Human–Exoskeleton Interaction Force Estimation in Indego Exoskeleton
Mohammad Shushtari, Arash Arami
Abstract
Accurate interaction force estimation can play an important role in optimizing human–robot interaction in an exoskeleton. In this work, we propose a novel approach for the system identification of exoskeleton dynamics in the presence of interaction forces as a whole multibody system without imposing any constraints on the exoskeleton dynamics. We hung the exoskeleton through a linear spring and excited the exoskeleton joints with chirp commands while measuring the exoskeleton–environment interaction force. Several structures of neural networks were trained to model the exoskeleton passive dynamics and estimate the interaction force. Our testing results indicated that a deep neural network with 250 neurons and 10 time–delays could obtain a sufficiently accurate estimation of the interaction force, resulting in an RMSE of 1.23 on Z–normalized applied torques and an adjusted R2 of 0.89.