Skip to Main content Skip to Navigation
Conference papers

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

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-02877670
Contributor : Hal Ifip <>
Submitted on : Monday, June 22, 2020 - 3:52:07 PM
Last modification on : Monday, June 22, 2020 - 5:05:43 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Saeka Furuichi, Kazuki Abe, Satoshi Nakamura. The Possibility of Personality Extraction Using Skeletal Information in Hip-Hop Dance by Human or Machine. 17th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2019, Paphos, Cyprus. pp.511-519, ⟨10.1007/978-3-030-29390-1_28⟩. ⟨hal-02877670⟩

Share

Metrics

Record views

28