Neural network based patient recovery estimation of a PAM-based rehabilitation robot

Abstract

Rehabilitation robots have shown a promise in aiding patient recovery by supporting them in repetitive, systematic training sessions. A critical factor in the success of such training is the patient’s recovery progress, which can guide suitable treatment plans and reduce recovery time. In this study, a neural network-based approach is proposed to estimate the patient’s recovery, which can aid in the development of an assist-as-needed training strategy for the gait training system. Experimental results show that the proposed method can accurately estimate the external torques generated by the patient to determine their recovery. The estimated patient recovery is used for an impedance control of a 2-DOF robotic orthosis powered by pneumatic artificial muscles, which improves the robot joint compliance coefficients and makes the patient more comfortable and confident during rehabilitation exercises.