Ballroom Dance Movement Recognition Using a Smart Watch

TL;DR

This paper explores using a single smart watch with deep learning to accurately recognize ballroom dance movements, achieving over 92% accuracy by modeling dance sequences as Markov chains.

Contribution

It introduces a novel approach combining deep learning and Markov chain modeling for whole body movement recognition with a single IMU sensor.

Findings

Deep learning outperforms traditional models in movement classification.

Modeling dance as a Markov chain improves accuracy from 85.95% to 92.31%.

Single smart watch sensors can effectively recognize complex dance movements.

Abstract

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.