Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning

Varun Badrinath Krishna

Keywords: ballroom, sequence, deep, learning, machine, markov, prior

Abstract: Smart watches are being increasingly used to detect human gestures and movements. Using a single smart watch, whole body movement recognition remains a hard problem because movements may not be adequately captured by the sensors in the watch. In this paper, we present a whole body movement detection study using a single smart watch in the context of ballroom dancing. Deep learning representations are used to classify well-defined sequences of movements, called \emph{figures}. Those representations are found to outperform ensembles of random forests and hidden Markov models. The classification accuracy of 85.95\% was improved to 92.31\% by modeling a dance as a first-order Markov chain of figures.

One-sentence Summary: Deep learning combined with Markov priors are used

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