Ballroom dance step type recognition by random forest using video and wearable sensor

Abstract

The paper presents a hybrid ballroom dance step type recognition method using video and wearable sensors. Learning ballroom dance is very difficult for less experienced dancers as it has many complex types of steps. Therefore, our purpose is to recognize the various step types to support step learning. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the dancers’ images overlap each other. To solve the problem, we propose a hybrid step recognition method combining video and wearable sensors for enhancing its accuracy and robustness. We collect seven dancers’ video and wearable sensors data including acceleration, angular velocity, and body parts location change. After that, we pre-process them and extract some feature values to recognize the step types. By adopting Random Forest for recognition, we confirmed that our approach achieved f1-score 0.760 for 13 step types recognition. Finally, we will open our dataset of ballroom dance to HASCA community for further research opportunities.