Feed-forward support of human walking

Abstract

In this paper we present a method to estimate a feed-forward torque to support human walking. Our method does not require a model of human walking or pre-recorded gait data. Instead we use muscle EMG as a learning signal to construct a feed-forward torque with an adaptive algorithm. As a proof of principle a pilot study on a single muscle was performed to test the method. The pilot showed that for the biceps femoris the EMG could be reduced by 15 percent up to 64 percent. The aim of the study is to gain more insight in how humans can be supported by robotic exoskeletons, and to apply the knowledge in future exoskeleton designs.