Computer Algorithm Can Identify Unique Dancing Characteristics
Researchers at the Centre for Interdisciplinary Music Research at the University of Jyväskylä in Finland have been using motion capture technology to study people and dancing over the last few years. It is being used as a way to better understand the connection between music and individuals. They have been able to learn things through dance such as how extroverted or neurotic an individual is, their mood, and how much that individual empathizes with other people.
By continuing this work, they have run into a surprising new discovery.
According to Dr. Emily Carlson, the first author of the study, “We actually weren’t looking for this result, as we set out to study something completely different.”
“Our original idea was to see if we could use machine learning to identify which genre of music our participants were dancing to, based on their movements.”
There were 73 participants in the study. As they danced to the eight different genres of Blues, Country, Dance/Electronica, Jazz, Metal, Pop, Reggae and Rap, they were motion captured. They were told to listen to the music and then move their bodies in any way that felt natural.
“We think it’s important to study phenomena as they occur in the real world, which is why we employ a naturalistic research paradigm,” according to Professor Petri Toivianinen, the senior author of the study.
Participants’ movements were analyzed by the researchers using machine learning, which attempted to distinguish between the different musical genres. The process didn’t go as planned, and the computer algorithm was only able to identify the correct genre less than 30% of the time.
Even though the process didn’t go as planned, the researchers did discover that the computer was able to correctly identify the individual from the group of 73, based on their movements. The accuracy rate was 94%, compared to the 2% accuracy rate if it was left to chance, or the computer guessed without any given information.
“It seems as though a person’s dance movements are a kind of fingerprint,” says Dr. Pasi Saari, co-author of the study and data analyst. “Each person has a unique movement signature that stays the same no matter what kind of music is playing.”
There was an increased effect on individual dance movements depending on the genre of music that was played. When individuals danced to Metal music, the computer was less accurate in identifying who it was.
“There is a strong cultural association between Metal and certain types of movement, like headbanging,” Emily Carlson says. “It’s probable that Metal caused more dancers to move in similar ways, making it harder to tell them apart.”
These new developments could lead to something such as dance-recognition software.
“We’re less interested in applications like surveillance than in what these results tell us about human musicality,” Carlson explains. “We have a lot of new questions to ask, like whether our movement signatures stay the same across our lifespan, whether we can detect differences between cultures based on these movement signatures, and how well humans are able to recognize individuals from their dance movements compared to computers. Most research raises more questions than answers and this study is no exception.”