In this paper, we present a comparison between four HMM-based real-time decoding algorithms for stylistic gait recognition and following. The approach is based on a probabilistic modelling of walking gestures recorded through motion capture. The algorithms are evaluated on their ability to recover the progression of the performed gestures over time in real-time, i.e. as the gestures are performed, and their robustness when the decoding is only performed on a subset of the model dimensions. The performance of studied algorithms are also evaluated in the context of a framework for "gait reconstruction", i.e. where the walking gestures recognised on lower body dimensions are used to synchronously regenerate the upper body dimensions (and vice-versa).
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