The way you dance is unique, and computers can tell it's you

 

The way you dance is unique, and computers can tell it's you

 

Almost everyone responds to music with movement, and a recent study has shown that our own individual dance style is almost always the same – regardless of type of music – and a computer can identify the dancer with astounding accuracy.

Researchers study how people move to music as a way to understand how and why music affects us the way it does. Over the last few years, researchers at the Centre for Interdisciplinary Music Research at the University of Jyväskylä in Finland have been using motion capture technology and have discovered that your dance moves say a lot about you. This can include how extroverted or neurotic you are, what mood you are in, and how much you empathise with other people.

Recently, however, they discovered something new. "We actually weren't looking for this result, as we set out to study something completely different," explains Dr. Emily Carlson, the first author of the study. "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."

73 participants were motion captured dancing to eight different genres: blues, country, dance/electronica, jazz, metal, pop, reggae, and rap. Their only instruction from the researchers was to listen to the music and move 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," says Professor Petri Toiviainen, the senior author of the study.

The participants’ movements were analysed using machine learning to try to distinguish between the musical genres. However, the algorithm was only able to determine the correct genre less than 30% of the time. What the researchers did discover was that the computer could correctly identify which of the 73 individuals was dancing 94% of the time. If left to chance, the expected accuracy would be less than 2%.

"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."

It was found that some genres had more effect on individual dance moves than others. For this reason, the computer was less accurate in identifying individuals when they were dancing to metal music. "There is a strong cultural association between Metal and certain types of movement, like headbanging," Emily Carlson explains. "It's probable that Metal caused more dancers to move in similar ways, making it harder to tell them apart."

Does this mean that face-recognition software will soon be joined by 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," she concludes, "and this study is no exception."

 

 

https://www.sciencedaily.com/releases/2020/01/200117104740.htm

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