  Inertial Measurement Unit (IMU) sensors are being increasingly used to detect
human gestures and movements. Using a single IMU sensor, whole body movement
recognition remains a hard problem because movements may not be adequately
captured by the sensor. 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 and correcting estimates of the immediately
preceding figure.
