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Inertial Measurement Unit (IMU) sensors are being increasingly used to detecthuman gestures and movements. Using a single IMU sensor, whole body movementrecognition remains a hard problem because movements may not be adequatelycaptured by the sensor. In this paper, we present a whole body movementdetection study using a single smart watch in the context of ballroom dancing.Deep learning representations are used to classify well-defined sequences ofmovements, called \emph{figures}. Those representations are found to outperformensembles of random forests and hidden Markov models. The classificationaccuracy of 85.95\% was improved to 92.31\% by modeling a dance as afirst-order Markov chain of figures and correcting estimates of the immediatelypreceding figure.

Ballroom Dance Movement Recognition Using a Smart Watch