“Challenges in measuring partner dancing skills via wearable accelerometers” by Lorenzo A Rossi, Shengrong Yin, and Omprakash Gnawali. In Proceedings of the 6th ACM Workshop on Wearable Systems and Applications (WearSys 2020), June 2020.
Social partner dancing is a fun but challenging activity requiring different motion related skills. Common criteria used by professionals to assess the quality of this type of dancing fall in the categories of timing, technique and teamwork (often referred to as "the 3 Ts") and variety of motion (i.e. "moves"). We focus on the teamwork and variety skills for practitioners of a type of Swing dancing called Balboa. Our dataset consists of the wearable accelerometer data collected from the participants to 3 different Balboa social dance contests. Panels of professional dancers judged the contests. Later, some of those professional dancers evaluated the skills of each participant by watching video recordings of the contests. We propose four novel measures for teamwork and motion variety and we evaluate them versus the expert assessments and also activity based labels. Our preliminary results show that the measures can be useful for activity recognition and somehow useful for teamwork assessment.
BibTeX entry:
@inproceedings{dancing-wearsys20,
author = {Lorenzo A Rossi and Shengrong Yin and Omprakash Gnawali},
title = {Challenges in measuring partner dancing skills via wearable
accelerometers},
booktitle = {Proceedings of the 6th ACM Workshop on Wearable Systems
and Applications (WearSys 2020)},
month = jun,
year = {2020}
}