The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine

  • Saeka FuruichiEmail author
  • Kazuki Abe
  • Satoshi Nakamura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11749)


The same dance can give different impressions depending on the way the dancers convey their own emotions and personality through their interpretation of the dance. Beginner dancers who are teaching themselves often search for dance videos online that match their own personality in order to practice and mimic them, but it is not easy to find a dance that suits their own personality and skill level. In this work, we examined hip-hop dance to determine whether it is possible to identify one’s own dance from skeleton information acquired by Kinect and whether it is possible to mechanically extract information representing the individuality of dance. Experimental results showed that rich experienced dancers could distinguish their own dances by only skeleton information, and it was also possible to distinguish from averaged skeletal information. Furthermore, we generated features from the skeletal information of dance and clarified that individual dance can be distinguished accurately by machine learning.


Dance Personality Kinect Skeleton Random forest 



This work was supported in part by JST ACCEL Grant Number JPMJAC1602, Japan.

Supplementary material

Supplementary material 1 (MP4 47131 kb)


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Meiji UniversityNakano-kuJapan

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