January 29, 2020
In cities such as Beijing or London, it’s hard to go anywhere without being caught on security cameras—and a new study has established that, if you boogie, tango, shimmy, or tap, you actually will be easier to identify than people who simply walk. In fact, researchers at the University of
in Finland have found that every single person has his or her own unique way of dancing— and computers are able to ascertain the identity of dancers with startling accuracy.
According to a report by Study Finds, regardless of the type of music, from jazz to reggae, the vast majority of people maintain a uniform uniqueness to their dancing style. (Think of Elaine Benis on Seinfeld.) It’s this ever-present personality in each of our dance moves that makes it easy for computers to ID dancers.
Over the past few years, the study’s authors have been using motion capture technology to analyze people’s dance moves, and to infer what they can tell us about the individual. And that’s a whole lot of information—including whether he or she is extroverted, neurotic, happy or moody, and even how this person empathizes with others.
Humorously enough, the research team hadn’t initially set out to use computers to identify dancers. The original plan was to use machine learning to determine the musical genre participants were dancing to at a particular moment.
“We actually weren’t looking for this result, as we set out to study something completely different,” explains first study author Dr. Emily Carlson in a press release. “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.”
In total, 73 dancers took part in the experiment. Each participant was motion captured as they danced to eight different genres: rap, reggae, blues, country, electronic dance, jazz, and heavy metal. They were told to dance in whatever way 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,” said Professor Petri Toiviainen, the senior author of the study.
Rather surprisingly, the machine learning algorithm actually wasn’t very good at identifying the musical genres, only offering a correct guess about 30% of the time. However, the computer was much better at identifying the dancers based on their movements. Among the 73 participants, the computer accurately determined who was dancing 94% of the time.
“It seems as though a person’s dance movements are a kind of fingerprint,” says Dr. Pasi Saari, another study co-author and data analyst. “Each person has a unique movement signature that stays the same no matter what kind of music is playing.”
“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,” Dr. Carson concludes, “and this study is no exception.”
The study has been published in The Journal of New Music Research.
Research contact: @StudyFinds