2024-03-29T09:30:36Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1546332021-08-30T18:23:06Zoai_pmh:all154633
https://hdl.handle.net/2286/R.I.38695
http://rightsstatements.org/vocab/InC/1.0/
All Rights Reserved
2016
vi, 37 pages : illustrations (chiefly color)
Masters Thesis
Academic theses
Text
eng
Iyengar, Varsha
Xin Wei, Sha
Turaga, Pavan
Coleman, Grisha
Arizona State University
Partial requirement for: M.S., Arizona State University, 2016
Includes bibliographical references (pages 35-37)
Field of study: Computer science
This thesis aims to explore the language of different bodies in the field of dance by analyzing<br/><br/>the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,<br/><br/>the term habitual patterns is defined as the postures or poses that tend to re-appear,<br/><br/>often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing<br/><br/>the movement vocabulary of a dancer to reveal his/her unique fingerprint.<br/><br/>The proposed approach for uncovering these movement patterns is to use a clustering<br/><br/>technique; mainly k-means. In addition to a static method of analysis, this paper uses<br/><br/>an online method of clustering using a streaming variant of k-means that integrates into<br/><br/>the flow of components that can be used in a real-time interactive dance performance. The<br/><br/>computational system is trained by the dancer to discover identifying patterns and therefore<br/><br/>it enables a feedback loop resulting in a rich exchange between dancer and machine. This<br/><br/>can help break a dancer’s tendency to create similar postures, explore larger kinespheric<br/><br/>space and invent movement beyond their current capabilities.<br/><br/>This paper describes a project that distinguishes itself in that it uses a custom database<br/><br/>that is curated for the purpose of highlighting the similarities and differences between various<br/><br/>movement forms. It puts particular emphasis on the process of choosing source movement<br/><br/>qualitatively, before the technological capture process begins.
Computer Science
Dance
Clustering
Machine Learning
Motion Capture
Movement
repository
vernacular
Movement (Acting)
cluster analysis
Dance--Data processing.
Dance
Analysis of habitual patterns in vernacular movement