Analysis of juggling data: An application of $k$-mean alignment

Abstract

We analyze the juggling data by means of the $k$-mean alignment algorithm using cycles as the experimental units of the analysis. Allowing for affine warping, we detect two clusters distinguishing between mainly-planar trajectories and trajectories tilted toward the body of the juggler in the lower part of the cycle. In particular we detect an anomalous presence of tilted trajectories among the trial third cycles. We also find warping functions to be clustered according to trials suggesting that each trial is performed at a different pace and thus associated to a different typical cycle-duration.