Nobody boogies quite like you – Naked Security


That spasmodic jerking around that some of us refer to as “dancing?”

It’s the latest biometric: we can be identified by our twerking, our salsa, our rumba or our House moves with an impressive 94% accuracy rate, according to scientists at Finland’s University of Jyväskylä.

To be specific, the researchers asked 73 volunteers to dance to eight music styles: Blues, Country, Dance/Electronica, Jazz, Metal, Pop, Reggae and Rap. The dancers weren’t taught any steps; rather, they were simply told to “move any way that felt natural.”

Their study, described in a paper titled Dance to your own drum, was published in the Journal of New Music Research last week.

Identifying people by their dance moves is not what the researchers were after. They had set out to determine how music styles affect how we move:

Surely one does not move the same way in response to a song by Rage Against the Machine as to one by Bob Dylan – and research has indeed shown that audio features extracted from the acoustic signal of music influence the quality of dancers’ movements.

The original question: could they determine the style of music just by watching how people are dancing? Previous research has indicated that you can: low-frequency sound generated by kick drum and bass guitar relates to how fast you bop your head around, while high-frequency sound and beat clarity have been associated with a wider variety of movement features, including hand distance, hand speed, shoulder wiggle and hip wiggle. Dancers also increase their movements as a bass drum gets louder. Jazz is associated with lesser head speed.

It could all have to do with music’s audio features, but then again, cultural norms tell us how we’re supposed to move. Jazz? Let’s swing dance! Metal? HEADBANG!

In short, testing the idea that different music will elicit different movement patterns from listeners is complicated.

There’s already a fairly large body of work using machine learning to differentiate between musical genres. Work has also been done regarding how humans identify individuals based on their distinctive bodily movements.

Building on that previous work, University of Jyväskylä researchers set out to similarly use machine learning to explore the degree to which genre can be distinguished from volunteer dancers’ bodily movements.