Biokinematic Control Strategy for Rehabilitation Exoskeleton Based on User Intention

Abstract

Rehabilitation exoskeletons require a control interface for the direct transfer of mechanical power and exchange of information in order to assist the patient in his/her movements. By using co-contraction indexes (CCI), it is possible to accurately characterize human movement and joint stability. But when dealing with human movement disorders, no existing index allows to achieve neuro-motor control with bio-kinematic sensors. Thus, we propose a neuro-motor interactive method for lower-body exoskeleton control. A novel dynamic index called neuro-motor index (NMI) is introduced to estimate the relation between muscular co-contraction derived from electromyography signals (EMG) and joint angles. To estimate the correlation in the state space and enhance the precision of the NMI, we describe an estimation method relying on a two-way analysis of canonical correlation (CCA). A thorough assessment is presented, by conducting two studies on control subjects and on patients with abnormal gait in a medical environment. i) An offline study on control patients showed that NMI captures the complex variation induced by changing walking speed more accurately than CCI, ii) an online study, applied on successive gait cycles of patients with abnormal walk indicates that the existing CCI have a low accuracy related with joint angles while it is significantly higher with NMI.