Feasibility of an optimal EMG-driven adaptive impedance control applied to an active knee orthosis.

This paper deals with EMG-driven torque estimation and optimal adaptive impedance control during robot-aided rehabilitation. In this preliminary and feasibility study, the proposed framework was evaluated considering an active knee orthosis and only healthy subjects. First, a simplified and optimized musculoskeletal model is used to compute the estimate of user’s torque considering electromyographic (EMG) signals taken from selected muscles acting during flexion and extension movements. The model optimization is performed by comparing the estimated torque from EMG with the torque generated by the inverse dynamics tool of the OpenSim software. As an alternative solution, a multilayer perceptron neural network (NN) is proposed to map the EMG signals to the user’s torque. The proposed approaches are evaluated by a set of healthy subjects wearing the knee orthosis and performing a protocol created for user–robot interaction analysis. Then, an EMG-driven adaptive impedance control is proposed to improve the user participation during the rehabilitation session. The approach is based on an optimal solution which considers the position error and the robot assistance level. The experimental results indicate the use of EMG signals is feasible for adaptive control strategies, taking into account the current condition of the user and optimizing the robot assistance.