Automatic virtual impedance adaptation of a knee exoskeleton for personalized walking assistance

Abstract

This paper attempts to address the problem of online modulation of virtual impedance for an assistive robot based on real-time gait and activity measurements to personalize the assistance for different users at different states. In this work, smart shoes and inertial sensors are introduced to measure ground contact forces and knee joint kinematics, respectively. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) based on real-time activity recognition and gait phase detection. The activities considered in this paper are level, uphill, and downhill walking. A Gaussian mixture model (GMM) is employed to map the fuzzy likelihood of various activities and gait phases to the desired virtual impedance of the KAD. The prior estimate of virtual impedance is defined using human knee impedance identified with the walking data collected on different users. The AIT approach is integrated into the high-level impedance-based controller of the KAD for assistance during the stance phase. Finally, to evaluate the benefit of the proposed algorithm in stance phase, an EMG sensor is placed on the vastus medialis muscle group of three participants. The proposed approach is compared with two baseline approaches: constant impedance and finite state machine, and the results demonstrate that the profiles of impedance parameters and robot assistive torque are smoother and the muscle activity of vastus medialis is reduced. It is also noticed that the participants reduce their step lengths and increase walking cadence with assistance from the KAD.