Assist-as-Needed Robotic Rehabilitation Strategy Based on z-Spline Estimated Functional Ability

Shawgi Y. A. Mounis,Fatai Sado,Norsinnira Zainul Azlan

doi:10.1109/access.2020.3019450

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.

Highlights

Stroke is a leading cause of disability world-wide [1], [2]
The functional ability (FA) algorithm uses patients’ quality of movement, and time score in a z-spline approximation function which is much related to the clinical procedure
The results proved that the formulated FA using spline function is successful in estimating the patient’s level of capability accurately as in the real clinical procedure done by therapists

Summary

INTRODUCTION

Stroke is a leading cause of disability world-wide [1], [2]. It significantly reduce patients’ functional ability and performance of activities of daily living (ADL) [3]. Other sensor-based adaptive techniques include the adaptive impedance method proposed by Pérez-Ibarra et al [25] which estimate patients’ contribution from torque and kinematic information during the motion and adapt the robotic assistance based on patient’s performance in a video game Another is the reinforcement learning-based impedance controller that actively reshapes the stiffness of the force-field to the subject’s performance, while providing assistance only when needed. Other examples include the RL framework used in [28] which learns the sensitivity factor of the system model in order to reduce physical human robot interactions, and the AAN controller framework proposed by Obayashi et al [29] that uses a RL algorithm to adjust the stiffness of an IC in order to help subjects learn a dart-throwing task Another noteworthy AAN adaptive control (hierarchical compliance) strategy by Liu et al [30], proposed for the soft ARBOT, and it suggests a method to estimate the subject’s active ankle torque and movement performance by monitoring the subject’s active participation online adapted to the individual’s behavior and ability.

FA ALGORITHM

FA - TORQUE MAPPING ALGORITHM

RESULTS AND DISCUSSIONS

CONCLUSION