Ballroom Dance Movement Recognition Using a Smart Watch
This work addresses a specific problem for dancers or fitness enthusiasts by improving movement detection accuracy, but it is incremental as it builds on existing deep learning and Markov chain methods.
The paper tackled whole-body movement recognition in ballroom dancing using a single smart watch, achieving a classification accuracy of 92.31% by modeling dances as a Markov chain of figures.
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.