mildly impaired) and 75.14%-97.14% (for the severely impaired) patents in both reaching task consistent with their FA estimation.