Human-robot synchrony: flexible assistance using adaptive oscillators

We propose a novel method for movement assistance which is based on adaptive oscillators, i.e. mathematical tools that are capable to extract the high-level features (amplitude, frequency, offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexionextension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps EMG. Importantly, since the oscillator adapted to the movement imposed by the user, the methods flexibly allowed to change the movement pattern and was still efficient during the non-stationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require pre-specifying a reference trajectory and does not require complex signal sensing or single user calibration: The only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.