Towards the Analysis of Movement Variability in Human-Humanoid Imitation Activities

In this paper, we present preliminary results for the analysis of movement variability in human-humanoid imitation activities. We applied the state space reconstruction's theorem which help us to have better understanding of the movement variability than other techniques in time or frequency domains. In our experiments, we tested our hypothesis where participants, even performing the same arm movement, presented slight differences in the way they moved. With this in mind, we asked eighteen participants to copy NAO's arm movements while we collected data from inertial sensors attached to the participants' wrists and estimated the head pose using the OpenFace framework. With the proposed metric, we found that sixteen out of eighteen participants imitate the robot well by moving their arms symmetrically and by keeping their heads static; two participants however moved their head in a synchronous way even when the robot's head was completely static and two different participants moved their arms asymetrically to the robot. Although the work is in its early stage, we believe that such preliminary results are promising for applications in rehabilitation, sport science, entertainment or education.